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2019 Annual Report
Published on NCAR Annual Report (https://nar.ucar.edu)

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2019 RAL Annual Report

Table of Contents

  • 2019 RAL Annual Report
    • Director's Message
    • Next Generation Air Transportation
      • InFlight, Ground and Engine Icing
      • Prediction of Convective Storm Hazards for Aviation
      • Turbulence
      • Integration of Weather Information into Air Traffic Management
      • Dissemination of Aviation Weather Information
      • Oceanic Weather
      • Weather Impacts on Emerging Modes of Aerial Transportation
      • Ceiling and Visibility Products for Alaska
    • New and Emerging Applications
      • Surface Transportation Weather
      • Renewable Energy
      • Weather Prediction Machine Learning Optimization
      • Statistical Methods in Forecasting
      • Wildland Fire Modeling and Prediction
      • Model Development using Machine Learning
    • National Security Applications
      • Numerical Weather Prediction and Data Assimilation
        • Four-Dimensional Weather System (4DWX)
        • Real-Time Four-Dimensional Data Assimilation (RTFDDA) and Forecasting Advances
        • Fine-Scale Precision NWP: WRF-RTFDDA-LES
        • GPU-Accelerated Microscale Modeling: FastEddy
        • Mesoscale Ensemble Data Assimilation and Prediction
      • Tropical Cyclones and Related Extreme Weather
      • Post-Processing
        • Analog-Based Methods
        • Quantile Regression
      • Air Quality Forecasting
      • Statistical and Dynamical Mesoscale Climate Downscaling
        • Fine-Scale Seasonal Climate Prediction
        • Global Climatological Analysis Toolkit
      • Atmospheric Transport and Dispersion of Hazardous Materials Research and Development
        • Hazardous Material Source Term Estimation
        • Climatological Dispersion Patterns with Self-Organizing Maps
      • Disease-Spread Modeling
        • Monkeypox Study
    • Numerical Systems Testing and Evaluation
      • Regional Modeling Systems
      • Advanced Verification Techniques and Tools
      • Global Modeling
      • Tropical Cyclone
    • Hydrometeorological Applications
      • Water System Program
      • Short-Term Explicit Prediction
      • WRF-Hydro and the National Water Model
      • Water Resources Applications
      • Winter Weather
      • Land Atmosphere Interactions
      • Climate and Managed Water Systems
      • Hydrometeorological Observations
    • Geographic Information Systems (GIS) Program

Director's Message

Welcome to the Research Applications Laboratory's Annual Report for FY2019. RAL strives to be a world leader in performing collaborative end-to-end scientific research, development, and technology transfer, expanding the reach of atmospheric and related sciences and bringing them to bear in addressing important societal problems. Achieving this vision requires the willingness and ability to work in an interdisciplinary way with internal and external colleagues, collaborators and stakeholders in the public and private sectors.

William Mahoney, RAL Director
William Mahoney, RAL Director

NCAR’s new strategic plan focuses on NCAR’s unique national and international role of performing fundamental science through technology transfer to address critical scientific challenges facing society. RAL plays a key role in ensuring that NCAR’s science is actionable science – science that informs decision making to save lives, property, and contributes to a strong economy.

RAL is an organization with annual expenditures of approximately $34M (FY19) and a transdisciplinary staff composed of approximately 180 scientists, software engineers, mathematicians, geographers, physicists, managers/administrators, and numerous personnel bringing expertise across many other disciplines. RAL is substantially a “soft-funded" laboratory. RAL’s NSF base funding represented 8% of RAL’s total budget and NSF special funds, and one-time NCAR reinvestments added another 4%. The NSF funds are highly leveraged with external sponsor funding used for advancing our scientific research, development, and societal impact mission.

RAL continues to make substantial contributions to the atmospheric science research community and the capabilities we develop support the global weather, water, and climate enterprise. Research areas that gained momentum in 2019 include the development and application of machine learning methods, meso- to microscale numerical weather prediction, large eddy simulation (LES) acceleration using graphical processing units (GPUs), wildland fire behavior prediction, air quality prediction, and the application of LES models to support unmanned aerial systems (UAS) and wind energy prediction.

I hope you will enjoy this year’s Report as it describes many of our exciting scientific accomplishments over the past year and plans for the future.

 

Next Generation Air Transportation

Play a leadership role within the atmospheric research community to provide the necessary scientific underpinning and technology to support the weather and climate–related needs of the Next Generation Air Transportation System (NextGen).

  • InFlight, Ground and Engine Icing
  • Prediction of Convective Storm Hazards for Aviation
  • Turbulence
  • Integration of Weather Information into Air Traffic Management
  • Dissemination of Aviation Weather Information
  • Oceanic Weather
  • Weather Impacts on Emerging Modes of Aerial Transportation
  • Ceiling and Visibility Products for Alaska


InFlight, Ground and Engine Icing

BACKGROUND

For the past two decades RAL scientists have worked to improve diagnoses and forecasts of icing conditions that impact aviation. The research areas include icing aloft, jet or turbine engine icing, and ground icing. Much of this work is accomplished as part of the FAA's Aviation Weather Research Program. 

Figure 1. Aircraft undergoing the deicing process.
Figure 1. Aircraft undergoing the deicing process.

One outcome from the icing aloft research performed by the In-Flight Icing (IFI) team is the development of operationally-available, automated in-flight icing forecasts over the CONUS and Alaska. At this time, the Current and Forecast Icing Products (CIP and FIP) developed at RAL are running at the National Weather Service's Aviation Weather Center (AWC) and are approved for unrestricted supplementary use. The outputs include expected icing severity, probability of encounter, and potential for supercooled large drop (SLD, those drops with diameters exceeding 50 microns) at 13-km resolution on flight levels from 1000 ft to 29 kft over the CONUS for 0-18 h.

The engine icing research falls under the FAA’s High Ice Water Content (HIWC) program. This program sponsors research to develop an algorithm called the Algorithm for Prediction of High Ice Water Content Areas (ALPHA) to diagnose atmospheric conditions conducive to engine icing events. Another aspect of the HIWC program is field experiments to characterize the atmosphere where engine icing events will occur. RAL scientists have participated in these experiments that have taken place in Darwin, Australia; Florida; Cayenne, French Guiana; and Florida, California, and Hawaii.

The FAA also funds icing research in the terminal area after a new rule was enacted regarding flight restrictions in known icing conditions. To improve the detection and forecasting of icing in the terminal area, the Terminal Area Icing Weather Information for NextGen (TAIWIN) program is conducting research focused on two key areas: improved ground detection of icing conditions (notably the detection of freezing drizzle) and improved performance and forecasting of freezing drizzle and freezing rain in numerical models. Improved ground detection of icing conditions has focused primarily on automated detection of freezing drizzle and ice pellets and improvements to feature-tracking algorithms for radar and satellite data are also being explored. The numerical modeling tasks funded under TAIWIN have focused on development of a time-lagged ensemble for better forecasts of icing conditions; improved aerosol initialization and fluxes in the models’ identification of shortcomings of model initialization of clouds and precipitation; testing of new data assimilation methods; and improved blending of observations and nowcasts with numerical forecasts of icing conditions.

Ground deicing research continued with a focus on improving the NCAR snow machine to better match the indoor holdover times versus the outdoor holdover times observed in nature. The machine has typically shown more conservative (shorter) holdover times as compared to outdoor times the FAA is interested in determining why that is and correcting for it. Upgrades to the machine were the major focus for improving machine performance and reliability during fluid testing and an entirely new top of the machine is being designed for testing in early 2020.

FY2019 ACCOMPLISHMENTS 

In 2019, the IFI and TAIWIN teams conducted a field experiment targeting SLD icing conditions. Based in the Chicago-area, the teams collaborated with the FAA and Canadian icing researchers to operate an instrumented aircraft which collected data in winter storms. The In-Cloud Icing and Large drop Experiment (ICICLE) also included surface-based sensors deployed around the region by the TAIWIN team. This unprecedented data set includes numerous events with freezing drizzle and freezing rain which will be used for enhancing our understanding of the processes which lead to icing and for improving icing weather tools.

RAL continued work on a number of icing aloft sub-projects for the FAA: 1) research on high-resolution NWP model with explicit microphysics and improved use of sensor data to develop drop size distribution (DSD) products for icing prediction and severity calculations with the intention of addressing FAA regulations to discriminate between freezing drizzle and freezing rain.  2) an icing product tailored for Alaska that is transitioning to operational use. 3) Improvements to icing diagnosis using NEXRAD dual-polarization data. 4) Engineers worked with NCEP to transfer operational icing products to its WCOSS supercomputing environment.

RAL staff working on the High Ice Water Content (HIWC) project continued to evaluate and refine methods for detecting and nowcasting areas of HIWC using data from a series of field experimentsThe evaluation process has resulted in an upgraded version of ALPHA (Algorithm for Prediction of High Ice Water Content Areas) which uses NWP output combined with satellite and radar data to diagnose cold cloud tops, warm atmosphere (compared to a standard sounding), high radar reflectivity below typical flight cruise altitudes, and other factors to determine regions conducive to the high ice water content hazard. Under a joint effort with the Australian Bureau of Meteorology to implement ALPHA in their operational setting and distribute products to airlines, the HIWC team conducted a preliminary trial of the product in an operational setting.

The TAIWIN project made significant progress in both the modeling and observational areas. The automated algorithm for ground detection of freezing drizzle was modified to include detection of ice pellets and frost utilizing the existing Automated Surface Observing System (ASOS) infrastructure. The algorithm has gone through several iterations now, and the latest results indicate that the algorithm is likely working as expected and can be used to reprocess archived data for determining periods where freezing drizzle, ice pellets and frost may have been occurring. A manuscript describing this algorithm was submitted to the Journal of Atmospheric and Oceanic Technology and is currently under review.

TAIWIN staff also conducted a review of the U.S. present weather reporting capabilities with a focus on the impacts that ASOS has had since its inception in the early 2000s. The study highlighted that reports of freezing drizzle, drizzle and ice pellets were all negatively impacted by ASOS, while reports of snow, rain and freezing rain had been improved. A manuscript has been accepted for publication by the Journal of Applied Meteorology and Climatology and is expected to be in final print by years end.

Radar and satellite tasking focused on automated tracking of possible derived Supercooled Large Drop (SLD) conditions from both radar and satellite data with a goal of providing a near-term nowcast of icing conditions. Considerable effort also went into the model improvement tasks with most of the work focusing on the Time-Lagged Ensembles (TLEs), cloud underproduction, and aerosol tasks. A new TLE of the HRRR model was developed and is currently being run in real-time within RAL. The modeling group is performing an initial verification of the TLE to determine if adjustments need to be made to the weighting of the different model runs. Significant progress was also made on the cloud underproduction task with efforts focused on development of a new cloud-fraction scheme for the WRF model to better forecast cloud development. A journal article summarizing these results was published in late spring.

PLANS FOR 2020

The In-flight Icing Project will continue its research using state-of-the-art NWP model ouput and observations to develop icing products that provide information about cloud drop size to the aviation community. The data set obtained during ICICLE will support this development and verify products.

The HIWC team will complete evaluation of the diagnostic capabilities of ALPHA and continue research on HIWC forecasting techniques.  A formal HIWC Nowcasting Trial Exercise will be conducted in Australia jointly with the Bureau of Meteorology during the northern Australia monsoon season in early 2020. This trial exercise will provide insight to the FAA sponsors on the feasibility of fielding an operational version of ALPHA in the United States.

TAIWIN will continue to focus its efforts on the aforementioned tasks, with the goal of publishing the results of the freezing drizzle algorithm work. Data analysis from ICICLE will be the primary focus of TAIWIN tasking in the upcoming year. This data will be used to study microphysical cloud properties in order to better understand how icing conditions (particularly freezing rain, freezing drizzle and ice pellets) form and evolve over time.

The snow machine work is expected to continue with a focus on conducting more outdoor versus indoor testing, upgrading the NCAR snow machine, and testing the next design in the cold room by simulating the outdoor tests. If successful, this will clear the way for laboratory testing of aircraft anti-icing fluids in a laboratory environment.

Prediction of Convective Storm Hazards for Aviation

Background

The Next Generation Air Transportation System (NextGen) is a national priority designed to meet the air transportation needs of the United States in the 21st century—in particular, a significant growth in demand for air traffic services, possibly on the order of two to three times today's demand levels. Meanwhile, the number of commercial applications of Unpiloted Aerial Systems (UAS) has been growing rapidly with primary operating space being the lowest 400 feet of the atmosphere. The expected increase in congestion of the NAS requires improved detection and prediction of weather hazards and their translation into air traffic flow impacts in order to maintain aviation safety and improve the efficiency of Air Traffic Flow Management.

For the past several years, the Aviation Applications Program within NCAR’s Research Applications Laboratory (RAL) has been engaged in multiple FAA-funded R&D efforts aimed at improving convective weather prediction, including in areas such as lightning impacts on airport operations and the subsequent ripple-effects throughout the National Airspace System (NAS), developing state-of-the-art CONUS-scale short term predictions of convective storms for tactical-to-strategic time scale planning of the NAS, and optimization of global-scale probabilistic predictions of convective storms in support of the ICAO-led harmonization of global weather hazard products.

FY2019 Accomplishments

Convective Weather Forecast System Bridging Tactical-to-Strategic Planning Time Horizons.

Figure 1. Image examples from NWP demonstrating the performance of the (left) 2 hour forecast of precipitation when compared with (right) corresponding observed precipitation intensity valid as obtained from the new NWP display.
Figure 1. Image examples from NWP demonstrating the performance of the (left) 2 hour forecast of precipitation when compared with (right) corresponding observed precipitation intensity valid as obtained from the new NWP display.

CoSPA is a forecast system that produces 0-8 hour forecasts of convective storm intensity and convective cloud top heights by blending extrapolation and model based forecasts using image processing techniques and forecast heuristics.  CoSPA, under FAA sponsorship, was developed collaboratively by MIT Lincoln Laboratory, NOAA Earth System Research Laboratory, and NCAR RAL to improve convective weather forecasts spanning the tactical to strategic time frames. The inputs to NCAR-developed blending include MIT-LL multi-scale advected VIL and Echo Tops and longer range model forecasts from the High Resolution Rapid Refresh (HRRR) Model. Currently the blending algorithms developed in RAL are undergoing final enhancements as part of the technology transfer to enable faster throughput while maintaining forecast performance and to meet specific FAA requirements for reliable forecast generation. The system latency continues to be improved compared to the legacy version of CoSPA (See Table 1), and features such as a “hot start” capability have been added to support an extremely rapid 15 minute failover capability without a fully redundant host. Additional features to support FAA integration and testing activities have also been added that allow for historical data processing.  Finally, portions of the technology were streamlined to meet modern computing system architecture.  

Table 1. Blending algorithm capabilities and characteristics. The NWP-Latest version is currently undergoing technology transfer to the FAA.
Table 1. Blending algorithm capabilities and characteristics. The NWP-Latest version is currently undergoing technology transfer to the FAA.

The latest version of the blending system (V5.5.2.x) was delivered to the FAA in August 2019 and is being implemented as part of the NextGen Weather Processor (NWP). An example of the latest version’s performance is shown in Figure 1. We continue to support a legacy version of the blending (V4.0) used in CoSPA which will continue to be made available year-round to aviation planners (i.e., ARTCCs, the FAA Command Center, the Aviation Weather Center and airline industry partners) until NWP forecasts become available.

Lightning Impact on Airport Terminal Operations and Safety.

Lightning poses a safety risk to personnel working outdoors at airports.  At larger United States (US) airports major airline and airport stakeholders have established safety guidance rules and procedures to mitigate such risks.  These, however, result in outdoor work being halted which causes interruptions in servicing of the arriving and departing aircraft at airport gates.

In a collaborative effort with AvMet Applications, Inc. (hereafter simply AvMet), this project aimed to quantify air traffic impacts due to lightning-caused ramp closures in terms of delay cost and safety risk cost with the goal of identifying an optimal range of safety guidance rules. To investigate the tradeoff cost associated with both safety risks and traffic delays, NCAR and AvMet established a framework to compare safety & efficiency costs based on model simulations of air traffic flow. Traffic simulations used were from AvMet’s weather-aware superfast-time NAS / Air Traffic Management (ATM) simulation model called the “Dynamic Airspace Routing Tool (DART)” as it allowed us to directly identify delays associated with lightning induced ramp closures. Utilizing the developed framework, the goal was to conduct a wide range of traffic simulations that capture different types of convective scenarios, different traffic demands and different types of ramp closures based on different safety rules as well as lightning data to comprehensively quantify impacts in terms of delay cost and safety risk cost. This was done based on air traffic flow simulations for various realization scenarios for three months of the 2014 convective season 2014 for two of the core 30 airports, Atlanta (ATL) and Orlando (MCO).

Figure 2. Total traffic delay cost and total lightning safety cost based on different safety rules and lightning data from three different lightning sources  (colored icons).
Figure 2. Total traffic delay cost and total lightning safety cost based on different safety rules and lightning data from three different lightning sources  (colored icons).

Results of this study show that the cost of ground traffic delays caused by lightning related ramp closures are substantial. This also applies to costs associated with lightning risk if not mitigated. Results also show significant dependencies of risk and delay costs to air traffic demand at the time of impact and ramp closure durations. Typical lightning safety procedures that are employed at US airports often range from lightning safety procedures employing a critical radius of 3 miles and a 6 minute wait period after the last lightning strike to a 5 mile radius with a 15 minute wait period. In our study we found that a 3mile/6min safety rule more frequently has periods of unmitigated risk, at times these can be substantial. Conversely, a  5 mile/15 minute safety rule can be associated with substantial traffic delay costs. A 5 mile /10 minute rule represents a compromise – it decreases the delay cost in most cases at the expense of slight increases in the risk cost (Fig. 2). Our study also found that human factors resulting in delayed ramp closures or reopenings can cause significant safety and delay costs.

This project also focused on investigations concerned with the accuracy and potential usability of short-term lightning hazard predictions (instead of traditional lightning detection systems) based on a limited amount of existing methods. Investigating various lightning hazard predictions can identify opportunities for improved guidance and together with the efficiency/safety analysis can identify opportunities in minimizing avoidable impacts.

Results show that radar, satellite and ground based electric field mill based lightning predictors have some skill in identifying a lightning hazard. Their accuracy in predicting the onset and cessation of lightning varies. We found it most reliable for nowcasts based on radar data. Satellite and electric field data from surface electric field mills were not found to be as reliable in providing accurate lightning warnings when used on their own. Finally, a high temporal and spatial resolution is needed for lightning hazard predictions at airports to minimize unnecessary ramp closures due to latency.

Ensemble Prediction of Oceanic Convective Hazards (EPOCH).

Figure 3.  Example quality assessment selector panel (top left) and accompanying high-glance value plots, ROC curve (bottom left) and Reliability Diagram (bottom right).
Figure 3.  Example quality assessment selector panel (top left) and accompanying high-glance value plots, ROC curve (bottom left) and Reliability Diagram (bottom right).

Work on improving techniques to generate aviation weather hazard probabilistic forecasts continued this past year, with the overarching goal of achieving a practical, transparent approach that can be easily implemented for operational use.  We examined in more detail forecast quality and performance metrics, implementing an online routinely-updated evaluation capability.  Figure 3 shows an example ROC (Receiver Operating Characteristic) plot and reliability diagram along with the web-based selection user-interface.  Updated with each forecast run (as observations become available), these show high value at-a-glance quality metrics applicable to the previous 30 days.  Notable additions to the system this year include adjustments to handle ½ degree horizontal resolution data with a 3-hourly lead-time resolution.  Figure 4 provides an example of the forecast with increased resolution in comparison to a satellite-derived verification field called, Convective Diagnostic Oceanic (CDO).    

Figure 4. Comparison of 1 degree 6 hourly (left column) and ½ degree 3 hourly (middle column) EPOCH 24-hour forecasts with CDO (right column), for two regions. Upper row (highlighted in gold) for a central/western portion of Africa and lower for a region (highlighted in red) in the US central plains.
Figure 4. Comparison of 1 degree 6 hourly (left column) and ½ degree 3 hourly (middle column) EPOCH 24-hour forecasts with CDO (right column), for two regions. Upper row (highlighted in gold) for a central/western portion of Africa and lower for a region (highlighted in red) in the US central plains.

Finally, work began on transitioning the EPOCH system to NOAA (World Area Forecast Center [WAFC] - Washington), the anticipated host entity for operations.  Operational products from the WAFC will support the World Area Forecast System (WAFS) requirement for high-resolution probabilistic gridded forecasts of aviation weather hazards.  As part of this in an effort to help understand how future model upgrades could impact EPOCH, we also examined the differences between GFS v15, a precursor to the anticipated next version of the GFS ensemble, and the previous version (v14).  V14 is based on the underlying model core that is used in the current ensemble system that is utilized by EPOCH.  Our goal is use these results to achieve an EPOCH design that minimizes changes required due to model upgrades, in an effort to lower the overall cost of software maintenance.

FY2020 PLANS

RAL will continue to support the technology transfer of the latest version of the blending to the FAA for inclusion in the NextGEN Weather Processor (NWP) and work will continue on the EPOCH system development and technology transfer.  Enhancements for EPOCH will include updates to the calibration algorithm to support higher (northern) latitude coverage.  Other updates will seek to optimize probabilistic forecast skill and measures of economic value through examination of case studies selected from industry examples. These products are aimed at aiding aviation weather forecasters in their development of guidance products as well as aid airline dispatchers in their strategic planning for transoceanic flights.

Turbulence

BACKGROUND

Turbulence encounters by general and commercial aviation continue to pose significant safety and flight efficiency concerns. Almost anyone who has flown commercially has had an unpleasant experience with turbulence and has a tale to tell about it. According to some estimates, turbulence encounters account for well over 75% of all weather-related injuries on commercial aircraft and amount to at least $200M annually in costs related to passenger and crew injuries." Consequently, there is an urgent need to provide better turbulence information to pilots and route planners so that the number of encounters can be minimized, or adequate warnings provided so that passengers and crew can be better prepared for expected encounters.

For more than 25 years, a group of scientists and engineers at the National Center for Atmospheric Research’s Research Application Laboratory (NCAR/RAL) has led efforts to address these needs. Working under the sponsorship of the Federal Aviation Administration (FAA), the National Aeronautics and Space Administration (NASA), the National Oceanic and Atmospheric Administration (NOAA), and the Taiwan Civil Aeronautics Administration (CAA) and in collaboration with several universities and private companies, the team has conducted research aimed at improving fundamental understandings of the nature and causes of turbulence and developing new techniques for better observing and forecasting turbulence.  These technology improvements are typically transitioned to the Weather Service or private vendors for 24x7 dissemination, and this ultimately leads to safer travel for the flying public.

Efforts have been focused in four areas: (1) Development, implementation, and monitoring of new techniques for obtaining automated in situ reports of turbulence encounters from commercial aircraft; (2) development of an automated system for detecting in-cloud turbulence using Doppler weather radar data; (3) development and implementation of an automated turbulence nowcasting and forecasting system called Graphical Turbulence Guidance or GTG; and (4) high-resolution simulation studies of observed turbulence events to better characterize the nature and genesis of aircraft-scale atmospheric turbulence. The products developed at RAL have reached a level of maturity that allow them to be used operationally by pilots and route planners in tactical and strategic planning for avoiding turbulence or mitigating the effects of encounters. One important aspect of all NCAR products is that they provide an aircraft-independent measure of atmospheric turbulence known as the energy (or eddy) dissipation rate or EDR (m2⁄3/s).

AUTOMATED IN SITU EDR MEASUREMENTS

Despite the continued reporting of the frequency and severity of turbulence encounters, our understanding of the nature and genesis of aviation-scale atmospheric turbulence remains limited.  Research to better understand the nature and causes of free atmosphere aviation-scale turbulence has been hampered in part by a lack of routine, consistent and reliable observational data. Until recently, verbal pilot reports (PIREPs) have typically been the only source of information routinely available about the location and severity of turbulence. These reports are, unfortunately, incomplete (since reporting is voluntary), and highly subjective (what one pilot views as “moderate” might be perceived as “light” or “severe” by another) as well as aircraft dependent (different aircraft can experience the same atmospheric turbulence intensity differently). Further, recent investigations into the accuracy of PIREPs have indicated an average position error of about 50 km, or several grid points for current operational numerical weather prediction (NWP) model grid spacings. While NWP models are very useful in forecasting other atmospheric hazards, they are of limited value for turbulence given that turbulence exists for short periods of time and in small geographical areas. In order to improve the detection and forecasting of turbulence, it is clearly essential to upgrade the turbulence observation and reporting system and to create automated systems for obtaining more abundant, reliable data. In pursuit of this goal, NCAR is in the process of augmenting, and perhaps eventually replacing, the PIREPs with in situ observations from commercial aircraft. These observations and their dissemination are completely automated, and provide a measure of atmospheric turbulence intensity levels (EDR), instead of aircraft-specific estimates of turbulence severity. The in situEDR system developed by NCAR scientists and engineers consists of a simple software upgrade to the aircraft’s ACMS (Aircraft Condition and Monitoring System), or other suitable onboard computer system, and no hardware changes are required.

Figure 1. In situ EDR observations of turbulence automatically reported by UAL, DAL and SWA aircraft over a 5 months period in 2019.
Figure 1. In situ EDR observations of turbulence automatically reported by UAL, DAL and SWA aircraft over a 5 month period in 2019.

FY2019 Accomplishments

The in situ EDR software package continues to be implemented on Delta Air Lines (DAL) B737, B767, B777, A319, A320, A321, A330 (and very soon on B757), United Airlines B777 and B787, and Southwest Airlines (SWA) B737 aircraft in the United States, and a number of European and Asian carriers, mostly on B777s. An example of the coverage from the U.S. carriers is shown in Fig. 1. This algorithm is expected to be implemented on other aircraft worldwide in the coming years; the highest priority is implementation on international aircraft to enhance global coverage, and implementation on package carriers to enhance nighttime observations.

FY2020 Plans

Discussions will be conducted with IATA, Airbus, Teledyne, Boeing, GoGo, DAL, Lufthansa, SWISS, Air France, Aer Lingus, UPS, Korean Airlines, Qantas, KLM, and others to implement the in situ EDR algorithm on all or parts of their fleets. Some of these implementations will be on laptops/tablets in the cockpit for downlink by WIFI.

Also, some SWA aircraft are also equipped with new water vapor sensor measurements and are just beginning to be included on the turbulence reports. This will allow us to determine which reported events are in-cloud or out-of-cloud.  Finally, a feasibility study will be undertaken to investigate a new method to derive EDR from ADS-B (1-sec) data.

REMOTE SENSING MEASUREMENTS

In order to give pilots better information about potentially hazardous regions of turbulence in thunderstorms before they encounter them, RAL scientists developed the NEXRAD Turbulence Detection Algorithm (NTDA) which uses ground-based Doppler radar data from the NEXRAD WSR-88D network to remotely detect turbulence within clouds. The NEXRAD spectral width data is converted to EDR.  The algorithm runs on data from each radar in the network, processing each “tilt” or “sweep” independently to obtain estimates of EDR within cloud. The results are merged (or “mosaicked”) with measurements from other NEXRADs and mapped to chosen flight altitudes. The initial version of the NTDA was adopted by the National Weather Service and implemented on all of its radar systems in 2007 and 2008. Since then, a number of advancements have been made to the NTDA to increase its coverage, accuracy, speed and maintainability, and to accommodate NEXRAD changes like the adoption of dual-pol and the implementation of a new spectrum width estimator (also developed by RAL staff). The NTDA has been modified to run on radars in Taiwan, as well.

FY2019 Accomplishments

Work has focused on enhancing the EDR estimation of NTDA. This was achieved by implementing antenna scan rate adjustments to account for different antenna scan rates of different NEXRAD radar scans. The antenna scan rate impacts spectrum width estimation which in turn impacts EDR estimation. Furthermore, radio frequency interference (RFI) has been increasingly impacting NEXRAD measurements. We developed and implemented an RFI mitigation scheme to mitigate high erroneous EDR values in NTDA due to RFI contamination.  Furthermore, transfer of the NTDA system to the National Weather Service was continued but is currently on hold pending an FAA requirement for the National Weather Service to continue this work. Furthermore we continued investigating the relationship of turbulence inside thunderstorms and related turbulence intensity and volume to the occurrence of lightning. The correlation between these quantities can likely be used in conjunction with future geostationary satellite lightning mapping data to help diagnose likely regions of turbulence in regions not served by Doppler radar. 

FY2020 Plans

RAL scientists plan to investigate further enhancements of NTDA.  NTDA will continue to run as a real-time prototype over the CONUS, Alaska, Hawaii and Puerto Rico, providing data used for the development of turbulence nowcast products and scientific investigations of the development of convective storms. It will be adapted as needed to accommodate changes to the NEXRAD radars.  Implementation at NCEP is on hold but it is planned that NTDA be incorporated in the Graphical Turbulence Guidance Nowcast (GTGN) algorithm (see below) in 2 years time. In the meantime NCAR supports experimental GTGN and NTDA operations at NCAR as directed by the FAA.

NOWCASTING/FORECASTING TURBULENCE

Figure 2. Example GTG3 EDR output as it appears on NOAA’s ADDS website for a 0-hr forecast at flight level (FL) 370, i.e., about 37,000 ft, valid 1800 UTC 12 Nov 2018. PIREPs are overlaid for comparison.
Figure 2. Example GTG3 EDR output as it appears on NOAA’s ADDS website for a 0-hr forecast at flight level (FL) 370, i.e., about 37,000 ft, valid 1800 UTC 12 Nov 2018. PIREPs are overlaid for comparison.

RAL has been developing and testing aviation-scale turbulence forecast algorithms that provide forecasts out to 18 hours, updated hourly. The forecast system is known as the GTG (Graphical Turbulence Guidance product). It relies on the WRF RAP NWP model (http://rapidrefresh.noaa.gov/) output and provides what amounts to an ensemble weighted mean of various turbulence diagnostics output as EDR (m2/3 s-1) on designated flight levels. The output is available to interested users on NOAA’s Aviation Digital Data Service (ADDS) web site (http://www.aviationweather.gov/adds/).  An example is provided in Fig. 2.  Development of a global version of GTG has also been completed and an example output is provided in Fig. 3, based on NOAA’s global FV3 model.  The global version has also been implemented at the UK Meteorological Office based on their global Unified Model.

Figure 3. Example Global GTG EDR (colors) output a 12-hr forecast at flight level (FL) 320, i.e., about 32,000 ft, valid 0 UTC 21 Sep 2017.  PIREPs are overlaid for comparison.
Figure 3. Example Global GTG EDR (colors) output a 12-hr forecast at flight level (FL) 320, i.e., about 32,000 ft, valid 0 UTC 21 Sep 2017.  PIREPs are overlaid for comparison.

In addition to the GTG forecast system (updated hourly over the US, 6-hourly globally), RAL had developed a turbulence nowcast system, GTG-N, which provides rapid turbulence (every 15 min) updates and makes heavy use of the latest available turbulence observations from the in situ EDR estimates, PIREPs, NTDA, and other sources (e.g., METARs gust information) on a GTG background. This product is intended to provide enhanced pilot situational awareness, especially for turbulence associated with thunderstorms (convectively-induced turbulence or CIT). GTG-N will be running at NCAR later this year in an experimental setting and under FAA funding distributed to interested users via a licence agreement.

FY2019 Accomplishments

Work continued on the development of the global GTG using the GFS FV3, UKMet Office UM,  ECMWF and ARPEGE global NWP models.  Versions were and continue to be delivered to NCEP for integration into its Unified Post Processing System (UPP) using the GFS FV3 model and to the UKMet Office for use with its Unified Model (UM). An updated version of the global GTG using GFS FV3 is planned to be running operationally at NCEP next year. 

New low-level turbulence (LLT) prediction algorithms were developed to better represent turbulence in both the stable and unstable planetary boundary layers.  This is particularly important for Unmanned Aerial Systems (UAS) operations which typically fly in the atmospheric boundary layer (ABL). Furthermore work is underway to include convective induced turbulence (CIT) algorithms into GTG.

FY2020 Plans

Support for running GTG-N and disseminating it to users will continue. It is also planned to make GTG-N enhancements which involves including new data sources for CIT and LLT nowcast improvements.  Research on developing algorithms to better forecast convectively-induced turbulence (CIT) within the GTG forecast system will also continue.  These CIT forecast algorithms will become part of the next version of GTG, GTG4, which will use the 3-km HRRR model as input; GTG4 will incorporate the new LLT algorithms researched and developed in FY2017-8.

CHARACTERIZATION EFFORTS

Figure 4. Example (0130 UTC 4 June 20165) of turbulence (+) associated with gravity waves in the anvil of a Plains thunderstorm.
Figure 4. Example (0130 UTC 4 June 20165) of turbulence (+) associated with gravity waves in the anvil of a Plains thunderstorm.

Substantial effort has been invested in developing a better physical understanding of the mechanisms responsible for convectively induced turbulence (CIT) and clear-air turbulence (CAT) with the long-term goal of providing better operational turbulence forecasts. These studies make use of high-resolution nested (WRF) numerical simulations that have outer computational domains large enough to capture the relevant large-scale forcing processes and inner domains fine enough to capture the turbulence generating mechanisms. An example turbulence case related to gravity waves embedded in anvil cirrus from a plains thunderstorm is shown in the satellite image of Fig. 4.  In this case, there were many reports of turbulence in the vicinity of the anvil, and it was shown that some of these may be due to the observed gravity waves.

FY2019 Accomplishments

By careful examination of observations (PIREPs and in situ EDR reports) compared to satellite imagery, RAL scientists have isolated several cases in which gravity wave and banded structures in the anvil cirrus of convective storms seem to be highly correlated to regions of elevated turbulence.  The relation of the bands to the turbulence has been investigated for summertime storms revealing that the bands seem to have the character of planetary boundary layer rolls.  Other cases involving banded structures in wintertime storms were investigated as well. Such cases are extremely complex, with convection playing a major role in the production of turbulence, even when the turbulence occurs outside the storm boundaries.  Case studies such as these are ongoing.   Very high resolution simulations (~500m) have been performed for an upper-level clear-air turbulence case.

FY2020 Plans

Efforts to isolate cases and resolve turbulence sources will continue. This will lead to a better understanding of turbulence in the free atmosphere, which in turn should suggest improved forecasting strategies. Since this work is unique and original, we anticipate several publications to be forthcoming on these investigations. Investigations through very high resolution simulations (~500m)  continue for an upper-level clear-air turbulence case.

Integration of Weather Information into Air Traffic Management

BACKGROUND

Since weather conditions can seriously restrict aircraft operations and levels of service available to system users, the manner in which weather is observed, forecast, disseminated, and used in making air traffic management (ATM) decisions is of critical importance to the operation of the United States’ National Airspace System (NAS) and international airspaces, especially oceanic domains.  As the United States moves toward significantly increasing the capacity of the NAS through implementation of the Next Generation Air Transportation System (NextGen), integrating weather information (and associated uncertainty) into ATM decision-making processes is critical.  Moreover, harmonization around the globe with partners such as the Single European Sky ATM Research (SESAR) and the Collaborative Action for Renovation of Air Traffic Systems (CARATS) in Japan plays an important role as well.  NCAR/RAL contributes in various ways to these efforts by developing aviation weather hazard guidance products and means for their dissemination, collaboration with users of such products, including assistance with integration into decision support tools.  In addition, RAL participates in many outreach and education activities.

WEATHER INTEGRATION ACTIVITIES WITH FAA

Under sponsorship of the FAA’s Aviation Weather Research Program, RAL continues to develop weather hazard guidance products based on utilizing observations, diagnosing model output, and making use of data fusion and mining technologies.  Probabilistic prediction methodologies are developed that build on ensemble models and translate atmospheric conditions into aviation impacts.  These research and development efforts are discussed in the convective storms, turbulence, icing, oceanic weather and dissemination sections of the annual report.

WEATHER INTEGRATION ACTIVITIES WITH NOAA

Many of the weather hazard guidance products developed under sponsorship by the FAA Aviation Weather Research Program end up being implemented in NOAA’s operational environment for routine production and dissemination to the aviation industry.

WEATHER INTEGRATION ACTIVITIES WITH NASA

Currently RAL is supporting the NASA UTM—Unmanned Aerial System (UAS) Traffic Management system—development by researching how weather, and in particular turbulence, affects UAS performance.  RAL is also supporting NASA in its quest to further Urban Air Mobility (UAM) by understanding the weather sensitivity of these emerging aerial ride-sharing vehicles and whether current routine weather guidance (both observations and forecasts) may be adequate to support UAM operations.  Details of the RAL research and development efforts are discussed in the weather impacts on emerging modes of aerial transportation section of the annual report.

WEATHER INTEGRATION OUTREACH ACTIVITIES

RAL continues to participate in many outreach venues to further weather R&D, harmonization, and integration into ATM decision support tools.  Notable events this past year included the Friends and Partners in Aviation Weather (FPAW; https://fpaw.aero) meetings organized by RAL and hosted by the National Business Aviation Association (NBAA) at its Annual Convention & Exhibit and the National Transportation Safety Board (NTSB), respectively, and the UAS Weather Forum (https://ral.ucar.edu/events/uaswf) held at the Association for Unmanned Vehicle Systems International (AUVSI) XPONENTIAL show.  These events continue to serve as excellent venues to share and discuss latest developments with industry, government and research partners.  RAL staff is represented on several ICAO, FAA and industry advisory committees, and professional organizations such as AMS and AIAA.  RAL participated in several exhibits, including the annual Air Traffic Control Association (ATCA) and AUVSI XPONENTIAL shows.

HALABY FELLOWSHIP

Figure 1.  Convective weather hazard deviation analysis.
Figure 1.  Convective weather hazard deviation analysis.

The Najeeb E. Halaby Graduate Student Fellowship (https://ral.ucar.edu/opportunity/halaby-fellowship) was established by NCAR/RAL to shape the next generation of researchers in aviation weather, honoring the late Najeeb Elias Halaby, an eminent aviator and administrator, for his vision and more than five decades of extraordinary contributions to aviation.  The recipient of a Halaby Fellowship will spend three months in residence with NCAR’s Aviation Weather Research Program, which Mr. Halaby was instrumental in establishing in the 1980s, conducting research broadly aimed at improving the integration of weather into decision support tools for improved weather avoidance and air traffic management.

Arman Izadi, this year’s Halaby Fellow, focused his research on characterizing the benefits of having enhanced weather information in the cockpit of commercial airliners for avoiding en-route convective weather hazards, especially over vast oceanic airspace (Figure 1).  Arman will present his results at the June 2020 AIAA Aviation Forum in Reno, Nevada, showing how the use of satellite-based weather hazard information is yielding smoother weather hazard avoidance routing.

Dissemination of Aviation Weather Information

BACKGROUND

Development of the Next Generation Air Transportation System (NextGen), a national program designed to meet the expanding air transportation needs of the US in the 21st century, is well underway, with member agencies overseeing R&D and implementation acquisitions.  Defining the weather information needs of NextGen and providing common weather-related decision information to all stakeholders within the system is an important element of the overarching program. Since weather conditions can seriously restrict aircraft operations and levels of service available to system users, the manner by which weather is observed, forecast, disseminated, and used in decision–making is of critical importance.

RAL's activity in dissemination of aviation weather information is focused in two areas:

  1. Common Support Services – Weather (CSS-Wx), developing next generation technology and infrastructure for dissemination of weather data to US Government and other users; and
  2. Weather Technology in the Cockpit (WTIC), developing methods for making the best weather information available to pilots for decision-making in the cockpit.

COMMON SUPPORT SERVICES – WEATHER (CSS-WX) PROGRAM

Figure 1. Diagram of FAA NextGen Weather Architecture with CSS-Wx in a central role
Figure 1. Diagram of FAA NextGen Weather Architecture with CSS-Wx in a central role

RAL has been one of the key contributors in developing standards and technology for the FAA research and acquisition programs focused on weather in the NextGen.  This work, part of the FAA’s Common Support Services Weather (CSS-Wx) Program, is aimed at developing next generation technology and infrastructure for dissemination of weather data to FAA and other aviation users. It focuses on enabling ubiquitous access to aviation weather data anywhere an appropriate network connection is available.

CSS-Wx achieves its goal by using a service-oriented architecture (SOA) approach in which existing Internet technology is leveraged to build weather data delivery services that conform to international standards. CSS-Wx has defined a network of data servers based on the Open Geospatial Consortium (OGC) Web Feature Service (WFS), Web Coverage Service (WCS), and Web Map Service (WMS) standards, operating over the FAA’s System Wide Information Management (SWIM) messaging broker. The services provide weather data encoded with standards from the World Meteorological Organization (WMO), International Civil Aviation Organization (ICAO), and UCAR’s Unidata. Using these technologies, it is possible to build complex, dynamic weather systems in which data sources and clients can be developed and modified independently but remain compatible while optimizing data latency and network bandwidth. RAL's participation in this program is sponsored by the FAA CSS-Wx Program Office and work is conducted collaboratively with the FAA's William J. Hughes Technical Center, MIT/Lincoln Laboratories, and NOAA.

FY2019 Accomplishments

In FY2019, the FAA CSS-Wx program has been proceeding with acquisition of the system through a contract to a commercial vendor for implementation and operational deployment of the CSS-Wx system in the FAA National Airspace System. The contractor has been heavily engaged in developing and testing the software for CSS-Wx this year. Based on years of experience during the development of CSS-Wx program concepts and proof of concept prototypes, NCAR/RAL acts as a subject matter expert to the FAA advising the agency about technical issues related to the contractor’s implementation of the system.

NCAR/RAL has also been a key contributor to the coordination between NOAA and FAA subsystems of NextGen. Forecast model output generated at the National Centers for Environmental Prediction (NCEP) and weather observations from various existing subsystems are reformatted for dissemination through the CSS-Wx network and generation of supplementary forecast products in the NextGen Weather Processor (NWP). NCAR/RAL has been the leader in developing and maintaining software for bridging from Gridded Binary (GRIB2) datasets to netCDF and for converting Traditional Alphanumeric Code (TAC) en-route advisory streams into the ICAO Meteorological Information Exchange Model version 3 (IWXXM-3).

In addition, RAL continues to work with experts from US and international agencies to validate and refine rules of practice for operational use of the data service standards and weather data format standards. RAL hosts an official validator for IWXXM schemas for public access online.

FY2020 Plans

The focus for FY2020 is to continue supporting the FAA's acquisition process for CSS-Wx, including providing technical guidance to the FAA and the CSS-Wx commercial vendor. RAL expects to troubleshoot and propose solutions to technical issues in the operational system as they are uncovered.  RAL will maintain a limited-scale testing system to verify solutions and anticipate issues that may be faced during the operational implementation. RAL will also continue its work on data services and weather data formats in concert with the new standards from the OGC, ICAO, and WMO. The new versions of the standards will improve interoperability of weather systems, but will require additional evaluation and integration with legacy systems.

WEATHER TECHNOLOGY IN THE COCKPIT (WTIC) PROGRAM

One of the programs led by the FAA's Aviation Weather Office (AWO) is Weather Technology in the Cockpit (WTIC). RAL is engaged in an effort for WTIC to study the requirements and technologies that would enable pilots to gain the advantages inherent in the rapidly emerging world of mobile technologies, including both tablets and phones. In this project, referred to as MobileMet, RAL provided a comprehensive technology assessment of mobile devices for use in delivery of weather information to the cockpit. RAL conducted a broad survey of the needs and expectations of users in relation to mobile devices for aviation weather delivery, developed several prototype applications based on the user needs survey, evaluated pilot responses to various weather presentations, and crafted an initial set of Minimum Weather Service Recommendations (MWSR) for mobile device use in general aviation aircraft.

FY2019 Accomplishments

Radar Latency

Figure 2. NVN simulation for the Radar Latency pilot evaluation
Figure 2. NVN simulation for the Radar Latency pilot evaluation

RAL examined the potential benefits of presenting pilots with a radar depiction on a mobile device, forecasted to the current time. The goal is to reduce pilot error due to the latency involved in sending observed weather data in the cockpit. As part of this study, RAL researchers developed and validated a novel, “virtual volume,” approach to reducing radar processing time as well as forecasting cell growth and decay. The resulting nowcast was named NEXRAD Valid Now (NVN). NVN forecast lead times from 0-15 minutes were verified to assess their potential errors. RAL prepared software for a pilot evaluation to compare these errors with the errors in pilot judgement from latent radar information. The results of that evaluation may be combined with other studies conducted at the FAA, and incorporated into the Minimum Weather Service Recommendations report.

Tactical Turbulence

Figure 3. Determination of the turbulence notification and demonstration display
Figure 3. Determination of the turbulence notification and demonstration display

RAL performed pilot evaluations and simulator evaluations to assess the feasibility, identify capabilities, and prototype implementations for tactical turbulence notification in the cockpit.  From these evaluations RAL has developed a real-time notification system, FlightAlert, that projects aircraft positions forward in time and calculates a categorical severity for a turbulence along the aircraft's path.  The system is capable of creating XML output messages which are sent to each target aircraft via Aircraft Access to SWIM (AAtS) and present to the aircraft's pilots. This notification can provide advanced warning and avoidance capabilities to mitigate turbulence injuries. The project focused on the tactical use of the notification messages to mitigate turbulence injuries, rather than forecast use for route planning.

In FY2019, RAL refined the FlightAlert system to generalize the algorithm to work with any gridded categorical hazard type and, additionally, to run on all available commercial aircraft.  RAL also analyzed the timeliness of available input aircraft data and the accuracy of aircraft projections utilizing the real-time system.  The results from the analysis showed that the projected aircraft hazard box locations were sufficiently accurate to forecast turbulence and that the system as a whole was suitable to support direct aircraft notifications.

FY2020 Plans

RAL expects to participate in a Radar Latency pilot evaluation in FY2020, providing software and technical support to the FAA-designated executor of the evaluation, assisting with processing of the results, and communicating findings through a Minimum Weather Service Recommendations report.

Although the Tactical Turbulence project completed in FY2019, RAL is currently discussing with the WTIC office the potential for follow-on work to apply the same technique to other aviation hazards.

Oceanic Weather

BACKGROUND

Weather conditions can seriously restrict aircraft operations and levels of service available to system users.  Thus, the manner by which weather is observed, forecasted, disseminated, and used in decision-making is of critical importance.  Aviation users operating within oceanic and remote regions have limited access to high-resolution (temporal and spatial) weather products that depict the current and future locations of deep convection and turbulence.

To address these needs, RAL scientists and engineers are developing weather products to identify and characterize the oceanic/remote occurrence of deep convection.  The convection diagnosis systems detect deep convection using satellite-based methodologies, ground-based and geostationary lightning data and numerical model results using two products, the Cloud Top Height (CTH) and the Convection Diagnosis Oceanic (CDO). Since 2015,  these products have been created over a near-global domain and displayed in the flight decks of Lufthansa Airlines as part of the Global Weather Hazards project. In the summer of 2018, the FAA Weather Technology in the Cockpit (WTIC) began to display the CTH and CDO products in the flight deck of three domestic airlines as part of the Remote Oceanic Meteorology Information Operational (ROMIO) demonstration over  a hemispheric domain. Accomplishments and plans related to the ongoing research and development of oceanic convection weather products are discussed below.

Remote Oceanic Meteorology Information Operational (ROMIO) Demonstration

Figure 1. Information provided by a pilot participant on the use of and quality of the ROMIO CTH and CDO products. The upper left panel shows the onboard weather radar that displays the weather radar return of the clouds below the aircraft altitude. The upper right panel shows the cloud tops as viewed out the window. The bottom panel shows the ROMIO Viewer display of the CDO (green shapes) and CTH (gray shapes) products as the aircraft approached the area of concern. The text box at the bottom contains pilot feedback on ROMIO effectiveness.
Figure 1. Information provided by a pilot participant on the use of and quality of the ROMIO CTH and CDO products. The upper left panel shows the onboard weather radar that displays the weather radar return of the clouds below the aircraft altitude. The upper right panel shows the cloud tops as viewed out the window. The bottom panel shows the ROMIO Viewer display of the CDO (green shapes) and CTH (gray shapes) products as the aircraft approached the area of concern. The text box at the bottom contains pilot feedback on ROMIO effectiveness.

The FAA WTIC ROMIO demonstration is analyzing oceanic aviation inefficiencies in current or future NextGen operations caused by gaps in either the available meteorological information or in the technology utilized in the cockpit.  Using an operational demonstration to uplink convective weather products into the cockpit of domestic airlines, this effort helps to identify and analyze operational gaps. 

In the summer of FY2018, the WTIC ROMIO team began an operational demonstration in a phased implementation that started with Delta Air Lines. United Airlines and American Airlines entered the demonstration early in 2019. Following the ROMIO Operational Plan, written in an earlier effort, all aspects of the demonstration were carefully planned and include the availability and ingest of meteorological data sets, the creation of weather products and their dissemination to the aircraft. Training the flight crews on the capabilities and limitations of the products, understanding how pilot decision-making might be facilitated with the convective products and soliciting flight crew feedback was completed. A similar effort was completed for dispatchers at the Airline Operations Centers. Development of the ROMIO Viewer, with an example shown in Fig. 2, was completed by BCI and includes the ability for pilots, dispatchers and air traffic controllers to provide feedback after the end of a flight. Virginia Polytechnic Institute and State University is collecting and analyzing the feedback to understand how the products fill existing gaps in meteorological information or in the technology utilized in the cockpit and how the products may change decision making by all parties. As shown in Figure 2, 73% of pilot respondents believe that ROMIO is more effective at obtaining relevant, timely weather information than the current system and hardware. Collaborative partners include the FAA, NCAR, BCI, Delta Air Lines, United Airlines, American Airlines, Virginia Polytechnic Institute and State University, Panasonic, Gogo and several FAA groups. An abstract was submitted to the AMS Aviation, Range and Aerospace Meteorology Conference to describe the ROMIO program and the benefits analysis results.

Figure 2. Results of the Virginia Polytechnic Institute and State University summary of pilot feedback for one of the questions.
Figure 2. Results of the Virginia Polytechnic Institute and State University summary of pilot feedback for one of the questions.

Arman Izadi, a PhD graduate student at the Virginia Polytechnic Institute and State University, was selected as the 2019 Halaby Fellow and spent the summer of 2019 at NCAR RAL working on the ROMIO benefits analysis, analyzing aircraft deviations around weather events. An abstract was submitted to the upcoming AIAA Aviation Forum and Exposition.

See Dissemination of Aviation Weather Information for more information on the FAA WTIC program.

 

FY2019 Accomplishments

The ROMIO demonstration began in July 2018 with Delta Air Lines. United Airlines and American Airlines entered the demonstration in early 2019. The NCAR ROMIO satellite processing system created the CTH and CDO products in real-time over a domain that includes the GOES-East and GOES-West satellites. The system was upgraded this year to include the new GOES-17 satellite and its Geostationary Lightning Mapper (GLM) total lightning data. Due to problems with the GOES-17 Advanced Baseline Imager (ABI) cooling system, the computation of the CDO product was redesigned to account for the lessened ABI data quality. Storm polygons of the CDO and CTH products were provided to the Virginia Polytechnic Institute and State University for the benefits analysis. A Program Management Review meeting was held at NCAR on 18 September 2019. The satellite processing system had a hardware upgrade of additional memory and an upgrade of the operating system.

FY2020 Plans

During FY2020, the ROMIO operational demonstration will be conducted with a planned end date of 31 December 2019. Feedback from pilots, the Airlines Operations Centers, and the FAA Oceanic Control Centers will continue to be collected and benefits analyzed by the Virginia Polytechnic Institute and State University to ensure that project goals are met. The satellite processing system will be upgraded to include the new Himawari-8 satellite when the FAA WTIC program provides the data feed.

Plans are underway to extend ROMIO to the general aviation community in Alaska and to write a Project Plan for this effort. A second effort is planned that will write a Transition Plan to take the ROMIO CTH and CDO products to an operational status. During the Transition Plan effort, the satellite system will continue to be operated at NCAR and will be available to licensed users.

GLOBAL WEATHER HAZARDS PROJECT

 green is >2 and yellow is >3. The grey shapes are CTH contours beginning at >30 kft (FL300) with darker shapes indicating higher contours at increments of 5 kft to a maximum of >50kft (FL500). The area shown includes Inter-Tropical Convergence Zone (ITCZ) over the northern part of South America. Storm motion vectors are shown with red arrows. NWS Convective SIGMETs are shown with the tan polygons.
Figure 3. Lufthansa Airlines eRM display of the CDO and CTH polygons. Color shapes represent the CDO interest values as follows: green is >2 and yellow is >3. The grey shapes are CTH contours beginning at >30 kft (FL300) with darker shapes indicating higher contours at increments of 5 kft to a maximum of >50kft (FL500). The area shown includes Inter-Tropical Convergence Zone (ITCZ) over the northern part of South America. Storm motion vectors are shown with red arrows. NWS Convective SIGMETs are shown with the tan polygons.

Inflight display of products depicting convective hazards are needed by pilots of transoceanic aircraft to assist with strategic route planning during long flights of up to ~17 hr. Such displays enable pilots to see potential convective hazards along the entire flight route, beyond the range of the onboard radar, and to reference the products while planning for future avoidance maneuvers. These new products are supplemental to the onboard weather radar for operational or tactical decisions.  Using satellite-based algorithms augmented with global ground-based lightning data, geostationary lightning data from the GOES satellites and global numerical model results, two convective products, the CTH and the CDO, are providing real-time, operational guidance to Lufthansa Airlines pilots. The products are uplinked into the flight deck and subsequently displayed on an Electronic Flight Bag (EFB) developed by Lufthansa Integrated Dispatch Operation (LIDO) named the eRouteManual (eRM) (Fig. 3).

The Global Weather Hazard (GWH) project began in 2015 with a partnership between Lufthansa Airlines, Basic Commerce & Industries, Inc. (BCI), NCAR and the Weather Solutions Division of the Sutron Corporation. This project is a commercial effort that has expanded coverage to a global domain with latitude limits of -50S to 70N. Display of both the CTH and the CDO products are shown on the LIDO eRM’s of Lufthansa Airlines B747-8 aircraft fleet with about 50 aircraft currently receiving the two products. The CTH and CDO polygons are plotted over the navigational charts on the eRM and provide the pilot with situational awareness of convective hazards over the planned flight route.

FY2019 Accomplishments

The CTH/CDO oceanic convection diagnosis system was successfully run on servers at Meteostar during the fiscal year, providing convective hazard guidance to Lufthansa Airlines pilots. The products are commercially available through BCI.

A quality-controlled archive data set of the CTH and CDO products was created for a 1 year period for use by the Google Loons project, to test the feasibility of their using the products for their stratospheric balloon deployments and navigation.

FY2020 Plans

Further enhancements to the GWH system are planned to include the GOES-16 and GOES-17 Geostationary Lightning Mapper (GLM) total lightning data, refinement of the storm extrapolation methodology, increasing the update rate of CDO to 5 min and enhanced data quality methods. Another agreement will be negotiated for tasks such as including the GOES-17 ABI data and reconfiguring the CDO computation to account for the ABI data quality deficiencies, and investigating the use of GOES-R level 2 products to enhance the CDO product.

Weather Impacts on Emerging Modes of Aerial Transportation

BACKGROUND

Unmanned aerial systems (UAS) and urban air mobility (UAM) are emerging as new and innovative modes of air transportation. Small UAS (weighing less than 55 lbs.) are increasingly performing all manner of commercial operations, including delivery of medical supplies, inspection of pipelines and railway tracks, surveilling mining operations, crop monitoring, search and rescue, public safety and numerous emerging applications. UAM is gaining attention as a futuristic mode of aerial ride-sharing to cross metropolitan areas through the airspace, thus avoiding congestion on roads.

The sensitivity of aviation to weather hazards increases as the size of an aircraft decreases. Moreover, particular challenges loom for these emerging modes of aerial transportation in complex terrain and areas of strong surface characteristic changes (e.g., land/sea contrasts), near thunderstorms, and in urban environments. NCAR is helping the FAA, NASA and industry leaders to appreciate the weather challenges that UAS and UAM operations are facing, and we’re developing relevant micro-weather prediction capabilities to effectively guide such operations, ultimately geared towards safe integration of UAS and UAM aerial operations into the National Airspace System (NAS).

This summary describes activities related to evolving unmanned aerial transportation through an improved understanding of potential weather impacts and improvements in their prediction over what is currently possible with today’s operational weather prediction systems. The following sections report on the development of new systems and products to support small UAS operations, the translation of turbulence into impacts on small UAS, and work to provide weather products to support UAM development efforts.

MICRO-SCALE WEATHER PREDICTION FOR UNMANNED AERIAL SYSTEMS

A key component for integrating UAS into the NAS is to ensure and demonstrate their safe flight during hazardous weather conditions. To meet this goal, under NASA and NSF funding, NCAR is coupling the latest weather prediction capabilities and data assimilation techniques to generate predictions of winds, turbulence, and other aviation weather hazards (fog, icing) at scales relevant to support UAS operations that will ultimately feed into Unmanned Traffic Management (UTM) systems.  Coupled with the impacts-translation modeling discussed below, these forecasts will provide critical information for flight planning, as well as in-flight decision making.  

FY2019 Accomplishments

Work proceeded on a number of fronts this past year in developing weather forecast guidance to support UAS flight planning and operations.  RAL’s Graphical Turbulence Guidance (GTG) product was adapted to use HRRR data as input to provide short-term predictions of low-level turbulence (LLT) for NASA-UTM testbeds in Reno, San Francisco, and South Texas. RAL’s realtime mesoscale-to-microscale prediction system, which was conducted over the San Luis Valley of Colorado during ISARRA LAPSE-RATE in July 2018, continues to undergo rigorous evaluation with preliminary results in various stages of publication.  In addition to using UAS data collected to evaluate the prediction system, studies are underway to assess the value of assimilating UAS data to improve the simulations.

The GTG-LLT algorithm was implemented within the framework of the GTG model. A one-yr-long GTG LLT calibration was performed using the High-Resolution Rapid Refresh operational (HRRR) model, and optimum GTG ensembles of turbulence indices for clear-air and mountain-wave turbulence that minimize the mean absolute percentage error (MAPE) were determined. Also included were new turbulence indices specific to atmospheric boundary-layer turbulence. The results from this work have been published in Muñoz-Esparza and Sharman (2018). This algorithm was implemented and run in realtime to translate HRRR data into realistic estimates of turbulence intensity. A user-friendly display was developed that allowed users to view GTG-LLT turbulence forecasts. Figure 1 shows a screenshot displaying Eddy Dissipation Rate (EDR) forecasts for each of the NASA UTM test regions (Reno, San Francisco, and South Texas). The data, along with other relevant meteorological variables provided from the HRRR (e.g., wind speed, wind direction, wind gust, ceiling, visibility, flight category, precipitation rate, and ground elevation) aided in UTM planning and decision-making.

 Reno, San Francisco and South Texas.
Figure 1:  Screenshot of EDR at 150 m AGL obtained from the realtime GTG-LLT display developed to support NASA UTM field campaigns at three NASA UTM Test Sites: Reno, San Francisco and South Texas.

While HRRR-based guidance gives a good depiction of the mesoscale environment and associated potential for turbulence, much finer-scale simulations are needed to provide more accurate guidance on winds and turbulence relevant to small UAS flight planning. The Figure 2 provides an example of the information gain possible with mesoscale to microscale coupling. In the case shown below, the HRRR model is not able to capture fine-scale variations in low-level winds associated with nocturnal drainage flows that formed on a daily basis during the summer 2018 LAPSE-RATE field experiment that took place in the San Luis Valley of Colorado. The HRRR is also unable to capture the full dynamic range of wind speeds on this day when compared with the microscale simulation (Figure 2). These issues are of critical importance in flight planning for small UAS.

Figure 2. Wind speed and direction obtained from (left) HRRR and (right) WRF-LES using 100-m grid spacing for the San Luis Valley of Colorado. The AWOS station 04V marks the location of UAS observations shown in Figure X3.
Figure 2. Wind speed and direction obtained from (left) HRRR with 3 km grid spacing and (right) WRF-LES using 100-m grid spacing for the San Luis Valley of Colorado. The AWOS station 04V marks the location of UAS observations shown in Figure 3.

While these comparisons reveal the sensitivity of key atmospheric quantities to model resolution, the fidelity of the fine-scale simulations is still under investigation. UAS observations obtained during the drainage flow IOP were used to perform a detailed evaluation of the characteristics of the drainage flow (Figure 3). It is evident that after a one-hour model adjustment period, the modeled drainage flow timing and depth is similar to that observed by UAS with some notable differences. The narrow layer of light winds is not well resolved by the model. Additionally, the modeled drainage flow was a bit deeper and wind speeds a bit stronger than observed. At the same time, the timing of the wind shift and strength/depth of the up-canyon flow was well captured by the model.

Figure 3. Comparison of wind speeds at 04V from (left) meso-to-micro model and (right) two UAS operated by the University of Kentucky.
Figure 3. Comparison of wind speeds at 04V from (left) meso-to-micro model and (right) two UAS operated by the University of Kentucky.

Finally, EnKF data assimilation experiments have been performed using NCAR’s Data Assimilation Research Testbed (DART) to develop a UAS data assimilation capabilities. Studies are being performed to evaluate a number of aspects involved in UAS data assimilation including appropriate sampling rates, radius of influence of UAS data and treating observation error. EnKF was chosen to capture flow-dependent error covariances while concurrently providing a means for estimating forecast uncertainty. Initial evaluations have demonstrated the value of assimilating UAS data as shown in Figure 4. In this example, assimilation of UAS data collected by the University of Kentucky (see Fig. 3), significantly improved the predicted strength and depth of the cold pool observed in the exit region of Saguache Canyon as independently measured with a coptersonde operated by the University of Oklahoma.

Figure 4. (left) Observed potential temperature obtained from the University of Oklahoma Coptersonde compared with ensemble mean modeled values obtained (middle) with UAS data assimilation and (right) without UAS data assimilation. Model data was obtained using EnKF data assimilation and a 40 member ensemble.
Figure 4. (left) Observed potential temperature obtained from the University of Oklahoma Coptersonde compared with ensemble mean modeled values obtained (middle) with UAS data assimilation and (right) without UAS data assimilation. Model data was obtained using a 40 member ensemble run with 1 km grid-spacing.

FY2020 Plans

Future work will focus on performing more in-depth analysis of the model performance and evaluating the potential of UAS data assimilation to improve short term prediction of winds and turbulence. Both of these efforts will continue to mine the suite of observations collected during the July 2018 LAPSE rate experiment resulting in multiple publications.

WEATHER IMPACTS ON UNMANNED AERIAL SYSTEMS

To predict the likelihood of mission success, given a predicted flow field, it is critical to understand how small UAS will respond to variations in turbulence intensity. In this NASA-sponsored study we are developing a full non-linear, six-degree-of-freedom flight-simulation capability for both fixed-wing and multirotor UAS. The key aspect of this work is to include the effects of arbitrary wind fields, via the induced aerodynamic forces and moments. In order to actuate these forces and moments for fixed-wing vehicles a “four point method” is employed.  With this approach, the aerodynamic effect of turbulent flow on a UAS is calculated by assuming the turbulent flow can be approximated by linear functions that are evaluated at four points on the vehicle.  Note that modeling the response of a fixed wing UAS is much different than modeling the response of a small multirotor aircraft. A key aspect of this work has been the development of a sub-grid scale wind model that provides three-dimensional turbulence representation for wind field simulations at a resolution required to simulate impacts on small UAS.

FY2019 Accomplishments

Intuitively, it is clear that small UAVs, flying at relatively low airspeeds, will be more sensitive to small-scale wind structures than larger faster aircraft. Figure 5 provides a concrete example, showing the vertical acceleration response of a small fixed-wing UAV (black) and a mid-size commercial transport aircraft (red) to the vertical wind component. Both vehicles are at low altitudes and slow (relative) airspeeds. It can be seen that the wavelengths of the winds that are most important to the UAV are in the meters to tens of meters range while impacts on a manned transport aircraft peak at a scale of 500 m. This implies that analyzing the impact of small-scale wind structures on UAVs quantitatively, it is imperative to model the winds accurately at these small scales.

Figure 5. Vertical acceleration response due to vertical wind for UAS (black) and mid-sized transport aircraft (red) – as a function of input wind wavelength.
Figure 5. Vertical acceleration response due to vertical wind for a fixed-wing UAS (black) and mid-sized transport aircraft (red) – as a function of input wind wavelength.

As described above, NCAR/RAL has developed the capability to model realistic wind fields via LES methods and synthetic turbulence fields via analytic/numerical methods. To enforce computational stability, numerical weather models typically filter out the smallest scales of the flow. Moreover, subgrid-scale processes are parameterized.  Therefore, in reality weather models can only fully resolve processes that are 5 to 10 times as large as the grid spacing. To overcome this issue, an empirical method was developed to merge information from  25 m LES wind fields (can resolve eddies of 250 m or greater) with synthetic meter-scale isotropic turbulence.  By matching the energy from a spectral decomposition of LES winds at the lower frequencies with that for isotropic turbulence at higher frequencies we can create a merged spectrum of turbulence energy. This matching information can then be used to produce the space- and time-varying winds at scales relevant for modeling impacts on a small UAS as illustrated in Figure 6. It can be seen that the LES-alone data provides the larger-scale variation in the winds, while the merged wind field provides much finer scale and realistic variability that is consistent with the larger-scale variations obtained with the LES.

The LES-alone and merged subgrid/LES winds were then used as input to a three degree of freedom (airspeed, height and pitch), small UAS flight simulation. Figure 7 shows the results for the height and acceleration response of the small, fixed-wing UAS flown though the merged wind field shown in Figure 6. The red curve is the height/acceleration response to the LES-alone vertical wind component, and the black curve is that for the merged LES/subgrid wind data. From these figures, it is clear that the LES-alone wind component is insufficient for simulating the magnitude of vertical variations along a planned UAS flight path intending to remain level.

Figure 6. Vertical wind component from LES (red) and merged subgrid turbulence and LES (black).
Figure 6. Vertical wind component from LES (red) and merged subgrid turbulence and LES (black).
Figure 7. Small UAS (left) height and (right) acceleration response to flight through LES-alone vertical wind component (red) and merged subgrid turbulence/LES (black).
Figure 7. Small UAS (left) height and (right) acceleration response to flight through LES-alone vertical wind component (red) and merged subgrid turbulence/LES (black).

Finally, as part of this project, we have also convened a UTM Weather Users Group which consists of approximately 30 individuals from a broad range of backgrounds and interests. The goal of this user group is to develop an initial UTM Weather Concepts document based on discussion and presentations made in a bi-monthly teleconference. The Concepts document will be developed in collaboration with a UTM Weather Advisory group and the NASA UTM group. The advisory group contains a cross-section of members from the UTM community, including public organizations, private companies, and research organizations. A number of questions has arisen regarding the implementation of weather data and products within UTM. In order to answer these questions we will need to baseline what is used now by the UAS community and identify gaps.

FY2020 Plans

Future work will focus on completing the implementation and verification of the fixed-wing and multirotor vehicle simulation and three-dimensional LES-subgrid turbulence merging. We will also complete the sequence of telecons, the last being on the subject of “UTM Supplemental Data Service Providers.” The information from these telecons will be used to develop an initial UTM Weather Concepts document.

WEATHER CHALLENGES FOR URBAN AIR MOBILITY

One of the critical elements that might limit more widespread use of UAM is weather. For commercial aviation, currently 25% of the aircraft get delayed and 75% of these delays are caused by weather. It is important to understand the implications of weather on UAM to determine its resiliency. Thus far, detailed low-altitude weather information was not as critical for the aviation industry, but with the emergence of small UAS and in anticipation of passenger carrying electric/hybrid vertical take-off and landing aerial vehicles (eVTOLs), it has become significantly more important to understand the implications of different types of weather on such operations.

FY2019 Accomplishments

This past year, an in-depth analysis was conducted to assess the observing infrastructure and routinely available weather guidance with regard to their adequacy to support emerging modes of transportation, like UAS and UAM. The focus was primarily on the Dallas / Fort Worth (DFW) metropolitan area, which has been selected as a testbed for early adoption of eVTOL flight operations. Also, fine-scale building-resolving simulations of the wind and turbulence characteristics in the DFW downtown area were conducted to anticipate operational UAM challenges and limitations.

Figure 8. Monthly wind roses based on METAR reports from the Dallas/Fort Worth (KDFW) ASOS site. Wind speed ranges are marked in colors and values are in knots.
Figure 8. Monthly wind roses based on METAR reports from the Dallas/Fort Worth (KDFW) ASOS site. Wind speed ranges are marked in colors and values are in knots.
Figure 9. Vertical cross section of fine-resolution wind flow through urban canopy.
Figure 9. Vertical cross section of fine-resolution wind flow through urban canopy.

The current weather observing infrastructure to support UAM over DFW was documented, capturing both in situ and remote sensing capabilities. Moreover, a wide range of routinely generated weather guidance products was identified that includes analysis products as well as forecast products. The core of the effort focused on climatological analyses of relevant weather characteristics (e.g., Fig. 8), including a discussion of high-impact weather scenarios. In the example shown, winds from the south exceeding 15 knots can occur frequently from March through June at DFW. This type of climatological information will be critical when planning eVTOL operations and routing structures. Based on these analyses, gaps were identified in both the observing infrastructure and the available weather guidance that may limit support of the emerging modes of aerial transportation. Opportunities were sought for enhancing the present weather capabilities beyond the current shortcomings.

The most challenging issue identified was to appropriately capture the potentially very dynamic situation of winds and turbulence that can occur within an urban landscape (Fig. 9) to enable safe, efficient and reliable UAM operations in the future. Moreover, in order to achieve operational reliability, serious consideration needs to go into enabling all-weather operations, including dealing with low-visibilities, impacts from thunderstorms (e.g., sporadic winds, heavy rain, hail and lightning) and wintry conditions (snow and in-flight icing).

FY2020 Plans

The above observational analyses will be expanded to include two dozen major cities across the United States. The fine-scale model simulations will be enhanced and digested with a specific focus on impacts on operations between select takeoff and landing spots in the DFW cityscape.

Citations

Muñoz-Esparza and Sharman (2018). An Improved Algorithm for Low-Level Turbulence Forecasting, Journal of Applied Meteorology and Climatology, 57, 1249 – 1263.

 

 

 

Ceiling and Visibility Products for Alaska

Background

Poor weather conditions, particularly restricted visibility and low cloud tops, were the leading cause of fatal general aviation (GA) accidents in Alaska from 2001-2012.  Traditional weather observations from Alaska’s widely dispersed airfields inadequately forewarn of weather likely to be encountered along routes between stations or, in particular, through hazardous mountain passes with localized conditions.  In 2014, the National Transportation Safety Board (NTSB) included “General Aviation: Identify and communicate hazardous weather” on its Most Wanted List to improve transportation safety.

Since 2016, the NCAR/RAL, the MIT/LL, Environmental Prediction Center (EMC), the Alaska Aviation Weather Unit (AAWU), and the FAA have been involved in a collaborative effort to produce a rapidly-updated, high resolution, gridded product of ceiling and visibility (C&V) conditions across Alaska.  This product, known as the Ceiling and Visibility Analysis – Alaska (CVA-AK) has supported AAWU forecasters in developing the Terminal Aerodrome Forecasts (TAFs) for C&V conditions across Alaska at or near instrumented and non-instrumented airfields and along data-sparse routes between airfields including treacherous and heavily-traveled mountain passes.

The CVA-AK product combines ceiling and visibility information from the latest NCEP RAP model with surface-based observations of C&V from the ASOS and AWOS stations across Alaska using data fusion techniques to produce grids of flight category for both ceiling and visibility. The NCEP model C&V forecast data are adjusted using a calibration algorithm that uses 2 hr model forecasts and corresponding observations from the last 30 days to reduce forecast bias. The final product is generated by applying a cloud mask based on GOES satellite data to remove modeled ceiling heights in regions deemed to be cloud free. The final gridded products are updated every 20 min and are viewable by AAWU forecasters on the IC4D display system that they use to produce their aviation weather forecasts.

Model Calibration

Frequency of ceiling heights of 1000 ft or less from (left) RAPv4 and (center) surface met stations along with (right) the frequency bias (M – O) at each surface met station site for (top) January and (bottom) June 2019.
Figure 1. Frequency of ceiling heights of 1000 ft or less from (left) RAPv4 and (center) surface met stations along with (right) the frequency bias (M – O) at each surface met station site for (top) January and (bottom) June 2019.

Understanding the level of consistency between surface-based observations and forecasts of C&V obtained from RAPv4 is critical for developing a useful analysis product. As seen in Figure 1, the model provides much greater detail than can be obtained from surface observations. The model resolves gradients in the frequency of low ceilings moving from peak values in coastal regions to much less frequent occurrence in the interior of Alaska. The model also resolves gradients in the frequency of low ceilings associated with terrain where the higher terrain is more often shrouded in clouds. Despite these attributes of the model, notable biases in the model predictions are also evident in the frequency difference (modeled minus observed frequency) plots (rightmost panels in Figure 1). These comparisons can be used to remove first order bias in the model and to better understand the representativeness of the surface observations.

Calibrated values of ceiling heights for a raw ceiling height of 1 kFT for June 2019. The calibrated values found in most coastal areas are generally greater than 1 kFT in order to correct the tendency of the model to over-predict the frequency of ceiling base heights below 1 kFT.
Figure 2. Calibrated values of ceiling heights for a raw ceiling height of 1 kFT for June 2019. The calibrated values found in most coastal areas are generally greater than 1 kFT in order to correct the tendency of the model to over-predict the frequency of ceiling base heights below 1 kFT.

Biases in the modeled ceiling and visibility forecasts are reduced using a quantile matching algorithm. Quantile matching simply maps the cumulative density function of the model values to that obtained from the observations. The matching is performed at a set of predetermined ceiling heights or visibilities to generate calibration functions for each quantity that are a function of space and time. An example of the calibrated values for a raw ceiling height of 1000 ft is shown in Figure 2. In this example, the largest adjustments to the raw ceiling height values are evident as the darker shades of blue or brown.

Evaluation of Calibration Algorithm

Box and whisker plots showing interquartile range of model  (raw - red and calibrated - green) ceiling height values for an observed category of (top) IFR and (bottom) MVFR obtained for a subset of surface met stations across Alaska for the period 1-30 June 2019. The median value is denoted by a filled circle while the IQR is given by the error bars. The 75th percentile value occasionally lies outside the plotting range (i.e., 10 kFT). Station locations are indicated in the map on the right.
Figure 3. Box and whisker plots showing interquartile range of model  (raw - red and calibrated - green) ceiling height values when the observed category is (top) IFR and (bottom) MVFR. Evaluation is for a subset of surface met stations across Alaska for the period 1-30 June 2019. The median value is denoted by a filled circle while the IQR is given by the error bars. The 75th percentile value occasionally lies outside the plotting range (i.e., 10 kFT). Station locations are indicated in the map on the right.
Table. Heidke Skill Scores computed for all Alaska stations with observation vs model correlations greater than 0.35. Green fill indicates that the calibration improved the skill.
Table 1. Heidke Skill Scores computed for all Alaska stations with observation vs model correlations greater than 0.35. Green fill indicates that the calibration improved the skill. 

In 2019 the calibration algorithm was improved and evaluated using data collected in both January and June 2019. At each station location we determined whether or not the distribution of ceiling height values obtained for a given observed category was improved. Figure 3 (taken from June 2019) provides a general indication of how the calibration algorithm changes the distribution of ceiling height values. Ideally, the full range of model values should fall in the “target range.” It is seen that the calibration algorithm generally increases the value of ceiling height at the 75th quantile for both observed IFR and MVFR categories. This increase in the 75th quantile value is in response to the model’s tendency to predicted low ceiling too often. Despite this increase, the 75th quantile value for observed IFR conditions is generally maintained within the targeted range. At the same time, the calibration algorithm greatly improves the skill at predicting the MVFR category. Examples of this improvement are seen at several stations (e.g., PASC, PAPO, PAKH, PAAD, PAIW), where the median value of ceiling heights was correctly shifted from IFR to MVFR. The overall impact of calibration is to improve the skill in IFR, MVFR and VFR categories for both ceiling and visibility as indicated by the change in the Heidke Skill Score given in Table 1.

Slant Visual Range Study

Figure demonstrating the important distinction between RVR and SVR
Figure 4. The important distinction demonstrated between RVR and SVR

The slant visual range is a critical aspect of aviation especially for General Aviation (GA) and, more recently, remotely piloted vehicles (RPVs) and unmanned aerial system (UAS) operations. GA pilots often require surface-based visual landmarks like rivers and roads to navigate in VFR conditions and also need to be able to clearly see the airport/runway during approach and landing. Under FAA regulations, RPVs and most UAS operations require Visual Line of Site (VLOS). In this case, a surface-based observer looking upward must be able to visually detect the aircraft with the unaided eye. In each of these situations, the requirement is to be able to see on an angle away from the surface; however, current observations obtained by ASOS and AWOS are all reporting horizontal visibility or Runway Visual Range (RVR). Figure 4 demonstrates this important distinction between RVR and SVR. The goal of this study is to assess the impact of the disconnect between RVR measurement and SVR requirements  through literature survey and case study analyses. It is expected that the needs of GA for improved safety and emerging needs of UAS and Urban Air Mobility in terms of SVR will elevate the distinction between RVR and SVR to a critical issue in the years to come.

Plans for 2020

The calibration algorithm will be configured to utilize the Alaska High Resolution Rapid Refresh and run in realtime to supply a data feed of the calibrated products to EMC. This calibrated ceiling and visibility data will be assessed to determine whether or not it might be suitable for use in improving. first guess ceiling and visibility values used to produce the Realtime Mesoscale Analysis (RTMA). In addition, the literature review and case study exploring the importance of SVR to aviation operations including for emerging modes of transportation (e.g., air taxis, delivery drones) will be completed and recommendations will be made on next steps.

New and Emerging Applications

Identify, explore, develop and implement advanced weather decision support systems for new and emerging user sectors.

  • Surface Transportation Weather
  • Renewable Energy
  • Weather Prediction Machine Learning Optimization
  • Statistical Methods in Forecasting
  • Wildland Fire Modeling and Prediction
  • Model Development using Machine Learning

Surface Transportation Weather

BACKGROUND

RAL is a key contributor to road weather research across the US, including connected vehicle and winter maintenance support applications in multiple states and airports. Based around the Maintenance Decision Support System (MDSS) and the Pikalert® System, which were developed in conjunction and with support of the United States Department of Transportation’s (USDOT) Federal Highway Administration (FHWA), RAL’s road weather research program continues to engage with stakeholders in the public and private sectors to advance road weather research.

FY2019 Accomplishments

 Updated Pikalert display over Iowa.
Figure 1. Updated Pikalert display over Iowa.

Pikalert®

The Connected Vehicle program is focused on improving safety, mobility, and environmental efficiency. Connected Vehicle technologies can provide data from millions of vehicles (including weather observations) that will be available to support both road weather applications and the wider weather community. RAL partners with multiple state Departments of Transportation (DOTs) to implement a Pikalert® System (Figure 1) for their area of operation. Pikalert incorporates vehicle-based observations of the road and surrounding atmosphere with other, more traditional weather data sources (including weather radar and road-side weather observation stations). The vehicle data are quality checked and the fused vehicle and weather data are used for current weather assessments and forecasts of road weather conditions out to 72 hours. 

In FY19, RAL continued to enhance the Pikalert system with our partner states. These enhancements included tuning of the system running over Alaska, Iowa, Wyoming, Nevada, and Colorado as well as a new web-based display taking advantage of new display technology and based on stakeholder feedback.

Enhanced Products for Alaska

RAL continued its collaboration with the Alaska DOT to maintain the current Pikalert® system across the state. Positive feedback was received from maintenance managers, who integrate use of the system into their daily activities. System enhancements in FY19 included extension of the corridors covered north to Nome along with an update to the underlying model, where DICast location-based forecasts are expected to improve accuracy over the former grid-based system.

 Mobile display forecast for a runway at Denver International Airport.
Figure 2: Mobile display forecast for a runway at Denver International Airport.

The WYDOT CV Pilot

The Wyoming DOT (WYDOT) Connected Vehicle Pilot is a USDOT-funded initiative to move developed Connected Vehicle technologies out of the research arena and into operational deployment. While technical deployment is delayed due to issues with vehicle and roadside hardware, RAL continues to work closely with WYDOT personnel and on-site meteorologists to tune the Pikalert® system, which is providing hazard assessments to the Traffic Management Center for updating their Traveler Information Messaging. RAL also began a supplemental project with WYDOT to tune the Pikalert system for the state.

Nevada DOT

RAL worked closely with Nevada DOT to produce the updated display capability discussed above under the Pikalert® update. RAL also continued tuning of the system to meet NDOT’s needs.

Iowa DOT

RAL stood up a Pikalert instance for the state of Iowa, covering major roadways across the state 

Minneapolis and Denver Airport MDSS and Friction

RAL is working with the international airports in Minneapolis and Denver to improve runway decision support. Adverse winter weather can significantly disrupt airport operations both in relation to aircraft safety and visibility as well as runway friction and surface conditions. At Denver, the MDSS is configured across all major runways and uses known pavement information and rules of practice for winter maintenance to assist in chemical and plow application and deployment. During FY19, RAL continued to support the MDSS at Denver and  Minneapolis airports to support the Runway Friction and Closure Prediction System (RFCPS), which relies on data processing and machine learning techniques developed in RAL to combine a weather forecast with maintenance rules of practice to predict runway friction and runway closure alerts. A major advancement in FY19 was the development of a mobile display in conjunction with airport stakeholders (Figure 2).

FY2020 Goals

RAL plans to continue developing Pikalert® with current and future state partners to provide high quality tactical and forecast weather hazard information in support of DOT operations and traveler information messaging. FY20 goals also include expansion of decision support services for transportation outside of winter, including flooding, fire, and summer-related hazard decision support.

Renewable Energy

Background

Since 2009 RAL has collaborated with university researchers, U.S. Department of Energy (DOE) laboratories, international research organizations, commercial partners, and other NCAR laboratories to develop methods to more accurately analyze and predict wind and solar power in support of the renewable energy industry. Projects have focused on resource assessment, analysis of the interaction between the atmosphere and operating wind turbines, real-time wind, solar, and load forecasting to improve operations and economics of incorporating renewable energy into the power mix, and characterization and quantification of variability in wind and solar energy.

NCAR scientists have become recognized as world experts in applying meteorology concepts for enhancing renewable energy production. During 2019, they collaborated on two new book chapters documenting the background of meteorological modeling for renewable energy (Haupt et al. 2019a; Jiménez et al. 2019). Numerous other journal articles related to renewable energy research have been published this year or are in review; many of these are referenced in the sections below.

FY19 Accomplishments

NCAR/RAL is currently expanding capabilities of WRF-Solar through enhancing physics parameterizations, and developing a solar irradiance nowcasting system by combining satellite observations from multiple platforms with the WRF numerical weather prediction model. A large-scale, ongoing collaboration with Kuwait is furthering wind and solar power forecasting research, and the next phase of a project in New York will lead to distributed solar power forecasting statewide. Further research in coupling of mesoscale to microscale models and in flow in complex terrain will enable further improvements in high-resolution wind forecasting.

Wind Power Forecasting for Xcel Energy

As the wind power capacity in the areas served by Xcel Energy grew, Xcel Energy recognized a need for accurate forecasts of this variable resource. In 2009 NCAR started collaboration with Xcel Energy aimed at developing a renewable power forecasting system. The renewable power forecasting system enables more economical utilization of resources and more reliable grid operation while still meeting the needs of the utility’s electricity customers. In collaboration with Xcel Energy, RAL has developed, deployed, and transferred wind and solar power forecasting systems, which allows powering down traditional coal- and natural gas–powered plants when sufficient winds and solar irradiance are predicted (Mahoney et al. 2012; Haupt et al. 2013; Haupt and Mahoney 2015; Haupt and Kosović 2017; Haupt et al. 2017c). Operational implementation of the initial day-ahead forecasting system resulted in significant savings for the utility and the ratepayer, along with substantial reductions in emissions of carbon dioxide and other pollutants to the atmosphere (Pierce 2014). Although the most recent collaboration with Xcel Energy was completed during FY15, during FY19 the team continued publications on the results, including a new paper submitted to Energies (Kosović et al. 2019).

Renewable Energy Forecasting for Kuwait

FY19 was the second full year of NCAR’s Renewable Energy Forecasting for Kuwait project, a 3-year, $5.1M project sponsored by the Kuwait Institute for Scientific Research (KISR) (https://news.ucar.edu/126802/ncar-develop-advanced-wind-and-solar-energy-forecasting-system-kuwait). There are 18 tasks in this large project, spanning areas of numerical weather prediction (NWP), solar and wind nowcasting, general science, software engineering, system assessment, and management. The ultimate goal of this project is to deliver to KISR an operational wind and solar power forecasting system, the Kuwait Renewable Energy Prediction System (KREPS), for both nowcasting and day-ahead time horizons (and beyond), with which they can provide forecasts to their national power grid operators and wind/solar power plant operators.

Figure 1. Shagaya Renewable Energy Park Phase 1 10-MW wind plant. Photo by Jared A. Lee (NCAR/RAL).
Figure 1. Shagaya Renewable Energy Park Phase 1 10-MW wind plant. Photo by Jared A. Lee (NCAR/RAL).
 Gerry Wiener, Branko Kosović, Sue Ellen Haupt, and Jared Lee) visiting the Shagaya Renewable Energy Park for its official Grand Opening ceremony on 20 February 2019. Photo by Jared A. Lee (NCAR/RAL).
Figure 2. The NCAR project management team (L to R: Gerry Wiener, Branko Kosović, Sue Ellen Haupt, and Jared Lee) visiting the Shagaya Renewable Energy Park for its official Grand Opening ceremony on 20 February 2019. Photo by Jared A. Lee (NCAR/RAL).

Kuwait has a stated national goal of 15% renewable energy generation by 2030 (KISR 2019), and to that end has established the Shagaya Renewable Energy Park in the desert about 100 km west of Kuwait City. Phase 1 of Shagaya is now complete, with a demonstration-scale 10-MW photovoltaic (PV) solar plant commissioned in May 2017 (Al-Rasheedi et al. 2019), a 10-MW wind plant commissioned in July 2017 (Figure 1), and a 50-MW concentrated solar power (CSP) plant that was commissioned in November 2018. The NCAR management team for this project traveled to Kuwait in February 2019 for the official Grand Opening of Shagaya, a ceremony that was widely covered by local media and attended by government ministers (Figure 2). Additional wind, PV solar, and CSP solar capacity is planned beyond that in Phases 2 and 3 of Shagaya, with a goal of 3–5 GW of combined wind and solar power installed capacity at Shagaya by 2030.

Throughout FY19 the NCAR team, in collaboration with researchers from Penn State University and Solar Consulting Services, has been building various aspects of the system, leveraging advancements made on several past and current renewable energy forecasting projects in RAL, and developing new and improved techniques and products with pioneering research. The accomplishments in FY19 include securing real-time data feeds from the PV solar and wind farms at Shagaya in spring 2019; hosting a two-month training workshop on research data, machine learning, and high performance computing for a KISR staff person who will be in charge of operating KREPS upon its transfer to KISR; the continued operation of daily WRF-Solar® forecasts for day-ahead forecasting at 3-km grid spacing over Kuwait; studying the correspondence between various forecast models that predict aerosol lofting and advection, to identify the best aerosol forecast product to use with WRF-Solar (Lee et al. 2019a); improving analog ensemble (AnEn) forecasts for rare events (Alessandrini et al. 2019); testing new machine learning models to further improve StatCast-Wind and StatCast-Solar forecasts in the first 6 h (see the Weather Prediction Machine Learning Optimization]  section for more information on StatCast-Wind and StatCast-Solar); researching improved methods for power conversion (McCandless and Haupt 2019; Brummet et al. 2019); beginning to carry out meteorological case studies to better understand forecast performance; making several improvements to DICast® that will also feed back into other RAL projects; and enhancing the power grid operator display with feedback from KISR staff and grid operators in Kuwait. Several papers from this project are currently in preparation and will be submitted during FY20.

Solar Forecasting for New York State

In FY19, NCAR completed Phase 2 of a multi-phase, multi-agency effort to improve solar forecasting in New York State. The work for Phases 1 and 2 was funded by both the New York Power Authority (NYPA) and DOE, and was done in collaboration with partners at the Electric Power Research Institute (EPRI) and Brookhaven National Laboratory (BNL). Phase 3 has been funded by the New York State Energy Research and Development Authority (NYSERDA) and NYPA, also includes the University at Albany as a collaborator, and will begin in FY20 for two and a half years.

Figure 3. WRF-Solar 6-h forecast of GHI, valid on 1 Jun 2018 at 1800 UTC.
Figure 3. WRF-Solar 6-h forecast of GHI, valid on 1 Jun 2018 at 1800 UTC.

NCAR’s work in Phase 1 of this project used a WRF-Solar® 10-member ensemble to assess the modeled variability in solar resource, focusing on various locations around the New York at which BNL proposed to install networks of sky cameras, and comparing it to observed irradiance variability at BNL. In Phase 2 NCAR configured WRF-Solar for a one-year reforecast dataset of nowcasts (0–6 h) at 3-km grid spacing over the entirety of New York State (Figure 3). This WRF-Solar reforecast dataset, combined with meteorological and irradiance observations at BNL, provided the training dataset for machine learning methods (StatCast-Solar) to blend recent observations with WRF-Solar for improved nowcasts of irradiance in the first 1–2 hours (Lee et al. 2019b). Phase 3 will extend this work further, making the WRF-Solar and StatCast-Solar systems quasi-operational at BNL and New York State Mesonet sites, building an open source gridded model blending tool for use with WRF-Solar with select publicly available models for both intra-day and day-ahead time horizons, and building an open source solar power forecasting algorithm for select utility-scale PV plants and distributed PV at ZIP code granularity across New York State.

WRF-Solar v2

In collaboration with the Pacific Northwest National Laboratory (PNNL) and Vaisala, NCAR is developing the WRF-Solar® version 2 model (Jiménez et al. 2016a), supported by the DOE Solar Energy Technologies Office. While the first version of WRF-Solar resulted in significant improvements in short-range and day-ahead forecasts for both clear sky and cloudy conditions there are several areas where further improvement could result in significant error reduction in predicted solar irradiance. The goal of this project is to reduce forecast errors of global horizontal irradiance and direct normal irradiance and to yield better forecasts of irradiance ramps, improvements in estimates of sub-grid scale variability, and more accurate estimates of forecast uncertainty. WRF-Solar v2 will include a number of enhancements including:

  • New representation of boundary-layer clouds (both shallow cumulus and the breakup of stratocumulus) including the impact of entrainment on cloud fraction in a grid cell,
  • Improved treatment of cloud microphysics, and impacts of aerosol (including black carbon),
  • New parameterizations to account for the sub-grid temporal variability of solar irradiance during periods with broken clouds, and
  • Detailed analysis to better quantify model uncertainty and improved calibration of WRF-Solar v2 using uncertainty quantification (UQ) techniques.

Figure 4. Sketch representing the physical processes that WRF-Solar® improves. The different components of the radiation are also indicated.
Figure 4. Sketch representing the physical processes that WRF-Solar® improves. The different components of the radiation are also indicated.

With these improvements, WRF-Solar v2 will be a new tool that will lead to improved intra-day and day ahead forecasts. The new version of WRF-Solar (Figure 4) will be a community model that will become the new standard in irradiance forecasts.

During 2019 we made progress in the following areas:

  • Improved the representation of boundary layer clouds and examined the potential of a better representation of horizontal cloud entrainment,
  • Identified a parameterization to account for sub-grid temporal variability of solar irradiance,
  • Compared reference simulations performed with WRF-Solar v1 against an improved representation of the unresolved clouds, and
  • Developed the strategy to include in WRF-Solar the impacts of black carbon on radiation as well as the methodology to assess the developments.

WRF-Solar Ensembles

In collaboration with the National Renewable Energy Laboratory (NREL), NCAR is enhancing the WRF-Solar model (Jiménez et al. 2016a) to incorporate a probabilistic framework specifically tailored for solar energy applications.

Figure 5. GHI observations and simulations from a WRF-Solar ensemble for a day-ahead forecast at the Table Mountain (TBL) SURFRAD site near Boulder, CO. The ensemble introduces stochastic perturbations in the radiation parameterization. The top panel is for the observed and simulated irradiance, while the bottom panel shows the difference between the observed irradiance and the various ensemble member simulations (simulated minus observed).
Figure 5. GHI observations and simulations from a WRF-Solar ensemble for a day-ahead forecast at the Table Mountain (TBL) SURFRAD site near Boulder, CO. The ensemble introduces stochastic perturbations in the radiation parameterization. The top panel is for the observed and simulated irradiance, while the bottom panel shows the difference between the observed irradiance and the various ensemble member simulations (simulated minus observed).

During a first task of the project, we have identified the variables from six physical packages that produce the larges impact on the shortwave irradiance. These packages include a shortwave radiation parameterization, a microphysics parameterization, a planetary boundary layer parameterization, a land surface model, and two parameterizations of the radiative effects of unresolved clouds. The relevant variables have been identified using a sensitivity analysis based on adjoint modeling (Yang et al. 2019).

During the second task, we are developing WRF-Solar to introduce stochastic perturbations of the variables identified in the previous task. We have incorporated a user-friendly interface wherein the user can select which variables to perturb and the characteristics of the perturbations. We are in the process of coupling these perturbations to the physical packages. So far we have coupled the perturbations to the radiation parameterization. Figure 5 shows an example of an ensemble simulation introducing stochastic perturbations in the radiation parameterization. We are in the process of incorporating the perturbations in a parameterization that accounts for the radiative effects of unresolved clouds.

MAD-WRF: A solar irradiance nowcasting system to support the Group on Earth Observations (GEO) Vision for Energy

A GEO Vision for Energy (GEO-VENER) goal includes “the availability and long-term acquisition of data from satellite and in-situ instruments and models to make possible the effective deployment, operation, and maintenance of renewable energy systems and their integration in the grid.” The challenge in renewable energy systems is managing the high spatial and temporal variability of renewable resources. The adverse effects of this high variability in energy production can be mitigated via accurate predictions of the renewable resources (e.g., solar irradiance).

To overcome the limitations of current solar irradiance nowcasting systems we are blending a satellite- based initialization system with an NWP-based nowcasting approach to create an improved end-to-end solar irradiance forecast system, called MAD-WRF. The development of MAD-WRF forms part of the National Aeronautics and Space Administration (NASA) Earth Science Division (ESD) efforts to advance specific elements of the GEO Work Programme 2017–2019 through the Applied Science Program (ASP).

MAD-WRF will blend two nowcasting systems. The Multi-sensor Advection Diffusion nowCast (MADCast) version 2 (Xu et al. 2016) assimilates infrared profiles from instruments on board different satellites (GOES, MODIS, AIRS, etc.) using a particle filter to infer the presence of clouds. This three-dimensional (3D) cloud field is subsequently advected and diffused by a modified version of the WRF model that produces the solar irradiance forecast. The second nowcasting system is based on WRF with extensions for solar energy applications (WRF-Solar, Jimenez et al. 2016a,b; Haupt et al. 2018a). Our results running both models indicate that MADCast typically produces superior performance than WRF- Solar at the beginning of the simulated period (Lee et al. 2017; Haupt et al. 2018a). This is due to the improved satellite-based cloud analysis. However, after an hour or two into the forecast, the improved physics of WRF-Solar provides the superior solar irradiance forecast. Blending the two systems in MAD-WRF will provide a prediction system with the strengths of both nowcasting methods. Figure 6 shows a conceptual diagram illustrating the performance of MADCast, WRF-Solar, and the expected performance of MAD-WRF. An initial exploration of the viability of MAD-WRF showed promising results (Haupt et al. 2018a).

Figure 6. Schematic diagram illustrating the current performance of WRF-Solar and MADCast in the first six hours of forecast (blue and red solid lines), their expected performance after the improvements introduced in this project (dashed lines), and the expected performance of MAD-WRF (green solid line).
Figure 6. Schematic diagram illustrating the current performance of WRF-Solar and MADCast in the first six hours of forecast (blue and red solid lines), their expected performance after the improvements introduced in this project (dashed lines), and the expected performance of MAD-WRF (green solid line).

During 2019 we blended MADCast and WRF-Solar into a first version of MAD-WRF. In this version, the hydrometeors are nudged toward the hydrometeors that are advected and diffused by the WRF model without any microphysics. This strategy allows us to take advantage of the strengths of the two blended models illustrated in the diagram shown in Figure 6. We are in the process of comparing MAD-WRF with WRF-Solar to quantify the improvements.

We also made progress in the cloud analysis. We completed a first version of the cloud initialization system wherein we can impose the cloud mask and the cloud top height retrieved from NOAA’s GOES-16 satellite and the cloud base height from METAR stations. We are also training a statistical model to calculate the cloud mask directly from GOES-16 radiances in order to evaluate if we can improve the cloud mask level-2 product from GOES-16.

We are in the process of setting up MAD-WRF for a demonstration that will take place during the last year of the project (1 Feb 2020–31 Jan 2021).

Assessing the Value of WRF-Solar with Enhanced Cloud Initialization for Solar Irradiance Nowcasting

Iberdrola has approached NCAR to improve their solar irradiance nowcasting system. Iberdrola is interested in assessing the potential of using GOES-16/GOES-17 and METAR observations to improve solar irradiance nowcasting. This is well aligned with our research and development efforts with the MAD-WRF model. The goal of the project is to compare Iberdrola in-house forecasts with MAD-WRF simulations initializing the clouds with GOES-16/GOES-17 and METAR observations at two solar farms within CONUS.

We have started to process the data Iberdrola has sent, both observations and in-house forecasts, in preparation for the assessment. We have also performed preliminary WRF simulations at the target locations.

Mesoscale to Microscale Coupling for Renewable Energy

Figure 7. Process for coupling mesoscale to microscale simulations for wind plant management.
Figure 7. Process for coupling mesoscale to microscale simulations for wind plant management.

A collaboration with DOE began in FY15 that focuses on blending information from mesoscale model simulations into microscale simulations in order to provide a capability to more accurately model details of flow that impacts a wind plant (Figure 7). NCAR is leading a collaboration of DOE national laboratories: Pacific Northwest National Laboratory (PNNL), Lawrence Livermore National Laboratory (LLNL), National Renewable Energy Laboratory (NREL), and Los Alamos National Laboratory (LANL). This collaboration’s goal is to accomplish mesoscale and microscale coupled simulations of carefully selected cases that are representative of wind farm conditions.

During FY19 the Mesoscale to Microscale Coupling (MMC) project team conducted a rigorous analysis of the impact of modeling in the terra incognita on the microscale simulations (Rai et al. 2019). We found that 1) the upper range of the terra incognita is roughly the current depth of the boundary layer, 2) using higher resolution for the mesoscale model will produce a smaller fetch distance in a microscale simulation that will thus contain more turbulence kinetic energy, 3) use of the Lilly turbulence model on the microscale domain results in a higher level of turbulence than using the MYNN or YSU mesoscale planetary boundary layer (PBL) parameterization schemes, and 4) the microscale results do not vary with the type of turbulence model (PBL schemes or large eddy simulation [LES] closure) used by its parent domain whose grid spacing falls within the terra incognita.

In FY19, two coordinating initiatives position the team for making important contributions that can be easily transitioned to industry use. The first of these initiatives is building an MMC-specific Phenomena Identification and Ranking Table (PIRT) that allows us to identify the most important areas for research. The PIRT table identified that offshore wind issues are important, yet not modeled nor validated well at this time. Specific phenomena to pursue include low level jets, land-sea breezes, weather fronts, tropical cyclones, Nor’easters, thermal pooling and terrain-gap flows, icing and precipitation, surface energy and momentum exchange, air-water-wave interactions, and roughness and canopy effects. The second major initiative was to compile and archive MMC code in a GitHub repository that form the basis of the code and toolset that is being transitioned to industry. This repository includes assessment tools in the form of Jupyter notebooks written in Python that enable reproducible comparison of multiple techniques. It also includes a common base of the WRF model that is our mesoscale solver. This MMC version is based on WRF v4.1, includes MMC-specific upgrades and additions, and is accompanied by a “setups” repository.

NCAR oversaw and participated in two major intercomparisons: in the actual coupling and in designing and assessing best practices for initializing turbulence at the microscale that are subgrid to the mesoscale. Additionally, NCAR worked on near-surface physics improvements through the use of a machine-learning model in place of Monin-Obkhov Similarity Theory (MOST). Both random forests and artificial neural networks improved on the standard MOST approach.

Figure 8. Twice the turbulent kinetic energy at a location from the WFIP2 region. Both the observations and different numerical simulations are shown (see legend).
Figure 8. Twice the turbulent kinetic energy at a location from the WFIP2 region. Both the observations and different numerical simulations are shown (see legend).

We continued with our developments of a planetary boundary layer (PBL) parameterization that represents the turbulent mixing in three dimensions (see the 2018 RAL LAR entry on WFIP2 for more information on the 3D PBL development). The standard practice is only to account for turbulent mixing in the vertical direction (one-dimensional). We identified the source of model instabilities. To overcome this issue, we run our PBL parameterization in sub-steps smaller than the model time step. The scheme is now stable. Figure 8 demonstrates how the time series of twice the TKE at an observational site from the Second Wind Forecast Improvement Project (WFIP2; Olson et al. 2019). Our parameterization (labeled as PBL3, blue line) clearly shows the best comparison with observations (purple line). In particular, it shows superior performance compared to a standard one-dimensional PBL parameterization (red line).

To ensure that the MMC efforts remain relevant to the wind industry, the team held three webinars with industry, both to present our most recent advances and to solicit feedback from industrial partners on their needs and where they see the most useful advances. In addition, MMC formed an Industrial Advisory Panel including six members that represent wind plant developers, turbine manufacturers, and wind power forecasters. This Panel is helping to plan an Industry Workshop to be held in June 2020.

Finally, the team began the pivot toward studying MMC processes for the offshore environment during FY19. As stated above, the PIRT analysis identified the offshore environment as ripe for advance. As the team winds up the onshore efforts, the members are also beginning the process of identifying appropriate data, constructing ML models of the offshore surface layer, testing fully coupled simulations for an offshore case, and using actuator disk codes to simulate turbines in both WRF-LES and Nalu-Wind. This planned initial case study will further inform where to focus resources to make the most important progress.

Reports from previous years of the MMC project are available (Haupt et al. 2015, 2017a,b, 2019b). A summary journal article has also been accepted by BAMS and will be in print soon (Haupt et al. 2019c).

The MMC team continues to work collaboratively and has determined strategies to work through the remaining issues required to optimally provide coupled model simulations, including for the offshore environment. These simulations and advances in technologies will provide the wind industry new tools that can be used in the planning, design, layout, and optimization of wind plants, thus facilitating deploying higher capacities of wind generation.

Offshore Wind Resource Assessment for Alaska

In FY17 and FY18 NCAR partnered with NREL to perform a wind resource assessment for Alaska’s offshore regions. NREL had previously developed wind resource assessments for offshore regions of the conterminous U.S. (CONUS) and Hawai’i. This project performed the first known rigorous assessment of the offshore wind resource for Alaska, extending to 200 nautical miles from the coastline.

Figure 9. 100-m average wind speed from the 2002–2016 WRF simulation within the technical resource area. [From Fig. 14 in Doubrawa et al. (2017) and Fig. 4 in Lee et al. (2019c).]
Figure 9. 100-m average wind speed from the 2002–2016 WRF simulation within the technical resource area. (From Fig. 14 in Doubrawa et al. [2017] and Fig. 4 in Lee et al. [2019c].)

This resource assessment was based on a 14-year WRF simulation covering Alaska and its surrounding waters at 4-km grid spacing. The WRF dataset had previously been developed by RAL for regional climate and hydrologic applications in Alaska, and is publicly available for download (Monaghan et al. 2016, 2018). NREL team members performed the wind resource assessment, which found a large technical wind resource at hub height (Figure 9), including in favorable areas with shallow waters close to population centers and existing transmission grids (Doubrawa et al. 2017). NCAR team members performed the validation of this WRF dataset against available surface-based and radiosonde observations, in a study that was published this year (Lee et al. 2019c). The WRF modeled wind speed was found to have near-zero average bias and positive skill, which provided confidence of the wind resource assessment.

FY2020 will continue to be an exciting time for renewable energy research at RAL. New and continuing collaborations with national laboratories, university scientists, private companies, and foreign research institutions and companies will advance the state-of-the-science necessary to make a large penetration of renewable energy capacity feasible. In FY2020 significant efforts will include advancing comprehensive renewable power forecasting capabilities. An emphasis will be on expanding collaborations with industry partners and porting the research to other regions of the world, especially the Middle East and Asia.

Other plans include:

  • Technology transfer of the Kuwait Renewable Energy Prediction System (KREPS) to the Kuwait Institute for Scientific Research (KISR), and training of KISR staff on the system.
  • Developing a new proposal to KISR for a second phase of research tasks.
  • Initial development of open source gridded model blending (and model/obs blending) tools, and distributed PV power forecasting algorithms for New York.
  • Expansion of wind and solar forecasting capability into new areas.
  • Continued collaboration with DOE laboratories to develop best practices for coupling mesoscale with microscale simulations, focusing on complex terrain and nonstationary conditions, and further development of the WRF 3D PBL scheme.
  • Continued collaboration with NREL and PNNL focused on improving and expanding solar forecasting capabilities through development of an ensemble forecasting system and WRF-Solar v2.
  • Continued development of MAD-WRF to develop a more robust and accurate version of MAD-WRF to improve nowcasts of solar irradiance.

References

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Haupt, S. E., and W. P. Mahoney, 2015: Taming wind power with better forecasts. IEEE Spectrum, 52, 47–52, https://doi.org/10.1109/MSPEC.2015.7335902.

Haupt, S. E., W. P. Mahoney, and K. Parks, 2013: Wind power forecasting. In: Troccoli, A., L. Dubus, and S. E. Haupt (Eds.), Weather matters for energy, Springer, 528 pp., https://doi.org/10.1007/978-1-4614-9221-4.

Haupt, S. E., A. Anderson, L. Berg, B. Brown, M. J. Churchfield, C Draxl, B. L. Ennis, Y. Fang, B. Kosovic, R. Kotamarthi, R. Linn, J. D. Mirocha, P. Moriarty, D. Munoz-Esparaza, R. Rai, and W. J. Shaw, 2015: First Year Report of the A2e Mesoscale to Microscale Coupling Project. Pacific Northwest National Laboratory Report PNNL-25108, 124 pp.

Haupt, S. E., A. Anderson, R. Kotamarthi, J. J. Churchfield, Y. Feng, C. Draxl, J. D. Mirocha, E. Quon, E. Koo, W. Shaw, R. Linn, L. Berg, B. Kosovic, R. Rai, B. Brown, and B. L. Ennis, 2017a: Second Year Report of the Atmosphere to Electrons Mesoscale to Microscale Coupling Project: Nonstationary Modeling Techniques and Assessment. Pacific Northwest National Laboratory Report PNNL-26267, 156 pp., https://www.pnnl.gov/main/publications/external/technical_reports/PNNL-26267.pdf.

Haupt, S. E., A. Anderson, L. Berg, B. Brown, M. Churchfield, C. Draxl, C. Kalb, E. Koo, B. Kosovic, R. Kotamarthi, L. Mazzaro, J. Mirocha, E. Quon, R. Rai, and G. Sever, 2017b: Third Year Report of the Atmosphere to Electrons Mesoscale to Microscale Coupling Project. Pacific Northwest National Laboratory Report PNNL-xxxxx, 137 pp.

Haupt, S. E., P. A. Jiménez, J. A. Lee, and B. Kosović, 2017c: Principles of meteorology and numerical weather prediction. In: Kariniotakis, G. (Ed.), Renewable energy forecasting: From models to applications. Woodhead Publishing (Elsevier), Cambridge, MA, 373 pp., https://doi.org/10.1016/B978-0-08-100504-0.00001-9.

Haupt, S. E., B. Kosovic, T. Jensen, J. K. Lazo, J. A. Lee, P. A. Jiménez, J. Cowie, G. Wiener, T. C. McCandless, M. Rogers, S. Miller, M. Sengupta, Y. Xie, L. Hinkelman, P. Kalb, and J. Heiser, 2018a: Building the Sun4Cast system: Improvements in solar power forecasting. Bull. Amer. Meteor. Soc. 99, 121-135, https://doi.org/10.1175/BAMS-D-16-0221.1.

Haupt, S. E., B. Kosović, J. A. Lee, and P. A. Jiménez, 2019a: Mesoscale modeling of the atmosphere. In: Veers, P. (Ed.), Wind power modelling: Atmosphere and wind plant flow. IET Publishing, Stevenage, UK, in press (expected release 27 Dec 2019).

Haupt, S. E., D. Allaerts, L. Berg, M. Churchfield, A. DeCastro, C. Draxl, E. Koo, B. Kosović, R. Kotamarthi, B. Kravitz, L. Mazzaro, J. Mirochoa, E. Q uon, R. Raj, J. Sauer, and G. Sever, 2019b: FY 2018 Report of the Atmosphere to Electrons Mesoscale-to-Microscale Coupling Project. Pacific Northwest Laboratory Report PNNL-28259, 124 pp.

Haupt, S. E., B. Kosović, W. Shaw, L. K. Berg, M. Churchfield, J. Cline, C. Draxl, B. Ennis, E. Koo, R. Kotamarthi, L. Mazzaro, J. Mirocha, P. Moriarty, D. Muñoz-Esparza, E. Quon, R. K. Rai, M. Robinson, and G. Sever, 2019c: On bridging a modeling scale gap: Mesoscale to microscale coupling for wind energy. Bull. Amer. Meteor. Soc., in press, https://doi.org/10.1175/BAMS-D-0033.1.

Jacobson, M., C. Draxl, T. Jimenez, B. O’Neill, T. Capozzola, J. A. Lee, F. Vandenberghe, and S. E. Haupt, 2018: Assessing the wind energy potential in Bangladesh. NREL Tech. Report NREL/TP-5000-71077, 136 pp., https://www.nrel.gov/docs/fy18osti/71077.pdf.

Jiménez, P. A., J. P. Hacker, J. Dudhia, S. E. Haupt, J. A. Ruiz-Arias, C. A. Gueymard, G. Thompson, T. Eidhammer, and A. Deng, 2016a: WRF-Solar: Description and clear-sky assessment of an augmented NWP model for solar power prediction. Bull. Amer. Meteor. Soc., 97, 1249-1264, https://doi.org/10.1175/BAMS-D-14-00279.1.

Jiménez, P. A., S. Alessandrini, S. E. Haupt, A. Deng, B. Kosović, J. A. Lee, and L. Delle Monache, 2016b: The role of unresolved clouds on short-range global horizontal irradiance predictability. Mon. Wea. Rev., 144, 3099–3107, https://doi.org/10.1175/MWR-D-16-0104.1.

Jiménez, P. A., J. A. Lee, S. E. Haupt, and B. Kosović, 2019: Solar resource evaluation with numerical weather prediction models. In: J. Polo et al. (Eds.), Solar resources mapping: Fundamentals and applications. Green Energy and Technology, Springer Nature, Cham Switzerland, 367 pp. https://doi.org/10.1007/978-3-319-97484-2_7.

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Lee, J. A., S. E. Haupt, P. A. Jiménez, M. A. Rogers, S. D. Miller, and T. C. McCandless, 2017: Solar irradiance nowcasting case studies near Sacramento. J. Appl. Meteor. Climatol., 56, 85–108, https://doi.org/10.1175/JAMC-D-16-0183.1.

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Lee, J. A., P. Doubrawa, L. Xue, A. J. Newman, C. Draxl, and G. Scott, 2019c: Wind resource assessment for Alaska’s offshore regions: Validation of a 14-year high-resolution WRF data set. Energies, 12, 2780, https://doi.org/10.3390/en12142780.

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Weather Prediction Machine Learning Optimization

BACKGROUND

Figure 1. DICast system diagram.
Figure 1. DICast system diagram.

RAL is a leader in the development of intelligent weather prediction systems that blend data from numerical weather prediction models, statistical datasets, real time observations, and human intelligence to optimize forecasts at user– defined locations. The Dynamic Integrated Forecast System (DICast®) and the GRidded Atmospheric Forecast System (GRAFS) are examples of such technology (Figures 1 and 2).

Figure 2. GRAFS system design.
Figure 2. GRAFS system design.

DICast® is currently being used by three of the nation's largest commercial weather service companies. Applications of this technology continue to expand as there is a growing desire in industry to have fine–tuned forecasts for specific user–defined locations. This trend is clear in the energy, transportation, agriculture, and location–based service industries. RAL's expertise in meteorology, engineering, and applied mathematics and statistics is being utilized to address society's growing need for accurate weather information.

FY 2019 ACCOMPLISHMENTS

During this year significant research has been performed with machine learning techniques in an attempt to improve both short-term machine learning techniques, called StatCast, and DICast® forecasts in a project with the Kuwait Institute of Scientific Research (KISR). This system started using DICast® as the core forecast engine for a combined wind and solar forecasting system. This system will combine output from global numerical weather prediction models and a high-resolution version of WRF to produce custom forecasts for an extreme desert climate environment. The KISR project is a multi-stage machine learning methodology as StatCast, the machine learning based approached for wind and solar power predictions based on surface observations, is being developed for the KISR project for short-term predictions out to six hours, DICast® is utilized across time scales, and the Analog Ensemble (AnEn) machine learning approach produces a calibrated ensemble forecast.  The StatCast techniques are currently undergoing significant research and development to advance the state of the science for combining regime classification with supervised machine learning techniques.  The StatCast-Solar methodology is currently comparing regime-classification using k-means clustering to find statistical regimes representing cloud types and then artificial neural networks compared to implicit regime separation methods such as regression trees and random forests.  The StatCast-Wind methodology is based on using stability regimes, as classified by the Richardson number, to separate regimes and train artificial neural networks on each regime separately.  A manuscript for the StatCast-Solar is under development and presentations on these machine learning approaches to renewable energy prediction will be given at the 2020 AMS Conference in January.

More information on the KISR project can be found at [Link to https://nar.ucar.edu/2018/ral/renewable-energy]

RAL has advanced the application of machine learning to support wildfire prediction.  Atmospheric conditions, fuel type, and fuel moisture content (FMC) are critical factors controlling the rate of spread and heat release from wildland fires.  Commonly used wildland fire spread models have displaced significant sensitivity to FMC; therefore, having accurate FMC estimates to use as initial conditions is important.  The National Fuel Moisture Database provides sporadically updated information about FMC created by interpolating sparse manual samplings of live FMC and relatively sparse surface observations of dead FMC (by Remote Automated Weather Stations.  At present gridded FMC data set that can be assimilated in real-time in an operational system does not exist.  RAL built a real-time FMC database to use in WRF-FIRE coupled atmosphere wildland fire prediction model, which is a component of the Colorado Fire Prediction System.   The random forest based models predict live and dead FMC that results in more realistic, dynamic representation of fuel heterogeneity and in improved accuracy of wildland fire spread prediction.  This model runs daily and the output is displayed on the Operations tab on Improved Wildfire Spread Prediction.

Figure 3. Real-time predictions of the Dead Fuel Moisture Content updated daily on RAL’s FTP page.
Figure 3. Real-time predictions of the Dead Fuel Moisture Content updated daily on RAL’s FTP page.

These RAL forecast systems also continuing to push the envelope of advanced weather forecasting in the transportation sector. The Maintenance Decision Support System (MDSS) was adapted from its original focus on roadways to be used as a Runway Decision Support System for Denver International Airport (DIA). The system generates tuned weather forecasts and treatment recommendations for the runways at DIA. In addition, DICast® and a weather-tuned version of GRAFS form the backend weather engine used in both the FHWA and Colorado Pikalert Hazard Assessment forecast systems.

FY 2020 PLANS

Areas of development for the next fiscal year include:

  • Extend machine learning techniques to other variables produced by DICast®.
  • Test Regime-Dependent Methodologies of predicting short-term solar and wind power generation as part of the KISR project.
  • Advance the application of machine learning in renewable energy prediction across timescales and climates.
  • Make improvements related to road temperature and precipitation forecasts in the MDSS.
  • Test the application of the Analog Ensemble on DICast® forecasts to produce probabilistic wind and solar power predictions using the Shakke Shuffle technique.
  • Utilize machine learning techniques for improving offshore wind energy modeling.

Statistical Methods in Forecasting

Background

Spatial forecast verification methods were rapidly introduced to address inconsistencies found between a forecaster’s subjective assessment of a forecast versus traditional verification’s assessment of the same forecast, which tended to favor coarser resolution models over the newer high-resolution counterparts.   Subsequently, much effort has been placed in evaluating the utility of these new methods; trying to determine their reliability in terms of both repeatability (if a researcher applies the method several times to the same cases, would they always have the same conclusions?), reproducibility (if different users apply the same verification method, would they have the same conclusions as each other?), as well as to determine if the methods yield sensible information about forecast performance (i.e., do they measure physically meaningful errors, or do they give erroneous information about performance?).  Such work continues with the development of a new set of cases along with their evaluation by several distance-based spatial verification measures; the findings of which have been submitted in the paper Gilleland et al (2019).

 Consider the binary set A, represented by the light blue circle in the center of each panel, which could represent a forecast area where a variable of interest exceeds a certain threshold.  Now consider two other event sets, B and C, where B is a large ring centered at the same spot as A and C is a circle of identical size as A but translated slightly to the right.  Which of B and C makes a better forecast of A?  Different users may have different opinions, but they should choose a verification measure that agrees with their opinion.  For example, the centroid distance favors B (it gives a perfect score, in fact, of zero) whereas for C it will be equal to the translation, in this case, which means it is a worse score than for B.
Figure 1. Consider the binary set A, represented by the light blue circle in the center of each panel, which could represent a forecast area where a variable of interest exceeds a certain threshold.  Now consider two other event sets, B and C, where B is a large ring centered at the same spot as A and C is a circle of identical size as A but translated slightly to the right.  Which of B and C makes a better forecast of A?  Different users may have different opinions, but they should choose a verification measure that agrees with their opinion.  For example, the centroid distance favors B (it gives a perfect score, in fact, of zero) whereas for C it will be equal to the translation, in this case, which means it is a worse score than for B.
 Circle cases overlaid on top of each other.  Each case is a single field, and different comparisons are proposed in Gilleland et al (2019).  For example, comparing 1 with 2, 2 with 3 and 2 with 4 tests each method for their reliability in terms of repeatability; that is, each pair is identical but placed in different parts of the domain and/or north-south instead of east-west orientation.
Figure 2. Circle cases overlaid on top of each other.  Each case is a single field, and different comparisons are proposed in Gilleland et al (2019).  For example, comparing 1 with 2, 2 with 3 and 2 with 4 tests each method for their reliability in terms of repeatability; that is, each pair is identical but placed in different parts of the domain and/or north-south instead of east-west orientation.

An example of one of the cases proposed is shown in Figure 1.  The centroid distance is a mathematical metric (meaning that it satisfies three generally desirable properties of a measure) that informs about the centroids of two fields (or individual features within a field).  The observation, A, is an area where a variable exceeds a certain threshold and B and C represent two different “forecasts” of this area.  The centroid distance favors B, giving it a perfect score, because it has identically the same centroid as A.  Therefore, if the centroid is the most important feature that a user is interested in, then centroid distance is a valuable measure.  On the other hand, if it is more important to get the overall area correct, even if it is displaced slightly in space, then centroid distance may not be ideal; at least in the sense of not being reliable as defined above.

FY2019 Accomplishments

Over fifty new (binary) geometric cases were proposed for testing spatial verification methods, particularly those aimed primarily at location errors, in order to test the reliability of verification measures, and to help determine what properties each measure has, as well as how they might fail.  Several distance-based measures were also applied to these cases, and the results are in the paper Gilleland et al. (2019).  In particular, a common situation in weather forecasting is that nothing is forecast (e.g., no rain anywhere in the domain).  If both fields are empty (zero-valued everywhere), then it should be a perfect forecast.  If one field has just a few non-zero values, then perhaps it is still an excellent forecast.  It turns out that many methods are either undefined for this situation, or when they are defined, they are highly sensitive to the addition of one or more non-zero-valued points; leading to spurious results.  Also, the position in space of these non-zero values can greatly affect several of the measures.

Many of the cases involve simple circles, some of which had previously been used in Gilleland (2017).  These cases each test how methods inform about errors for specific challenging situations and are summarized in Figure 2.  Other cases include ovals that mainly have one or more of three types of errors (size bias, location errors and orientation errors), as well as some cases involving random placement of event areas within different envelopes, and some additional sensitivity cases with noise added to other cases.

Additionally, the MesoVICT project had its final workshop in Vienna, Austria to conclude what had been learned about situations specific to complex terrain.

FY2020 Goals

  • Investigate bootstrap properties under realistic situations for forecast verification measures
  • Submit a paper on bootstrapping for forecast verification

References

Gilleland, E., 2017. A new characterization in the spatial verification framework for false alarms, misses, and overall patterns. Weather Forecast., 32 (1), 187 - 198, doi: 10.1175/WAF-D-16-0134.1.

Gilleland, E., G. Skok, B. G. Brown, B. Casati, M. Dorninger, M. P. Mittermaier, N. Roberts, and L. J. Wilson, 2019. A novel set of verification test fields with application to distance measures. Submitted to Monthly Weather Review on 3 August 2019.

Wildland Fire Modeling and Prediction

BACKGROUND

RAL is developing a useful suite of methods to predict wildfires and their coincident weather. This is a long-term project engaging the expertise of several scientists and engineers across the organization.  Decision makers who deploy resources and strategize effective targets for firefighting need reliable, accurate, frequently updated, readily accessible, geo-referenced, current and predicted weather and fire behavior information. Timely information allows decision makers to better assess current conditions and future trends. Reliable information about the potential for rate of fire spread and extreme fire behaviors is essential to saving lives and property. 

Current operational wildland fire-spread prediction systems are not coupled to numerical weather prediction (NWP) models. These systems often rely on wind fields coarsely resolved in space and time. However, when flows begin rapidly evolving due to storm outflows, density currents, frontal passages, and other weather features, or are spatially variable due to complex-terrain effects, highly resolved wind fields in time and space are essential to accurately predict fire spread rate and direction. Furthermore, large wildfires result in significant surface heat fluxes generating strong updrafts and consequently intensifying local winds, which in turn cause more rapid fire-spread rates. Large wildfires also produce significant smoke plumes that can affect radiative transfer, while lofted particulate matter and moisture can form pyrocumulus clouds. All these phenomena can be predicted using coupled models. Therefore, developing an operational coupled wildland fire-spread capability is essential for accurate wildland fire spread prediction. To achieve this goal, RAL researchers are extending capabilities of the Weather Research and Forecasting (WRF) NWP model, based on the Coupled Atmosphere Wildland Fire Environment (CAWFE) model. In addition to gauging fire perimeter, the modeling system (which runs at a resolution of 100 m on its inner domain) produces predictions of a variety of fire and weather variables, including rate-of-spread, flame length, and smoke. These developments are being incorporated into the community WRF-Fire model.

FY2019 ACCOMPLISHMENTS

During the last year, the wildland fire-prediction system was ported to a cloud-computing platform, which allows multiple simultaneous wildland fire simulations. Cloud-computing provides also greater flexibility and faster model execution due to availability of a range of hardware configuration. During 2019 fire season the cloud-computing instance of the system was extensively tested supporting operational wildland fire simulations. The system performed reliably for both automatic, nowcasting simulation as well as longer range high-resolution simulations. In addition, the wildland fire prediction system is was augmented by a fire spotting likelihood capability. The initial implementation of spotting capability was based on an online version of the Lagrangian particle dispersion model, HYSPLIT, integrated with WRF model. Due to inherent limitations of the online implementation of HYSPLIT in WRF, we have started development of a more flexible, massively parallel Lagrangian particle tracking algorithm.

The performance of the prediction system was assessed using observations from wildland fires in the State of Colorado from 2016 through 2018 fire seasons. The model tends to overpredict the size of most fires. One of the reasons for overprediction is that at present we are not able to account for the fire suppression efforts due to the lack of appropriate data. In previous years we carried sensitivity studies focused on the representation of terrain and fuel moisture content. This year we have also studied the effect of fuel types and related loads on the rate of spread prediction. The analysis indicated that even relatively minor errors in fuel type distribution can result in sizable errors in the rate of spread prediction. This result points to the need for more frequently updated fuel maps.

Because fuel moisture content is one of the significant parameters controlling the spread rate of wildland fires, having accurate estimates of the fuel moisture content are critical to firefighting managers. However, estimates of the fuel moisture content are currently based on spatially sparse observations. Furthermore, live fuel moisture is sampled infrequently because it has to be done manually. To address the need for a higher resolution fuel moisture data and also more frequent live fuel moisture observations, we have developed a real-time system for high-resolution, gridded fuel moisture-content data over conterminous United States (CONUS). Satellite reflectance data from MODIS Terra and Aqua platforms are used together with surface observations in machine-learning models to provide estimates of the dead and live fuel moisture content. We have selected random fores algorithm based machine learning model to produce daily, one-kilometer resolution maps of the dead and live fuel moisture over CONUS. The maps and associated data sets can be accessed through RAL’s web page: https://ral.ucar.edu/projects/improved-wildland-fire-spread-prediction, under the tap “Operations.”

FY2019 PLANS

Throughout next year, extensive evaluation of the system performance will continue based on wildland fires observed in Colorado during fire season 2019. The spot-fire prediction capability will be fully integrated in the wildland fire modeling system and its performance will be assessed using available spotting data. Spotting often causes the rapid rate of wildland fire spread (Jimenez et al. 2018). Projections about the likelihood of spotting can therefore be critical for more effective wildland fire management. The spotting likelihood is computed based on the likelihood of firebrand generation, transport, burnout, and deposition upon fuels that can be ignited. The spot fire capability will be assessed using observations of firebrand transport and deposition obtained using dual pol radar in recent wildland fires in Australia.

REFERENCES

Jimenez, P. A., D. Muñoz-Esparza, and B. Kosović, 2018: A high resolution coupled fire-atmosphere forecasting system to minimize the impacts of wildland fires: Applications to the Chimney Tops II wildland event. Accepted for publication in Atmosphere.

Model Development using Machine Learning

BACKGROUND

RAL is a leader in prediction systems at the intersection of data science and atmospheric science for many years.  A recent research project and key development area has been to integrate machine learning within parameterizations of numerical weather prediction models.

Surface layer parameterizations in numerical weather prediction models provide an interface between the land surface model and the lowest levels of the atmospheric model through the calculation of momentum, sensible heat, and latent heat fluxes. Current surface layer parameterizations are based on Monin-Obukhov (MO) similarity theory, which links the near surface vertical profiles of wind, temperature, and moisture to their relevant fluxes through the use of empirical functions conditioned on the stability of the surface layer. While these empirical functions agree closely with observations under homogeneous conditions, there are many situations in which observed fluxes do not match the estimates from similarity theory. Therefore, the goal of this project is to train a diverse set of machine learning approaches on multi-year time series of surface layer and flux observations.

FY 2019 ACCOMPLISHMENTS

We have acquired the necessary surface layer observations and quality controlled the data from meteorological towers in Cabauw, Netherlands, and Idaho, United States. We have trained random forests and artificial neural networks to predict friction velocity and the temperature and moisture turbulent scale terms. These terms can be used to derive the surface momentum, sensible heat, and latent heat fluxes as well as calculating stability diagnostics. We have evaluated each machine learning model and identify which approaches perform best under different stability regimes and weather conditions. We found that the machine learning approaches generally have lower error and higher correlation coefficient than MO Theory (Figure 1 shows the results for Temperature Scale on the Cabauw dataset and Figure 2 shows the results for the Friction Velocity also on the Cabauw dataset).

Figure 1. Predictions of the temperature scale on the Cabauw dataset for the Random Forest (left), MO theory (center) and Neural Network (right).
Figure 1. Predictions of the temperature scale on the Cabauw dataset for the Random Forest (left), MO theory (center) and Neural Network (right).
Figure 2. Predictions of the friction velocity on the Cabauw dataset for the Random Forest (left), MO theory (center) and Neural Network (right).
Figure 2. Predictions of the friction velocity on the Cabauw dataset for the Random Forest (left), MO theory (center) and Neural Network (right).

To verify the robustness of the models, we tested training the models at one site and applying to the other.  For all predictand variables (friction velocity, temperature scale and moisture scale), the machine learning models generally outperformed the MO similarity theory.

The best performing models are then evaluated within the WRF single column model to check for any potential biases created during the numerical model integration process.  These machine learning based methods are compared to the empirical method of surface layer parameterizations in WRF model.  The first step in the process was to save scikit-learn decision trees from random forest to csv files and read them into Fortran as an array of decision tree derived types.  Within the random forest surface layer parameterization, the process calculates the derived input variables for ML models, feeds vectors of inputs to random forests for friction velocity, temperature scale, moisture scale and calculates fluxes, exchange coefficients and surface variables.  The current work is testing with WRF Single Column Model on idealized case study using GABLS II constant forcing, YSU Boundary Layer and Slab Land Surface Model.  The initial results are promising with the random forest implementation generally capturing the daily patterns, as shown in Figure 3.

Figure 1. Predictions of the temperature scale on the Cabauw dataset for the Random Forest (left), MO theory (center) and Neural Network (right).
Figure 3. Comparison of the random forest WRF parameterization (left) with the MO theory parameterization (right) in terms of the evolution of moisture scale, temperature scale and friction velocity.

FY 2020 PLANS

Areas of development for the next fiscal year include:

  • Obtain additional datasets for a more robust verification of the machine learning models.
  • Finalize the verification of the random forest surface layer parameterization in Single Column WRF.
  • Implement the neural network surface layer parameterization in WRF.
  • Begin testing the parameterizations over water for enhancing offshore wind energy predictions.
  • Hold a workshop and build online tutorial to advance the usage of machine learning based parameterizations in numerical weather prediction.

National Security Applications

Significantly advance our understanding of mesoscale and urban-scale weather and climate processes, especially in the boundary layer, and our ability to forecast these atmospheric conditions operationally for the purpose of providing forecasters, decision makers, and emergency managers with accurate information to save lives and property.

  • Numerical Weather Prediction and Data Assimilation
  • Tropical Cyclones and Related Extreme Weather
  • Post-Processing
  • Air Quality Forecasting
  • Statistical and Dynamical Mesoscale Climate Downscaling
  • Atmospheric Transport and Dispersion of Hazardous Materials Research and Development
  • Disease-Spread Modeling

Numerical Weather Prediction and Data Assimilation

Over the past several decades, RAL has developed and deployed complex computer-based operational systems for analyzing and forecasting climate and weather at high resolution worldwide. This development is built upon the Laboratory’s deep foundation of applied scientific research and engineering.  Systems and their products are tailored to each project to maximize the benefit to the sponsors and end-users.  For example, improved analyses and forecasts at Army test ranges saves millions of tax dollars by identifying when weather suitable for testing will occur, and improves safety by predicting conditions that would be hazardous to personnel and materiel. Other domestic and international projects include forecasting for wind farms at resolutions that can approximate large eddies within the atmosphere’s boundary layer; new, innovative ways to supply models with current observations from radar ; and exploring how urban development affects the weather, and how that weather in turn affects the health of people living in urban areas.

  • Four-Dimensional Weather System (4DWX)
  • Real-Time Four-Dimensional Data Assimilation (RTFDDA) and Forecasting Advances
  • Fine-Scale Precision NWP: WRF-RTFDDA-LES
  • GPU-Accelerated Microscale Modeling: FastEddy
  • Mesoscale Ensemble Data Assimilation and Prediction


Four-Dimensional Weather System (4DWX)

BACKGROUND

Since the middle 1990s, the U.S. Department of Defense’s (DOD’s) Army Test and Evaluation Command (ATEC), then known as TECOM, has sponsored RAL to conduct research, development, and technology-transfer of the Four-Dimensional Weather (4DWX) system.  4DWX is an advanced numerical weather prediction (NWP) system that analyzes current weather and makes detailed predictions of weather over the next several days across many scales of phenomena.  4DWX’s NWP core is the Weather Research and Forecasting (WRF) Model.  4DWX ingests observations into the NWP core through RAL’s Real-Time Four-Dimensional Data Assimilation (RTFDDA) scheme.  RAL upgrades 4DWX software several times per year.

4DWX is used by ATEC meteorologists and other DOD staff at eight test ranges across five major climate zones: White Sands Missile Range, New Mexico; Electronic Proving Ground, Arizona; Dugway Proving Ground, Utah; Aberdeen Test Center, Maryland; Redstone Test Center, Alabama; Airborne and Special Operations Test Directorate, Fort Bragg, North Carolina; Yuma Proving Ground, Arizona; and Cold Regions Test Center, Alaska.  4DWX is also used at other locations when ATEC meteorologists are required to support temporary exercises in places such as San Nicholas Island, California; Spaceport America, New Mexico; Fort Wingate, New Mexico; Isle of Benbecula, Scotland; Woomera, Australia; Pacific Missile Range Facility, Hawai’i; and Kwajalein Atoll, Marshall Islands.

Thanks to 4DWX, ATEC meteorologists have greater access than ever to technology and expertise that help them produce weather forecasts and analyses at the scales, and with the accuracy and utility, required to support safe and cost-effective testing by the DOD.  For RAL and its collaborators in the university community, one of the most attractive elements of the 4DWX project is that the ATEC test ranges serve as natural laboratories for atmospheric research, complete with dense observing networks and specialized data that permit study of mesoscale and microscale phenomena in complex terrain.  Continual improvements to 4DWX and to community numerical weather prediction models, such as the WRF Model, are made possible through this collaboration with DOD.

PRIMARY 4DWX TECHNOLOGY

Weather Research and Forecasting (WRF) Model

The predictive core of 4DWX is the Advanced Research version of the WRF Model (sometimes abbreviated ARW), a long-established industry standard for NWP in operations and research.   The model code is open source.  It was developed by a group of partners including NCAR, the National Oceanic and Atmospheric Administration, the Air Force Weather Agency, the Federal Aviation Administration, and the university community.  The model is used across many scales, from global to microscale.

Real-Time Four-Dimensional Data Assimilation (RTFDDA)

The project continues to rely on Real-Time Four-Dimensional Data Assimilation (RTFDDA) as one way to ingest observations and define the atmosphere’s current state for 4DWX’s NWP core, the WRF Model.  RTFDDA involves modifications to an NWP model’s predictive equations so the model can be gently adjusted, or nudged, toward observed conditions during the model’s analysis stage, before the forecast stage begins.  The scheme is computationally efficient and preserves the precise timing of observations, which gives 4DWX a particularly accurate depiction of the weather at any instant.  RTFDDA continues to show itself superior to, or the equal of, many alternative methods of data assimilation in operational systems.  RTFDDA assigns quality flags to observations during the analysis and forecast cycling, rather than as a pre-processing step, providing more accurate and stable assessments of each observation’s usefulness in data assimilation.  RTFDDA also has an improved means of dealing with cases when a ground-based observing site’s actual elevation differs significantly from the simulated terrain height in the model, which is a mundane but under-appreciated problem in applied NWP.

Ensemble 4DWX (E-4DWX) 

Figure 1.  Probability density functions (PDFs) of an uncalibrated (solid black line) and a calibrated (solid blue line) 24-h ensemble forecast of 2-m air temperature (°C) from E-4DWX for station 01 at Dugway Proving Ground.  Calibration reduces bias, broadens spread, and increases sharpness in the ensemble forecast when compared with observations, such as 25.9°C (thick red bar) in this case, and when compared to the baseline prediction one could get from the climatological PDF for summer (dashed black line).  From article by Knievel et al. (Weather and Forecasting, 2017).
Figure 1.  Probability density functions (PDFs) of an uncalibrated (solid black line) and a calibrated (solid blue line) 24-h ensemble forecast of 2-m air temperature (°C) from E-4DWX for station 01 at Dugway Proving Ground.  Calibration reduces bias, broadens spread, and increases sharpness in the ensemble forecast when compared with observations, such as 25.9°C (thick red bar) in this case, and when compared to the baseline prediction one could get from the climatological PDF for summer (dashed black line).  From article by Knievel et al. (Weather and Forecasting, 2017). 

Since 2007, Dugway Proving Ground has used an ensemble version of 4DWX (called E-4DWX) developed by RAL.  E-4DWX provides a suite of 30 forecasts valid at the same place and time, each producing slightly different but similarly realistic forecasts.  Differences among ensemble members are induced by varying initial conditions, boundary conditions, and model physics.  All members are based on the WRF Model.  The ensemble captures the forecasts’ probability information that varies with changes in weather regime.  In 2014, E-4DWX was expanded to include three additional ranges in the intermountain West: White Sands Missile Range, Yuma Proving Ground, and Electronic Proving Ground.  E-4DWX products include maps and time series of means, standard deviations, or fractions of the ensemble exceeding thresholds.

A subset of output from E-4DWX is calibrated so that the probability of E-4DWX’s forecasts being realized matches the observed probability (Figure 1).  Benefits of calibration include: 1) reducing forecast error of the ensemble mean, partly by reducing bias; 2) increasing reliability, resolution, and sharpness, including for predicting extreme and potentially devastating weather; and 3) providing a measure of forecast uncertainty through the spread among ensemble members.  Calibration is performed on moments of the overall probability density function, no matter the size of the ensemble membership, as opposed to calibrating output from specific ensemble members.  This makes E-4DWX particularly robust, even if individual members of the ensemble fail at some point during the forecast.  E-4DWX’s calibration algorithms combine logistic regression with quantile regression.  To ensure the ensemble’s reliability, it is pre-processed, then the calibration is explicitly conditioned on the ensemble dispersion.  Regressions are always performed with cross-validation to minimize the likelihood of overfitting.  

Forecasts of severe weather

The 4DWX system has components that predict severe weather on two scales: the next few hours, based on both observations and model predictions blended via the AutoNowcaster (at Redstone Test Center and White Sands Missile Range) and the AutoNowcaster-Lite; and the next few days, based on model predictions alone.  The AutoNowcaster and AutoNowcaster-Lite systems employ the dual polarization data available from the nation’s WSR-88D (i.e., NEXRAD) network as well as Terminal Doppler Weather Radar (TDWR).  An algorithm called Trident alerts forecasters to conditions that could lead to flash flooding.  Trident predictions are at 10-min intervals to a lead time of 1-hour.  The algorithm currently uses a standard Z-R relationship to relate radar reflectivity to precipitation rate. 

Predictions of lightning

At all ranges, 4DWX now includes a tool for tactical prediction of lightning (lead times of minutes to tens of minutes) and a tool for strategic prediction of lightning (lead times of hours to days).  The former is based on WSR-88D radar data, used to monitor reflectivity above the melting level.  The latter is based on numerical output from 4DWX’s predictive core.  Algorithms are calibrated at each range independently, based on summer and winter cases from previous years.

Predictions of flood-inducing rainfall

To warn test ranges about the potential for flood-inducing rainfalls, 4DWX relies on Trident.  Trident uses two methods of calculating warning criteria, one based on maximum rainfall in a drainage basin, another based on the percentage of a basin covered by rainfall of various thresholds.  Those algorithms now include radar-based estimates of rainfall rate calculated from dual-polarization moments.

Analog ensemble 

Figure 2.  Example of the 4DWX display of AnEn predictions of near-surface conditions at station 1 of Dugway Proving Ground.  The top panel shows the mean prediction of 2-m air temperature (°C in dark blue) as a function of valid time (hour, month, and day) within an envelope of ± 1 standard deviation (cyan) about the mean.  The bottom panel shows the same but for 10-m wind speed (m s-1).  Observations at station 1 are in gray on both panels.
Figure 2.  Example of the 4DWX display of AnEn predictions of near-surface conditions at station 1 of Dugway Proving Ground.  The top panel shows the mean prediction of 2-m air temperature (°C in dark blue) as a function of valid time (hour, month, and day) within an envelope of ±1 standard deviation (cyan) about the mean.  The bottom panel shows the same but for 10-m wind speed (m s-1).  Observations at station 1 are in gray on both panels.

4DWX’s Analog Ensemble (AnEn) uses a set of algorithms to calculate probabilistic predictions that rely on archives of observations and model output to collect an ensemble of prior forecasts made under analogous weather patterns (Figure 2).  Predictions from analog-based methods are inherently calibrated, so an extra calibration step is not required.

Coupled applications

Direct weather analyses and predictions from 4DWX and E-4DWX are the core of the weather information used by forecasters at the ATEC ranges, but that information can be made even more valuable when it is supplied to decision support systems (DSSs) that simulate how the weather affects other processes and conditions, such as sound propagation and the transport and dispersion of airborne hazards.  Examples of DSSs that have been linked to 4DWX and/or E-4DWX include:

  • Noise Assessment and Prediction System (NAPS)
  • Second-order Closure Integrated Puff (SCIPUFF) model
  • Lewis Rocket Trajectory Model
  • Open Burn / Open Detonation Model (OBODM)

4DWX Web Portal

The primary interface to the 4DWX system at all ATEC ranges is the 4DWX Portal.  The Portal’s flexibility, accessibility, modularity, and extensibility are ideally suited to the customized weather support that RAL provides to forecasters, who have eagerly welcomed how the Portal improves their workflow.  Weather maps and related graphics from 4DWX include optional color palettes that can be accurately seen by the color-blind.  The Portal’s dashboard has a flexible, configurable layout, with streamlined access to portlets for coupled applications.  The list of output formats that the Portal supports includes the third-party BUFKIT and RAOB applications.

Integrated Data Viewer (IDV)

RAL collaborates with UCAR’s Unidata program to include among 4DWX’s display options the Integrated Data Viewer (IDV), which is a sophisticated, flexible, Java-based application for analyzing and displaying geophysical data.  IDV is the primary means by which range forecasters explore in greater depth the weather analyses and forecasts from 4DWX.  This more complex and flexible exploration complements the “virtual map wall” that is available through the 4DWX Web Portal, whose purpose is to provide the easiest and quickest interface to a standard suite of pre-generated weather maps.  IDV is also a research tool and is employed by scientists and engineers during their testing, development, and refinement of 4DWX.

OUTREACH AND TRAINING

Each year, RAL provides to ATEC meteorologists at each test range several days of on-site training on 4DWX technology.  Not only does 4DWX improve every year, but the test support required of ATEC meteorologists also changes frequently.  Moreover, turnover among ATEC forecasters also points to the need for a regular training cycle, independent of how rapidly 4DWX technology changes.  Close interaction between ATEC and RAL is critical for maintaining the project’s success.

SELECTED ACCOMPLISHMENTS IN FY2019

4DWX build and install on two clusters at DOD Supercomputing Resource Centers (DSRCs)

After redesigning key elements of 4DWX to be more platform-independent, modifying data feeds, and improving the scope and sophistication of 4DWX’s system monitoring, in FY2018 RAL began running 4DWX operationally at the DSRCs.  In FY2019, this effort was expanded to include fully automated builds and turn-key installations of the latest version of 4DWX at the DSRCs.

Moved 4DWX code repository to Git

Modernization of the 4DWX base code continued in FY2019, with the move to Git.  This allows for greater coordination of software updates and easier methods of deploying new software to the ATEC ranges and DSRCs.

Analog Ensemble (AnEn)

A full analysis of Cold Air Damming (CAD) events was conducted for Aberdeen Test Center.  Classic CAD cases have been identified and are being used to develop a prognostic algorithm to use 4DWX output to forecast the likelihood of CAD.

FastEddy

FastEddy is a new hybrid CPU/GPU-accelerated, large-eddy simulation (LES) model developed by RAL.  The model comprises resident-GPU code, so all prognostic calculations are carried out in an accelerated manner on GPUs, with CPUs used only for model configuration and input/output.  In FY2019, RAL began testing FastEddy for predicting the statistical properties of turbulence and other microscale phenomena at specially chosen test sites at the ATEC ranges.

Integration of new GOES satellite data into 4DWX

New datasets, including addition infrared channels, the Geostationary Lightning Mapper (GLM) and high temporal and special resolution products, are now available with the new GOES satellites.  These products have been integrated into 4DWX.

Display of 4DWX-predicted weather impacts

RAL has developed a new tool that displays the impacts of predicted weather conditions on tests at the ATEC ranges.  This tool allows the ATEC meteorologists to quickly assess weather threats and when safety and test operations may be impacted by unfavorable weather conditions. 

Testing predictions of wet-bulb globe temperature

Wet-bulb globe temperature (WBGT) is a key index of heat stress commonly used in the military and in the sports community to assess instances when heat, humidity, lack of wind, and intense sunshine combine to make working outside unsafe.  WBGT is an empirical quantity, but RAL has developed test algorithms to diagnose an approximate WBGT from 4DWX forecasts.  RAL evaluated several formulae for the algorithm, which will soon be applied in the form of a predictive ensemble for improving the safety of DOD’s outdoor test exercises.

SELECTED KEY PLANS FOR FY2020

Port E-4DWX to DSRCs

Following the success of 4DWX’s transition to the supercomputers at DSRC’s, RAL will begin to port E-4DWX to the same systems.  Because the available nodes at DSRC’s far exceed what has been available on ATEC’s dedicated clusters, porting the ensemble offers the promise of extending ensemble forecasting to every supported ATC range, beyond just the four that currently use the system.

FastEddy

In FY 2020, RAL will expand FastEddy to include moist atmospheric processes.  More accurate representations of turbulence and fine-scale meteorological phenomena are expected with the addition of these processes.  In FY2020 and beyond, RAL will design and implement post-processed products and images from FastEddy for operational use by ATEC meteorologists.

Improved displays of 4DWX model output

RAL will continue to improve the WRF output products available from 4DWX.  Engineers will develop a new animation tool that utilizes layering to overlay multiple variables and allows the user to easily query model output for each grid point.

Data assimilation

In FY2020 and beyond, RAL will continue to develop and optimize a scheme for using RTFDDA to assimilate lightning data.

Radar-based icing algorithm (RadIA)

RAL will adapt the RadIA algorithm for use in 4DWX.  This algorithm uses moments from dual polarization radar data to represent when and where in-flight icing hazards are occurring. 

Prediction of turbulence for unmanned aerial systems (UAS)

RAL will continue adapt the Laboratory’s Graphical Turbulence Guidance (GTG) for use at the test ranges to predict turbulence at altitudes and over areas relevant to UAS, which are the focus of substantial DOD testing.  Data sets from UAS tests over Dugway Proving Ground are now being used to adapt GTG.

Prediction of cloud cover and cloud ceiling

Accurate cloud cover and cloud ceiling predictions are needed for aviation-related testing that occurs at ATEC ranges, including UAS missions and air drops of cargo and personnel.  RAL will integrate relative humidity and microphysics predictions from WRF to develop cloud prediction products for 4DWX.

Wildfires

RAL continues to explore the potential of adding to 4DWX the physics module WRF-Fire for use at selected test ranges, such as White Sands Missile Range.  WRF-Fire simulates two-way coupling between wildfires and their environment, so the model can predict a fire’s spread, severity, smoke, and other characteristics. 

Observing and modeling the Chesapeake Bay breeze

A tool will be added to the 4DWX Portal that displays observations and model predictions of the Chesapeake Bay breeze.  ATC’s weather is often influenced by the breeze, so testing there will likely be improved by having explicit predictions of the breeze’s onset, duration, and extension inland.

Range climatographies

RAL will resume our previously paused effort to develop a full-grid climatography over each test range, based on 4DWX final analyses spanning a length of time to be determined. To accompany the climatographies, RAL is developing a method to extract and display data for particular locations, seasons, and times of day, and to display the data as 2- dimensional (2-D) isosurfaces.

Incorporating the second-generation Advanced Weather Interactive Processing System (AWIPS-II) into 4DWX

RAL will continue to work with ATEC meteorologists to determine how best to incorporate AWIPS-II into their workflow, and to define the modifications that must be made to 4DWX so it can be used most effectively in combination with AWIPS-II.

Real-Time Four-Dimensional Data Assimilation (RTFDDA) and Forecasting Advances

BACKGROUND

RTFDDA is a mesoscale numerical weather modeling technology that has been developed to meet critical weather needs, such as military tests and operations, renewable energy assessment and prediction, power grid weather safety, and weather-related emergency response.  RTFDDA is an extension of the WRF (Weather Research and Forecasting) Model and is designed to effectively and efficiently assimilate available weather observations into WRF and to provide rapidly updated precision weather information for local areas of interests. An important feature of RTFDDA is that it allows for smooth and uninterrupted assimilation of diverse weather observations and produces physically consistent and dynamically balanced 4D weather analyses and “cloud/precipitation “spun-up” predictions. In the past 19 years, RTFDDA has been applied to over 50 weather-critical applications across the US and internationally.  

TECHNOLOGIES 

Figure 1. RTFDDA and its extension for regional microclimatology dynamical downscaling, ensemble prediction and LES/VLES NWP of microscale flows.
Figure 1. RTFDDA and its extension for regional microclimatology dynamical downscaling, ensemble prediction and LES/VLES NWP of microscale flows.

RTFDDA has been continuously improved with respect to its data assimilation schemes, new data sources, dynamical configuration and physical parameterizations to advance the RTFDDA system itself and to improve its accuracy and capabilities. In the last 10 years, RTFDDA has evolved from a single mesoscale deterministic analysis and forecasting system to a modeling suite that integrates ensemble prediction technology (Ensemble-RTFDDA), regional climate downscaling with four-dimensional data assimilation (Climate-FDDA) and microscale NWP with a refined LES model grid at 10s to 100s meters of grid intervals (RTFDDA-LES) (Fig. 1). As a result, RTFDDA is now capable of producing customized high-resolution and ultra-high-resolution precision weather analyses and forecasts, probabilistic weather forecasts, and multi-year/multi-decadal microclimatology analyses for a given target region. In addition, the WRF-Chem model has been added to RTFDDA for forecasting air quality, sand storms and dust storms.     

RTFDDA integrates the following data assimilation technologies: Newtonian relaxation based “observation-nudging” and “analysis-nudging” FDDA schemes, the NCAR community WRFDA, GSI, DART-EnKF data assimilation modules, a four-dimensional relaxation ensemble Kalman filter (4D-REKF) FDDA scheme and a hydrometeor-latent-heat-nudging (HLHN) radar data assimilation schemes. The technologies are configured to formulate hybrid data assimilation to provide optimal modeling according to the application requirements.  

RTFDDA has been deployed for real-time operational weather services for over 50 weather-critical applications by US government agencies and international organizations over the US and other global regions, providing valuable decision-supporting information in the areas of national defense and security, energy, emergency response and health.  

FY 2019 ACCOMPLISHMENTS 

Radar Data Assimilation  

Figure 2. Comparison of observed and simulated radar reflectivity by the RTFDDA assimilation of a squall line with and without RDA.
Figure 2. Comparison of observed and simulated radar reflectivity by the RTFDDA assimilation of a squall line with and without RDA.

Weather radars detect detailed hydrometeor information inside convective systems. Radar data assimilation (RDA) has been an important advancement to improve mesoscale numerical predictions of precipitation systems and the accompanying severe weather. A significant effort has been put into refining the RTFDDA-RDA scheme. Studies have been carried out to assess the impact of two key experimental parameters, the time windows and weights and the nudging coefficients, in the hydrometeor and latent heat nudging (HLHN) scheme, which is the core engine of RTFDDA RDA. This research greatly improved our understanding of the complex data-ingestion processes of the RDA innovations and led to a new design of different latent heat controls for stratiform and convective clouds. This work significantly improves the RTFDDA-RDA performance for 0 – 6 hr forecasts of moist convection. Fig.2 compares a case study with and without RDA with radar observations. 

Lightning Data Assimilation

Another major advance with RTFDDA is the development of a lightning data assimilation (LDA) capability. Total lightning strikes are highly correlated with the total graupel mass in convective clouds. An algorithm was developed to reconstruct graupel amount (specific mixing ratios) for three-dimensional model grid points based on total lightning observations. The reconstructed graupel was then assimilated into WRF based on the RTFDDA HLHN scheme and on an EnKF-based time-lagged ensemble approach. Case studies of severe convection in the central plains of the US and a southern region in China demonstrated that RTFDDA-LDA is capable of significantly improving 0–3 hr lightning and precipitation forecasts of convective storms. This work has been published in the Journal of Geophysics Research: Atmosphere (Wang et al. 2019). 

Sand and dust forecasting with WRF-Chem

Dust forecast capabilities have been developed with RTFDDA fully coupled with WRF-Chem. RTFDDA assimilation of weather data is able to provide accurate weather environment for modeling dust emission and transport in WRF-Chem, improving the simulation of dust spin-up processes and thus the forecast accuracy. RTFDDA systems for forecasting dust have been deployed in the Middle East, including in Saudi Arabia and Israel.

US Army Test and Evaluation Commands (ATEC)

RTFDDA serves eight Army test ranges located in the US and also supports on-demand test missions of ATEC in other regions of the globe. More detail on specific advances made in ATEC modeling systems can be found at the 4DWX section of this report.

WRF-RTFDDA for State Power Investment of China (SPIC) Wind-Power Prediction

 

Figure 3. An example of the Google Earth display of the wind speed prediction of the SPIC E-RTFDDA system for Wind Turbine# YQ0100. The horizonal axis spans from November 21, 2018 to November 28, 2018 and the panels are corresponding to different forecast ranges.
Figure 3. An example of the Google Earth display of the wind speed prediction of the SPIC E-RTFDDA system for Wind Turbine #YQ0100. The horizonal axis spans from November 21, 2018 to November 28, 2018, and the panels are corresponding to different forecast ranges.

This is an ongoing collaboration with the Renewable Energy Branch of the State Power Investment of China. RTFDDA and its ensemble modeling technology are being applied for wind prediction at three large wind farms in the middle of China. The wind farms are located in two regions featuring complex terrain, including steep mountains and river corridors. Up to three-day forecasts of general wind evolution and rapidly updated 0–6 h forecasts of wind ramps are required for all wind farms. To meet these goals, a high-resolution (with 1-km grids) RTFDDA system was designed to run hourly forecasting cycles for predicting wind ramps, and a 3-km grid, 30-member ensemble RTFDDA system was developed to produce 0–72 h forecasts at three-hour forecast cycles. Scientific research foci of this project include a) performing assimilation that maximizes the impact of the high-density surface automatic weather station (AWS) network, hub-height wind measurements of wind turbines at wind farms, and wind farm meteorology tower weather observations for wind prediction; b) developing machine-learning post-processing tools based on the NCAR analog ensemble (ANEN) and quantile-regression (QR) ensemble calibration to improve the accuracy of the model forecast of the turbine hub-height wind speed; and c) studying the WRF model’s dynamics and physics to improve its boundary layer flow simulations over complex terrain. Fig. 3 shows a sample comparison of the model surface wind forecasts with observations from the automatic weather stations.

RTFDDA High-resolution Reanalysis and Nowcasting for Shenzhen, China

Figure 4. Realtime forecast of Typhoon Hato with the RUPPS modeling system (3D volume-display of radar reflectivity >= 20 dBZ). Two insets are observed radar reflectivity and a snapshot of video cam in the city at the similar time.
Figure 4. Realtime forecast of Typhoon Hato with the RUPPS modeling system (3D volume-display of radar reflectivity >= 20 dBZ). The two insets are observed radar reflectivity and a snapshot of video cam in the city at the similar time. 

Shenzhen is a major city located in the Pearl River Delta in southern China; the municipality, which includes both urban and rural areas, spans 2,050 square kilometers. In the last six years NCAR has been collaborating with the Meteorological Bureau of Shenzhen Municipality (SZMB) to develop urban-scale precision climate reanalysis, real-time weather analysis, and short-term weather prediction based on RTFDDA. The research goal is to effectively integrate a high-density observation network with advanced remote sensing instruments, including ultra-dense surface Automatic Weather Stations (AWS), wind profilers, radiometers, tall environmental towers, Doppler radars, the Global Positioning System (GPS), lightning, and other platforms, into the RTFDDA system to provide continuous weather analysis and forecasts, and to generate a ten-year microclimatology for the Shenzhen area. The modeling system was configured with four nested domains with horizontal grid sizes at 27 km, 9 km, 3 km and 1 km, respectively. The 1-km domain covers Shenzhen municipality, Hong Kong, and the neighboring area. The main accomplishments in FY2019 include: 1) upgrading the real-time operational rapidly updated urban-scale precision prediction system (RUPPS) at the SZMB HPC center with radar data assimilation (RDA) and lightning data assimilation (LDA). The system runs hourly analysis and forecast cycles, producing 24-h forecasts at 1-km grid spacing every hour; 2) evaluating the newly installed dual-polarization Doppler radar data, and developing a new scheme that more accurately retrieves the hydrometeor classification and water contents for data assimilation; 3) conducting numerical experiments for future observation system design; and 4) working with the SZMB scientists and engineers to produce the a ten-year climate-FDDA reanalysis and public climate service products. Fig. 4 shows a snapshot of RUPPS model prediction of Typhoon Hato.

PLANS FOR FY 2020

Research and development will be carried out to further improve the RTFDDA key components. In particular, new versions of the WRF model and the nudging-based FDDA scheme will be merged and integrated for new deployments. All operational RTFDDA systems will be upgraded to WRF Version 4.0. A plan has been set up to develop an RTFDDA framework based on the MPAS model.      

The RTFDDA data assimilation scheme will be enhanced by integrating the community advances in GSI and DART; improving its own “nudging”, HLHN, and EnKF-based radar and lightning data assimilation; and expanding the four-dimensional relaxation ensemble Kalman filter (4D-REKF) four-dimensional data assimilation scheme. 4D-REKF is an advanced FDDA capability that combines the advantages of Newtonian relaxation-based “observation nudging” and the advanced ensemble Kalman filter. 4D-REKF using flow-dependent data assimilation weights generated with dynamical ensemble forecasts and historical forecast analogs will also be studied. For radar and lightning data assimilation, GSI and 4D-REKF data assimilation schemes will be assessed and compared for assimilation of Doppler radar radial velocities and polarimetric radar products. In addition, algorithms for jointly assimilating polarimetric radar data and lightning measurements will be studied. 

Research on RTFDDA-GSI-HLHN hybrid data assimilation will be focused on enhancement of radar data assimilation (RDA) and lightning data assimilation (LDA) in 2019. GSI will be assessed and tuned for assimilating Doppler radar radial velocities. Strategies for nudging hydrometeors (rain, snow, and graupel mixing ratios) and the corresponding latent heat derivation from radar reflectivity and lightning observations will be studied. The RTFDDA-GSI-HLHN technology has been proposed for developing a real-time operational weather forecasting system for Indonesia, with a 3-km grid covering major islands and water bodies of the country, and two 1-km grids concentrated on oil and gas fields.

 

 

Fine-Scale Precision NWP: WRF-RTFDDA-LES

BACKGROUND

Fig. 1. Simultaneous multi-scale WRF-RTFDDA-LES simulations with six-nested-grid domains with model grid intervals varying from 33m to 8.1km.
Fig. 1. Simultaneous multi-scale WRF-RTFDDA-LES simulations with six-nested-grid domains with model grid intervals varying from 33m to 8.1km.

Demands on precision weather forecasts for weather-sensitive applications are rapidly increasing. To meet these rising needs, RAL developed numerical weather prediction technologies with sub-kilometer grid very-varge-eddy simulations (VLES) and large-eddy simulations (LES) on grids with intervals of 10s of meters. The VLES/LES NWP model is built around the NCAR Weather Research and Forecasting (WRF) model using real-time four-dimensional data assimilation (RTFDDA). The technology allows the VLES and LES models to be directly nested inside a parent mesoscale model, rendering simultaneous multi-scale simulations with full physics. The RTFDDA-V/LES system has been proven to provide valuable results for boundary-layer weather simulations in regions with complex terrain and areas with sharp transitional land use types, such as coastal regions with water-land contrasts and urban environments. The V/LES model also can improve the simulation of severe convective systems. Fig. 1 shows an example of RTFDDA-V/LES model configuration with six nested domains for the US Army Dugway Proving Ground.  

FY2019 ACCOMPLISHMENTS

Research on WRF-RTFDDA-LES in 2019 continued, with evaluations of the real-time modeling systems for the US Army Dugway Proving Ground (DPG) in Utah, and Aberdeen Test Center (ATC) in Maryland. A WRF-RTFDDA-LES model has also been set up to simulate fine-scale weather flows and mountain convection over the White Sand Missile Range in New Mexico.

32 UTC, May 4, 2012 with convective boundary layer) at 33m grid intervals. The field shown is the model vertical velocity at 200m Above Ground Level (m/s).
Figure 2. Snapshots of WRF-RTFDDA-LES simulation of early morning (left panel; valid at 11:00 UTC, May 4, 2012 with stable boundary layer) and around noon (right; valid at 17:32 UTC, May 4, 2012 with convective boundary layer) at 33-m grid interval. The field shown is the vertical velocity (m/s) at 200 m AGL..

RAL is studying the impact of grid resolution on the multi-scale flow interactions at Granite Mountain at DPG. RTFDDA-LES was configured for four nested domains, with grid intervals of 8.1, 2.7, 0.9 and 0.3 km, respectively (Fig. 1). The system assimilates all available observations, including the dense network of observations at DPG. Verification of the real-time analyses and forecasts shows the benefits of the ultra-high-resolution NWP system in resolving realistic sub-mesoscale flows; it also exposes artificially amplified turbulence over broad spatiotemporal scales. Modeling studies were conducted with six nested domains (two extra nested domains with grid sizes of 100 m and 33 m, respectively [Fig. 1]) to study microscale flows associated with the Granite Mountain (~60 km2). The modeling results show increasing ability of the VLES and LES model over the mesoscale model in resolving the fine-scale flow features, especially wind ramps (e.g. Fig. 2). 

Two approaches are being developed to constrain the artificially amplified turbulence in the VLES model. One approach is to add a TKE-based boundary-layer scheme on top of the LES sub-grid-filter, and the other is to adjust the sub-grid-filter mixing parameters. Both approaches mitigate, but do not remove, the artificially amplified turbulence. For the end user’s benefit, a wavelet-based scale separation strategy is being developed to post-process the VLES meteograms and remove the artificially amplified turbulences.


 

Figure 3. A snapshot view of RTFDDA-VLES simulation of summer monsoon convection initiation over mountain ranges around the army White Sam Missile Range.
Figure 3. A snapshot view of RTFDDA-VLES simulation of summer monsoon convection over mountain ranges around White Sands Missile Range. 

RTFDDA-VLES/LES has been employed to study the orographic convection over the mountain ranges surrounding White Sands Missile Range (WSMR) as well. Simulation experiments with seven cases show that with 300-m and 100-m grids, RTFDDA-VLES was able to forecast the initiation of moist convection over the complex mountain ranges.  

FY2020 PLANS

The improved ability of WRF-RTFDDA-V/LES is encouraging for microscale weather forecasting, but many challenges remain before it can be deployed for real-time operational forecasting. First, each V/LES NWP system should be formulated to address the specific needs of an application, and tools should be developed to improve the use and visualization of VLES forecast output for end users. Second, V/LES forecast verification and special observations that characterize microscale weather information should be explored to help understand when V/LES NWP is valuable and when it is not. This will help modelers to improve the modeling technology and to provide product guidance to end users. Lastly, data assimilation is critical for real-time operational forecasting with V/LES NWP models, and the assimilation schemes should be adapted for the V/LES-scale models.

In 2020, modeling studies with RTFDDA-V/LES will be carried out to understand small and microscale severe weather processes including strong winds, icing, thunderstorms, and extreme temperatures over small-scale complex terrain. RTFDDA-V/LES will be further assessed for modeling the high-impact local weather phenomena at different army test ranges and prepared for real-time operations at ATC, DPG, and WSMR in the next two years.

GPU-Accelerated Microscale Modeling: FastEddy

BACKGROUND

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Figure 1: These two animations are taken from different time periods within the same simulation where surface skin temperature was prescribed to evolve from a higher temperature (convective cell regime on the top), to lower temperature with weaker thermal forcing (convective roll regime on the bottom).

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Figure 2: FastEddy™ limited area domain simulation with the cell perturbation method for resolved turbulence instigation (top) versus a periodic domain reference simulation (bottom) versus. This feature allow FastEddy™ to be applied real-world locations for specific times and dates. The longer-term goal is to provide synchronization of FastEddy™ simulations as nested-domains driven by WRF mesoscale forecast simulations (e.g. High Resolution Rapid Refresh, HRRR forecasts).

 

Figure 3: FastEddy™ has been extended to allow for multiple GPU execution, alleviating the limited memory constraints on domain size for single GPU simulations. Here, the foreground image shows a single domain of size 22.5 km x 54 km consisting of ~10 million gridpoints run on a single GPU. The background image shows the results of utilizing 16 GPUs under horizontal domain decomposition via MPI, to model a domain 16 times larger (90 km x 216 km, 160 million gridpoints) at the same, sub-100m resolution.

 

Figure 4: Contours of velocity components: v = streamwise, u = spanwise, w = vertical, at a horizontal plane at z = 8 m. Flow direction is southerly. Southern boundary uses the cell perturbation to generate resolved turbulence. Downtown Oklahoma City, targeting one of the itensive observational periods during the Joint Urban 2003 field campaign..

The overarching goal of this effort is to design, develop, implement, validate, and promulgate a disruptive capability in the numerical modeling of complex microscale flows utilizing advanced computing architectures. To date, the application of the large-eddy simulation (LES) technique has been restricted to fundamental research due to the substantial computational expense of the method. Nonetheless, the efficacy of this method in capturing the influence of turbulence across a plethora of application scenarios only continues to grow. Our mission is to develop an LES modeling system targeting general-purpose-graphics-processing-unit (GPGPU) architectures in order to achieve at least order-of-magnitude performance gains. Such performance gains are the crucial requirement for realization of the LES method as a viable tool for microscale operational, educational, and more comprehensive research applications.

FastEddy™ is a new hybrid CPU/GPU-accelerated, LES model developed within RAL-NSAP beginning in FY2017. Applications of this model target turbulence-resolving microscale atmospheric boundary layer flow simulation with atmospheric transport and dispersion of hazardous species and greenhouse gases.  FastEddy™ is a resident-GPU model, meaning that all prognostic calculations are carried out in an accelerated manner on the GPU with CPU utilization strictly limited to model configuration and input/output of modeling results.  This resident-GPU approach shows tremendous early potential for achieving faster-than-real-time microscale simulations across domains of order 100-1000 km2 at a resolution of O(10m).

FY2019 Accomplishments

  • Momentum stress, turbulence closure, and surface layer parameterization (Monin-Obhukov) were implemented.
  • Demonstrated ~3x faster than real-time execution in fully-compressible mode for domain extents of 80 km2 at resolution of 30 m for canonical stability regimes on a single NVIDIA GP100 GPU.
  • Implemented building-resolving capabilities and carry out corresponding verification/validation based on a wind tunnel experiment and full atmospheric scale on the Joint Urban 2003 at Oklahoma City.
  • Further optimization to the multi-GPU implementation.
  • Implementation of the cell-perturbation method to generate resolved turbulence from a smooth mesoscale lateral-boundary-conditions forcing.
  • Implementation of two weighted essentially non-oscillatory (WENO) advection schemes of third- and fifth-order accuracy.
  • Code restructuring to have a modularized framework for the hydrodynamics solver.

FY2020 Plans

  • WRF-to FastEddy™-coupling for combined mesoscale and microscale modeling in one system utilizing the cell perturbation method for resolved turbulence instigation at the nested boundaries of LES domains.
  • Implementation of turbulence closure based on a subgrid-scale turbulence-kinetic-energy transport equation.
  • Implementation of a canopy model.
  • Enhance the FastEddy dynamical core to incorporate moist dynamics and microphysics.
  • Incorporate capability to model chemistry processes.
  • Publish several papers on the initial dynamical core formulation and subsequent capabilities.

Mesoscale Ensemble Data Assimilation and Prediction

BACKGROUND

 A description of E-RTFDDA framework
Figure 1: A description of E-RTFDDA framework

Given the chaotic nature of the atmosphere and the imperfections of numerical weather prediction (NWP) models, probabilistic forecasts are imperative for mesoscale weather forecasting. In the last 10 years, RAL developed an Ensemble Real-Time Four-Dimensional Data Assimilation (E-RTFDDA) and forecasting system. The system is built upon WRF. The first E-RTFDDA system was deployed to support US Army Dugway Proving Ground in 2007, known as E-4DWX. Since then the WRF core, data assimilation scheme, ensemble perturbation approaches, and ensemble output post-processing have been continuously improved. The second system was developed in 2010 to support Xcel Energy for real-time wind energy prediction. Recently, in working with the Chinese Electric Power Research Institute (CEPRI), the third system, a 30-member 9km-grid E-RTFDDA model that covers China, has been developed. 

TECHNOLOGIES

Unlike most other mesoscale ensemble systems, E-RTFDDA is a multi–model, multi–scale, and rapidly cycling data-assimilation-and-prediction system with multiple perturbation approaches. The continuous cycling mechanism of E-RTFDDA allows it to produce accurate nowcasts and short-term forecasts that are highly desirable for many weather-critical applications. One of the unique advantages of the E-RTFDDA system is that it is capable of integrating and dynamically downscaling the top-ranked global model forecasts, including the forecast data from ECMWF/IFS (Europe), NCEP/GFS (USA), EC/GEM (Canada), NASA/GEOS (USA), JMA/GSM(Japan), and CMA/GRAPES (China), to the same mesoscale application grids, and thus effectively takes in the information and uncertainties of these global models and produces more accurate mesoscale probability forecasts. Figure 1 presents a high-level description of the E-RTFDDA technology. 

Another E-RTFDDA core component is its innovative ensemble data-assimilation algorithm, four-dimensional relaxation ensemble Kalman filter (4D-REKF). In the ensemble modeling, 4D-REKF permits flow-dependent data weighting, which improves the simpler Cressman-type “observation-nudging” FDDA. 4D-REKF computes Kalman gains using the multi-model E-RTFDDA forecasts, which are ingested into E-RTFDDA models to replace the simple distance-dependent observation weighting functions in the original nudging model. A Local Ensemble Kalman Filter (LEKF) is employed to take account of multiple observations. 4D-REKF retains and leverages the advantages of both traditional Newtonian-relaxation and Ensemble Kalman Filter data assimilation schemes. It eliminates the shortcoming of empirical specification of spatial weighting functions in the current station-nudging FDDA formulation. Furthermore, it extends the traditional (intermittent) EnKF data assimilation method to a 4D continuous data assimilation paradigm that greatly mitigates the dynamic shocks associated with the intermittent EnKF processes. 4D-REKF reduces the critical dependency on the background error covariance inflation required by the traditional EnKF and permits effective assimilation of all observations that may be available at irregular locations and times.  Figure 2 describes the general formulation of the 4D-REKF FDDA scheme. 4D-REKF enhances both the accuracy of model initial conditions and also the initial condition perturbation approach, and thus improves the overall capabilities of E-RTFDDA ensemble prediction. 

 A description of Obs-nudging and 4D-REKF FDDA formulations.
Figure 2: A description of the observation-nudging and 4D-REKF FDDA formulations.

The research and development of E-RTFDDA is currently conducted under the sponsorship of the US Army Test and Evaluation Command, the State Grid Corporation of China (SGCC), Xcel Energy, the State Power Investment of China (SPIC), and Inner Mongolia Electric Power Corporation (IMEPC) for supporting military tests, electric-power-grid weather safety, and wind-power forecasts, respectively. 

FY2019 ACCOMPLISHMENTS

The E-RTFDDA WRF model has been upgraded to the newest community WRF release Version 3.9.1 and 4.0. The upgrades involved evaluating and adopting the community WRF advances, implementation of an analog-based bias-correction algorithm and a quantile-regression-based probability-calibration scheme for statistical post-processing. The Army E-4DWX system was redesigned to have a 3-km domain for four test ranges and one week of test runs were completed.  An E-RTFDDA system was used to support the power grid operation of the State Grid Corporation of China (SGCC) and wind power forecasts for three wind farms by the State Power Investment of China (SPIC). The SGCC E-RTFDDA system contains 30 WRF members and its domain cover the whole China region at grid intervals of 9 km. The system runs four forecast cycles per day, and each cycle produces 72-hour forecasts.  For the SPIC wind power forecasting, a 30-member 3-km E-RTFDDA system is developed for the SPIC wind farms over a region with complex terrain. The system runs 3-hour cycles, producing 93-hour forecasts.  

Besides continuous R&D of the existing operational ensemble system, a new ensemble system has been developed for the Inner Mongolia Electric Power Corporation (IMEPC) for the purpose of forecasting wind power in a very-large-scale wind power production region. Figure 3 shows the domain configuration of the modeling system. The wind farm clusters are modeled with two 2.7-km domains. The system contains 45 ensemble members.

Figure 3. The domain configuration of the modeling system
Figure 3. The domain configuration of the modeling system

PLANS FOR FY2020

The WRF stochastic kinematic energy backscattering (SKEB) scheme for dynamical ensemble perturbation and DART-EnKF for WRF initial condition perturbation strategies will be further evaluated and integrated for real-time E-RTFDDA operation.

4D-REKF is still in its early stages of initial operating capability. Further evaluation and enhancement are necessary to fully exploit the power of the technology.  The limited representativeness of Kalman gains computed from mesoscale ensemble forecasts of excessively small number of ensemble members is still the main challenge for effectively taking the advantage of 4D-REKF.  Empirical algorithms will be explored to address the fact that ensemble mesoscale forecasts often lead to formation of sporadic, unrepresentative local structures in the Kalman gains, which introduces noise and lessens the effectiveness of data assimilation. Refinement to 4D-REKF with cross-variable (covariance) “observation-nudging” capabilities for assimilating Doppler radar radial velocities should be studied. Strategies for nudging hydrometeors (rain, snow, and graupel mixing ratios) and radar reflectivity and lightning observations for cloud-resolvable ensemble forecast of convective systems will be studied. Finally, 4D-REKF for assimilating wind farm data to improve wind-power forecasts will be conducted in 2020.

 

Tropical Cyclones and Related Extreme Weather

Weather phenomena that are extreme from a meteorological point of view can also be extreme in how they affect society.  Among the examples that come readily to mind are hurricanes and other tropical cyclones, which are subjects of several projects in RAL.

Background

Tropical cyclones (TCs) present numerous societal and environmental risks to coastal and marine regions around the world.  Significant progress in understanding and predicting TCs has been achieved in recent decades because of advancements in fundamental science and engineering, high-resolution numerical weather prediction (NWP) tools, and computers. Yet despite such advancements the prediction of a TC’s genesis, intensity, and overall wind field still can be quite inaccurate, especially in cities and suburbs in a storm’s path.  One reason for inaccuracy is that TCs are nonlinear and multi-scale.  The large-scale environment surrounding a storm; its inner-core dynamics, convective processes, microphysics, and turbulence; and its interactions with the ocean, land, and build-up structures shape the storm’s evolution, especially its wind near the ground, which challenges NWP models in numerous ways.

RAL conducts scientific research on a wide variety of TC problems in the realms of theory, observations, models, and operational forecast applications.  In the last year, RAL has conducted research in the following areas:

  • Study of TC wind impacts on urban and built-up areas for landfalling TCs through the use of high-resolution numerical models, large-eddy simulations, and analysis of laboratory and observational data
  • Real-time implementation of NWP model post-processing techniques to improve forecasts of TC track, intensity, and wind
  • Statistical and machine learning methods for predicting TC intensity change


Accomplishments in FY2019

Multi-scale modeling of extreme winds in the urban canopy

The project team added to Cloud Model 1 (CM1) new code for representing vertical faces, which is required for modeling wind in the urban canopy of coastal cities.  The approach, called the internal boundary method (IBM), is based on work by Briscolini and Santangelo (1989).  The project team is working with the Center for Severe Weather Research to validate the IBM in CM1 with a dataset of observations from mobile Doppler radar that captured high-velocity wind around and over buildings on a barrier island as Hurricane Frances (2004) made landfall at Fort Pierce, FL.  We are also working with Forrest Masters at the NSF/NHERI wind tunnel facility at the University of Florida to model flows around and over simple arrangements of city buildings to generate another source of validation for CM1.  In addition, NSAP modified the Yonsei University (YSU) atmospheric boundary-layer (ABL) parameterization scheme to work with the building effects parameterization (BEP) and building environment model (BEM) in the Weather Research and Forecasting (WRF) Model (Hendricks et al 2019a).  It is now possible to include the YSU scheme for idealized and real-case hurricane simulations, an important step forward for the larger research community.  Before moving on to simulating with the new YSU scheme the high winds of hurricanes, the project team validated the modified YSU scheme against observations from Houston during the passage of a cold front (Hendricks et al 2019b).

Real-Time Implementation of the Analog Ensemble for Tropical Cyclone Intensity Change

Using NOAA’s Jet supercomputer, we conducted a second year of real-time testing with an analog ensemble (AnEn) tailored for TC prediction (e.g., Alessandrini et al. 2018) for both the Atlantic and Eastern Pacific hurricane seasons.  We specifically focused on an AnEn designed to improve the prediction of the Hurricane Weather Research and Forecasting (HWRF) model’s of intensity change with the specific goal of improving the prediction of rapid intensification (RI).  RI is a challenge for NWP models since RI is defined as the 95th percentile of intensity change over a given time period.  Earlier in 2019, we also submitted a manuscript demonstrating the AnEn’s real-time performance in the 2018 hurricane season (Lewis et al. 2020).  Figure 1 shows an example of the forecast skill of the HWRF-based AnEn from the 2018 Atlantic and Eastern Pacific hurricane seasons.

Real-Time Implementation of the Analog Ensemble for Tropical Cyclone Intensity Change
Figure 1. A homogeneous comparison of Brier skill scores (BSS) for RI forecasts generated by the deterministic HWRF, operational “DTOPS” probabilistic model, the operational “SHIPS-RII” model, and the AnEn for the RI defined over 24-, 48-, and 72-h lead times for the 2018 hurricane seasons in the Atlantic (left) and Eastern Pacific (right).

Statistical and machine learning methods for predicting TC intensity change

In FY2019, we also developed and evaluated statistical and machine-learning-based methods for TC intensity-change prediction.  So far, all methods have been based on HWRF output and have been dedicated to the improvement of TC intensity and RI prediction.  Both a simple logistic-regression technique and deep-learning-based feed-forward neural network (Cloud et al. 2019) have been derived to use one-dimensional predictors derived from HWRF forecast fields.  These predictors describe properties of the large-scale conditions impacting the TC and also the inner-core aspects of a TC according to the forecast model.  Both methods have shown promising skill in the prediction of RI.  In addition, we have developed a first-generation convolutional neural network that uses the entire forecast fields from HWRF.  Efforts are underway to improve its predictive skill and ability to interpret important physical processes and error tendencies in the HWRF model.

Plans for FY2020

Operational scale simulations of historical TCs in the US and Asia

Using knowledge gained from high-resolution simulations with CM1, RAL and collaborators will use the WRF Model to simulate several historical hurricanes, including Wilma (2005) and Irma (2017) in the US, Faxai (2019) in Japan, and Hato (2017) in China.  These simulations will enable us to compare methods of approximating the aggregate effects of city buildings before and after the WRF Model has been improved to produce better results when winds are hurricane strength.  In addition, idealized simulations with the WRF Model will be used to systematically explore the effects of storm speed, angle of approach, and land surface properties on the surface wind field in the urban canopy.

NWP Post-processing methods

In the realm of post-processing, FY2020 will include the submission of a manuscript highlighting the AnEn use in predicting TC track and wind structure.  We will also continue to communicate with our NOAA sponsor the results of real-time AnEn testing for the possibility of operational implementation.  In addition, work will continue in advancing machine learning methods for TC intensity prediction, which includes producing a skillful and interpretable convolutional neural network and testing this algorithm in a real-time environment for the 2020 hurricane season.  Finally, we are developing an experimental version of the AnEn for the HWRF that uses a convolutional neural network to recognize analogs in the two-dimensional forecast fields.

References

Alessandrini, S., L. Delle Monache, C. M. Rozoff and W. E. Lewis, 2018: Probabilistic prediction of tropical cyclone intensity with an analog ensemble. Mon. Wea. Rev., 146, 1723-1744.

Briscolini, M., and P. Santangelo, 1989: Development of the mask method for incompressible unsteady flows. J. Comput. Phys., 84, 57–75, doi:10.1016/0021-9991(89)90181-2.

Cloud, K. A., B. J. Reich, C. M. Rozoff, S. Alessandrini, W. E. Lewis, and L. Delle Monache, 2019: A feed forward neural network based on model output statistics for short-term hurricane intensity prediction. Wea. Forecasting, 34, 985-997.

Hendricks, E. A., J. C. Knievel, and Y. Wang, 2019a: Addition of multiple-layer urban canopy models to a nonlocal planetary boundary layer parameterization and evaluation in ideal and real scenarios.  J. Appl. Meteor. Climatol., submitted.

Hendricks, E. A., J. C. Knievel, and Y. Wang, 2019b: Evaluation of a hierarchy of urban canopy parameterizations in mesoscale model simulations of the passage of a cold front in Houston.  J. Appl. Meteor. Climatol., submitted.

Lewis, W. E., C. Rozoff, S. Alessandrini, and L. Delle Monache, 2020: Performance of the HWRF Rapid Intensification Analog Ensemble (HWRF RI-AnEn) during the 2017 and 2018 HFIP Real-Time Demonstrations. Wea. Forecasting, in revision.

Post-Processing

Post-processing the output from numerical weather prediction (NWP) models is a highly effective approach for improving models’ analyses and predictions.  This approach is often less time-consuming and less technically challenging than making more fundamental improvements to numerical methods, physical parameterizations, and other elements of NWP.  In particular, post-processing is critical for maximizing the utility of predictions from dynamical ensembles.

  • Analog-Based Methods
  • Quantile Regression

Analog-Based Methods

FY2019 Accomplishments

The Analog Ensemble (AnEn)

The analog ensemble has been applied with success to the following applications:

  • Generating 0-240 hour wind and solar power probabilistic forecasts for a site in Kuwait. The AnEn has been coupled with the Shaacke Shuffle technique to generate reliable probabilistic forecasts of aggregated wind+solar power.
Figure 1. Example of AnEn (a, c, e) and AnEn+SS (b, d, f) ensemble forecast for total wind power (a, b), total solar power (c, d) and total wind+solar power (e, f). The shaded area indicates the 10-90th and the 25-75th quantile range and the solid black line the verifying observations
Figure 1. Example of AnEn (a, c, e) and AnEn+SS (b, d, f) ensemble forecast for total wind power (a, b), total solar power (c, d) and total wind+solar power (e, f). The shaded area indicates the 10-90th and the 25-75th quantile range and the solid black line the verifying observations
  • The bias correction technique for rare events has been described in a paper (Alessandrini et al. 2019) and has been included in the official version of the code distributed through the Github platform. In Figure 2, a comparison between AnEn and AnEn with the bias correction is presented in terms of bias as a function of the predicted wind speed.
Figure 2. AnEn (analog ensemble in its original formulation) and AnEnBc (Analog Ensemble with the bias correction algorithm) ensemble mean bias as a function of the wind speed from Global Environmental Multiscale (GEM) model averaged over equally populated bins. 1-year training period and the shorter 9-month training period are used for both AnEn (analog ensemble in its original formulation) and AnEnBc. The error bars indicate the 95% bootstrap confidence intervals.
Figure 2. AnEn (analog ensemble in its original formulation) and AnEnBc (Analog Ensemble with the bias correction algorithm) ensemble-mean bias as a function of the wind speed from Global Environmental Multiscale (GEM) model averaged over equally populated bins. The 1-year training period and the shorter 9-month training period are used for both AnEn (analog ensemble in its original formulation) and AnEnBc. The error bars indicate the 95% bootstrap confidence intervals.

FY2020 PLANS

In FY20 the potential of the analog ensemble technique will be further explored for several applications: 1) improving WRF-CHEM operational air-quality predictions over New Delhi (India); 2) consolidating real-time operational forecasting of tropical cyclones rapid intensification; 3) exploring the use of AnEn to post-process predictions from neural networks or convolutional neural networks systems for tropical cyclone intensity predictions; and 4) improving air-quality predictions during wild fires over the US.  

REFERENCES

Alessandrini, S.; Sperati, S.; Delle Monache, L. Improving the Analog Ensemble Wind Speed Forecasts for Rare Events. Mon. Weather Rev. 2019, 147, 2677–2692.

Alessandrini, S., Delle Monache, L., Rozoff, C.M. and Lewis, W.E., 2018. Probabilistic Prediction of Tropical Cyclone Intensity with an Analog Ensemble. Monthly Weather Review, 146(6), pp.1723-1744

Clark, M.; Gangopadhyay, S.; Hay, L.; Rajagopalan, B.; Wilby, R. The Schaake shuffle: A method for reconstructing space-time variability in forecasted precipitation and temperature fields. J. Hydrometeorol. 2004.

Sperati, S., Alessandrini, S. and Delle Monache, L., 2017. Gridded probabilistic weather forecasts with an analog ensemble. Quarterly Journal of the Royal Meteorological Society, 143(708), pp.2874-2885.

Quantile Regression

BACKGROUND 

Figure 1: Quantile regression applied to dewpoint temperature at one station at the Army Test and Evaluation Command at the Dugway Testing Range in Utah, providing a probabilistic range that the dew point may fall within at a lead-time of 42-hr.
Figure 1: Quantile regression applied to dewpoint temperature at one station at the US Army Dugway Proving Ground in Utah, providing a probabilistic range that the dewpoint may fall within at a lead-time of 42-hr.

Post-processing ensemble forecasts is generally necessary to provide meaningful probabilistic guidance to users.  One approach that has been used for a variety of applications is quantile regression (QR). RAL scientists are applying a novel statistical correction approach by combining QR with other post-processing approaches (e.g. analog, logistic regression) to calibrate at the specific probability intervals required by the user. Some of the benefits of this approach are that no assumptions are required in the form of the forecast probability distribution function to attain optimality; the resultant forecast skill is no worse than a forecast of either climatology or persistence; and the generated ensembles have dispersive properties directly related to the uncertainty in the forecast, as one would expect.

QR is also a powerful approach for combining different forecast model outputs to generate one coherent and reliable probability distribution function of what the future weather will be. RAL scientists have merged medium-range ensemble rainfall forecasts from eight global weather centers (Brazil, Canada, China, ECMWF, England, France, Japan, US) to calibrate and enhance the accuracy of rainfall forecasts over both East Africa and the Indian subcontinent, gaining 2 to 3 days in additional forecasting skill in the process.

 

 Web display screenshot of our rainfall forecast visualizations provided to Sudan – individual and multi-model rainfall forecasts come from models from eight global forecasting centers.
Figure 2: Web display screenshot of our rainfall forecast visualizations provided to Sudan.  Individual and multi-model rainfall forecasts come from models from eight global forecasting centers.

ACCOMPLISHMENTS IN FY2019

  • The prediction of river water level across the Brahmaputra and Ganges catchments both in India and Bangladesh was expanded utilizing QR with a combination of river width measurements and hydrologic multi-model forecasting
  • A combined QR-analogue approach has been further refined in the application of post-processing of numerical weather forecasts for the US Army Test and Evaluation Command at the Aberdeen Test Center in Maryland
  • QR was also applied to blend ensemble rainfall forecasts from eight weather forecasting centers for the Indian Bihar State to optimize rainfall prediction ensembles that feed into hydrologic models for the Kosi and Bagmati river basins
  • QR was utilized to provide ensemble rainfall forecasts at 1-day to 6-week time scales for basins in Ethiopia as well as for the Blue Nile above Sudan, utilizing ensemble rainfall data from a variety of global circulation models and geophysical regressors
  • A training was carried out on QR and other techniques in Khartoum, Sudan for hydrologists and water engineers working on hydrologic forecasts, so they can apply these techniques locally

PLANS FOR FY2020

  • Work within new regions and states in India and Ethiopia to apply QR-based rainfall forecasting technology for basins within these countries to help improve their rainfall and river flood forecasting capacity, as well as general technical skill development

Air Quality Forecasting

Improving 48-h predictions of fine particulate matter (PM2.5) over the US

In an effort funded by NASA, NCAR and its partners have developed a new capability to produce 48-hour detailed forecasts of ground-level ozone and fine particulate matter. The new forecasting capability combines satellite and in-situ observations with state-of-the-art air-quality modeling. It is generating more detailed, probabilistic air-quality forecasts compared to the current forecasts, which provide just a single-value prediction and do not specify the uncertainty associated with the prediction. Just as a weather forecast, for example, might warn of an 80% chance of rain in the afternoon, new air quality forecasts may warn of an 80% chance of high ozone levels during certain times of the day, while the current forecasts only tell whether ozone will be high or low. Such detailed forecasts can significantly enhance decision-making in air-quality management. The system has been set up over the US but can be easily applied to any part of the world.

The first objective of the ongoing project is to improve the initialization of the National Oceanic and Atmospheric Administration (NOAA) / National Centers for Environmental Prediction (NCEP) operational air-quality system, which is based on the Community Multiscale Air Quality (CMAQ) model, through chemical data assimilation of satellite retrieval products with the Community Gridpoint Statistical Interpolation (GSI) system. We use GSI to assimilate retrievals of aerosol optical depth from the NASA Aqua/Terra Moderate Resolution Imaging Spectroradiometer (MODIS) satellite instruments to improve predictions of particulate matter of aerodynamic matter of less than 2.5 µm (PM2.5) over the US. The assimilation of MODIS AOD in CMAQ improves the model’s ability to simulate day to day variability, i.e., the correlation coefficient by ~67% and reduces the mean bias by ~38% (Figure 1).

 Top left panel shows EPA PM2.5 monitoring sites used for evaluation of CMAQ simulated PM2.5 mass concentrations. The comparisons of the observed and CMAQ simulated diurnal and daily variability of PM2.5 averaged over all the sites during 15 July to 14 August 2014 for three CMAQ experiments are shown in the top right and bottom panels, respectively. Standard deviation in the average observed values range from 4.8 to 11.9 µg/m3, and those in CMAQ average value range from 2.7 to 7.5 µg/m3. BKG represents the CMAQ experiment without assimilation and ASSIM represents CMAQ experiment with AOD assimilation.
Figure 1: Top left panel shows EPA PM2.5 monitoring sites used for evaluation of CMAQ-simulated PM2.5 mass concentrations. The comparisons of the observed and CMAQ-simulated diurnal and daily variability of PM2.5 averaged over all the sites during 15 July to 14 August 2014 for three CMAQ experiments are shown in the top right and bottom panels, respectively. Standard deviation in the average observed values range from 4.8 to 11.9 µg/m3, and those in CMAQ’s average value range from 2.7 to 7.5 µg/m3. BKG represents the CMAQ experiment without assimilation and ASSIM represents CMAQ experiment with AOD assimilation.

The second objective is to improve CMAQ’s deterministic predictions and to reliably quantify their uncertainty with analog-based post-processing methods applied to the deterministic predictions. The AnEn technique (Delle Monache et al. 2013) has been extensively tested for the probabilistic prediction of both meteorological variables and renewable energy (Alessandrini et al. 2015). The AnEn is built from a historical set of deterministic predictions and observations of the quantity to be predicted. For each forecast lead time and location, the ensemble prediction of a given variable is constituted by a set of measurements from the past (i.e., 1-hour averages of O3 concentrations). These measurements are those concurrent to past deterministic predictions for the same lead time and location, chosen based on their similarity to the current forecast. The forecast variables used to identify the past forecast similar to the current one are called analog predictors. In this application we use as predictors wind speed, wind direction, 2-m temperature, cloud cover, and PM2.5 and O3 concentrations forecasts over the continental US generated with CMAQ. The AnEn has been successful tested for the deterministic and probabilistic prediction of both PM2.5 and O3. The Analog-predictor weights are obtained independently at each station by an optimization algorithm which minimizes the continuous ranked probability score (CRPS) over May 2015.

In Figure 2 the AnEn O3 concentration forecasts are plotted through quantile ranges together with the observations and the CMAQ predictions. In this example, the AnEn mean can correct the CMAQ forecast, especially during the night when the O3 concentration decreases.

 Root Mean Squared Error (RMSE) and BIAS as a function of forecast lead time for the AnEn mean (red) and CMAQ (black) forecasts of O3 concentrations over all the available stations for the period of June 2015-September 2015. The vertical bars indicate the 5-95% boot strap confidence intervals.
Figure 2: Root mean squared error (RMSE) and bias as a function of forecast lead time for the AnEn mean (red) and CMAQ (black) forecasts of O3 concentrations over all the available stations for the period of June 2015-September 2015. The vertical bars indicate the 5-95% boot strap confidence intervals.

In Figure 2, we compared the CMAQ and the AnEn mean forecasts. The AnEn mean consistently improves the CMAQ forecast by significantly decreasing both the RMSE and the bias.  We have also looked at several attributes of probabilistic predictions, and similarly to other applications, the analog ensemble is statistically consistent, and it is able to reliably quantify the uncertainty of the prediction.

The third objective is the extrapolation of deterministic and probabilistic point-based predictions to a two-dimensional grid over the US with a Barnes-type iterative objective analysis scheme. Figure 3 shows an example of this technique, which is currently considered for operational implementation as part of the NOAA operational air quality prediction system.

 CMAQ Ozone gridded forecast data on the left and corresponding observed data on the right for August 29, 2017, 22 UTC.
Figure 3: CMAQ Ozone gridded forecast data on the left and corresponding observed data on the right for August 29, 2017, 22 UTC.

The proposed effort is led by NCAR, in collaboration with NOAA, CU Boulder, and the University of Maryland. Currently, NOAA/NCEP is running operationally in real-time the deterministic analog-based correction for the prediction of ground level ozone and surface PM2.5.

Air quality forecasting system for Delhi

NCAR and the Indian Institute for Tropical Meteorology (IITM), an autonomous institution of the Ministry of Earth Sciences of India, have jointly developed an air-quality forecasting system to enhance the decision-making activity in the area of air quality. The forecasting system synergistically integrates MODIS AOD retrievals and in situ measurements of fine (PM2.5) and coarse (PM10) particulate matter from 48 stations in the National Capital Region (NCR) of India with the WRF-Chem modeling system to improve forecasts of surface PM2.5 in Delhi. The system was tested for the October-November of 2016 and 2017 (Figure 4). The assimilation of MODIS AOD significantly improved the performance of WRF-Chem model in simulating PM2.5 mass concentrations in Delhi.

This air quality forecasting system has been providing operational air quality forecasts to the public and the decision-makers since October 2018. A website has been developed by the IITM to disseminate the forecasts, their near-real time verification, and warning bulletin and messages. The website can be accessed here: https://ews.tropmet.res.in/

 Verification of averaged 72 h WRF-Chem forecasted PM2.5 with (ASM) and without (BKG) assimilation of MODIS AOD against PM2.5 observations performed by the Central Pollution Control Board (CPCB) of India. The shades areas represent standard deviation in the average values.
Figure 4:Verification of averaged 72-h WRF-Chem forecasted PM2.5 with (ASM) and without (BKG) assimilation of MODIS AOD against PM2.5 observations performed by the Central Pollution Control Board (CPCB) of India. The shaded areas represent standard deviation in the average values.

A Quasi-operational air quality forecasting system for the CONUS

The Atmospheric Chemistry Observations and Modeling (ACOM) laboratory and the Research Application Laboratory (RAL) of NCAR have jointly developed a quasi-operational air quality forecasting system for the conterminous United States (CONUS). The system is designed to support field campaigns and research groups across the US such as NASA’s Tropospheric Ozone Lidar Network (TOLNET), to offer additional information to air-quality forecasters across the nation, to extend NCAR’s current global atmospheric chemistry prediction capability, and to provide a long-term model output for use in research projects and health studies. These forecast products are available via the following website: https://www.acom.ucar.edu/firex-aq/forecast.shtml

 An example of the hourly evaluation of WRF-Chem predicted surface ozone against the EPA AirNOW measurements.
Figure 5: An example of the hourly evaluation of WRF-Chem predicted surface ozone against EPA AirNOW measurements.

A near real-time evaluation system has also been set-up for the CONUS air quality forecasts. The evaluation system downloads surface ozone and PM2.5 observations from the EPA AirNOW monitoring network and evaluates WRF-Chem forecasts every hour. An example of hourly evaluation of WRF-Chem forecast is shown in Figure 5. Customized plots meeting the requirements of the TOLNET team are also created and posted on the website. In addition to hourly evaluation, monthly evaluation of first and second days of forecasts is also performed and provided on the website. We also provide our forecasts in Google Earth files to support the flight planning during the field campaigns.

 October) in 2014. The dark green label represent contribution from rest of India.
Figure 6: Top 5 contributors (as percentages) to CO and BC levels over Delhi during the winter monsoon (wmn: November to March), transition period in spring (tps: April-May), summer monsoon (smn: June-September), and transition period in autumn (tpa: October) in 2014. The dark green label represent contribution from rest of India.

Quantifying Inter-state transport of air pollutants in India

Quantifying transboundary transport of air pollution is an important component of state-level air quality management because air quality in a state depends on emissions within that state and upwind states. In an effort funded by the World Resource Institute (WRI), NCAR has employed a computationally efficient tagged-tracer approach in the Weather Research and Forecasting model coupled with Chemistry (WRF-Chem) to quantify interstate transport of air pollution in India during 2014. Carbon monoxide (CO) and black carbon (BC) are used as the tracers because they represent air pollutants with lifetimes ranging from about a week to more than a month. WRF-Chem simulations are evaluated against ground-based and space-borne retrievals of aerosols and trace gases. The results suggest that a potentially important role for interstate governance in managing India’s air quality. States in the Indo-Gangetic Plain (IGP) exhibit a strong linear relationship between the direct emissions and near-surface CO and BC mass concentrations, indicating that local emissions dominate in determining surface CO and BC in these states, but interstate transport contributes 43-48% and 38-46% of CO and BC, respectively, in the IGP. The linear relationship starts decreasing as we move to other parts of India, indicating that interstate transport starts to dominate as we move away from the IGP. The contribution of interstate transport to surface CO and BC mass concentrations is estimated to be 20-82% in different states and seasons. Figure 6 demonstrates how interstate transport affects CO and BC during different seasons in Delhi. These results could be used to curb local emissions and create joint mitigation plans among states to improve the air quality in India.

2019 Accomplishments:

  • Transition of the Delhi air quality forecasting system to the Indian Institute for Tropical Meteorology
  • Addition of surface PM2.5 and PM10 assimilation in the Delhi air quality forecasting system
  • Development of CONUS air-quality forecasting system in collaboration with ACOM
  • Development of a tagged tracer technique for quantification of transboundary transport of air pollutants in India

2020 goals: 

  • Develop an analog-based post-processing system for Delhi air quality forecasting system
  • Develop assimilation capability for the CONUS air quality forecasting system. 
  • Develop a chemical reanalysis system for the CONUS

Statistical and Dynamical Mesoscale Climate Downscaling

Among the fundamental, defining characteristics of any dataset is resolution in space and time.  In atmospheric science, the resolution of output from a numerical model or of an observational dataset determines the threshold for what phenomena can be well characterized from the data (one might say depicted, represented, or resolved) and what phenomena cannot.  Downscaling is a method of objectively inferring information at a higher resolution than a dataset might normally provide, given its native spacing of data in space and/or time.

  • Fine-Scale Seasonal Climate Prediction
  • Global Climatological Analysis Toolkit

Fine-Scale Seasonal Climate Prediction

BACKGROUND

Figure 1. The framework for fine-scale seasonal climate prediction
Figure 1. The framework for fine-scale seasonal climate prediction

Global seasonal climate predictions at about 100-200-km resolution issued by national climate centers provide reliable perspectives of the general circulation conditions about one month to six months in advance. Such forecasts, however, lack the fine-scale details that are critical to regional and local climate-sensitive business and decision-makers. To fill that gap, we are developing a fine-scale seasonal-climate prediction capability through dynamical downscaling. A framework for fine-scale seasonal-climate prediction has been set up. In this framework, the global large-scale seasonal forecasts issued by NCEP’s Climate Forecast System version 2 (CFSv2) are applied to force the Weather Research and Forecasting (WRF) model at fine scales. The version of the WRF model we use has been specially customized and configured for climate purpose.  Both deterministic and ensemble predictions can be performed. Techniques from artificial intelligence such as principal component analysis (PCA) and self-organizing map (SOM) analysis are used to extract the relevant climate information.

We are relying on the CFSv2 operational run outputs for this task. For CFSv2, the real-time analysis is carried out by an atmospheric model at T574 (roughly 27-km grid spacing) in the horizontal and 64 sigma-pressure hybrid levels in the vertical. The real-time forecasts are carried out by the same atmospheric model but at T126 horizontal resolution (roughly 100-km grid spacing) and 64 sigma-pressure hybrid vertical levels. The CFSv2 system includes the interactive Noah land surface model with 4 soil levels, the interactive Modular Ocean Model version 3 (MOM3), and the interactive 3-layer GFDL (Geophysical Fluid Dynamics Laboratory) Sea Ice Simulator sea ice model. A global ocean data assimilation system (GODAS) provides the ocean initial conditions for the CFSv2 analysis and forecasts. The real-time CFSv2 outputs include 9-month forecasts (only 7-month forecasts are available) initialized at 00Z, 06Z, 12Z, and 18Z of each day as shown in Figure 2, as well as a single one-season forecast initialized at 00Z and three 45-day forecasts initialized at 06Z, 12Z and 18Z of each day. We will utilize the 7-month forecasts.

Figure 2. Schematic of the operational daily CFSv2 configuration. Only 7-moth data are available daily for downloading from the NCEP NOMADS webpage.
Figure 2. Schematic of the operational daily CFSv2 configuration. Only 7-moth data are available daily for downloading from the NCEP NOMADS webpage.

Accomplishments in FY2019

In August 2018 we started downloading and archiving the 4-times-daily 7-month CFSv2 forecasts. The downloaded files include the surface files and the flux files initialized at 00Z, 06Z, 12Z, and 18Z each day. The downloaded data files are being used for configuring and tuning the seasonal climate prediction system and for analyzing the climate simulations. We can also examine the climatic conditions out to 7 months for the entire period for which data are available.

We evaluated the CFSv2 real-time forecasts focused on the CONUS domain using the analysis fields as truth. The evaluated variables include 2-m temperature and 10-m winds initially. The forecast lead times ranged from one month to two months, from one season to two seasons. Both spatial distributions and temporal evolution were examined. We have done sensitivity experiments with 4, 8, 12, 16, 20, 24, 28, and 32 ensemble members for determining the optimum number for use in sub-seasonal to seasonal climate forecasts. The ensemble members were constructed by using the CFSv2 forecasts for different cycles. We examined the ensemble mean, bias, standard deviation, and signal-to-noise ratio, and determined on the basis of bias, ensemble spread, and computing resources that 16 ensemble members is optimal for our application. Figure 3 shows the construction of the 16 ensemble members based on the 4-times-daily cycles for 4 days (i.e., 4 x 4 = 16). Figure 4 shows the 16-ensemble member mean, bias, standard deviation, and signal-to-noise ratio of 3-month simulated 2-m temperature.

Figure 3. Schematic of the setup of 16 ensemble members for sub-seasonal to seasonal climate forecasts.
Figure 3. Schematic of the setup of 16 ensemble members for sub-seasonal to seasonal climate forecasts.
Figure 4. Ensemble means (a), bias (b), standard deviation (c) and signal-to-noise ratio (d) of 3-month simulated 2-m temperature for 16 ensemble members.
Figure 4. Ensemble mean (a), bias (b), standard deviation (c), and signal-to-noise ratio (d) of 3-month simulated 2-m temperature for 16 ensemble members.

We have set up the experimental fine-scale seasonal climate forecasting system using WRF driven by the CFSv2 forecasts. The WRF domains were configured with a 30 km -> 10 km -> 3.3 km -> 1.1 km nested hierarchy. We have also downloaded and archived the observations from the NCEP Meteorological Assimilation Data Ingest System (MADIS) for the same time period for conducting point-to-point comparisons of 2-m temperature and relatively humidity, 10-m wind speed and direction, precipitation, surface pressure, and PBLH (planetary boundary layer height). Both low-level winds and PBLH are especially important for pollutant dispersion and transport. PBLH could be computed from the atmospheric soundings.

PLANS for FY2020

We are setting up the fine-scale seasonal forecasting system driven by the latest reanalysis from ECMWF (ERA5) as well as CFSR. The purpose of this work is three-fold: 1) evaluating the performance of the ERA5- and CFSR-driven WRF simulations at fine scales, 2) evaluating the CFSv2-driven WRF simulations using the reanalysis-driven data, and 3) exploring the possibility of increasing the ensemble spread by including ensemble members from different modeling systems. Figure 5 shows the ERA5-driven and CFSR-driven WRF 2-m temperature simulations at 1-km horizontal resolution for January 2019. Clearly, ERA5-driven simulations are colder than the CFSR-driven simulations. We will use the MADIS observations to evaluate the simulations.

Figure 5. ERA5-driven (left) and CFSR-driven (right) WRF 2-m temperature simulations at 1-km resolution valid for January 2019. The model domain centered on Utah.
Figure 5. ERA5-driven (left) and CFSR-driven (right) WRF 2-m temperature simulations at 1-km resolution valid for January 2019. The model domain centered on Utah.

We will extensively evaluate the CFSv2-driven WRF simulations at fine scales (e.g., 3.3 km and 1.1 km horizontal resolutions) using the MADIS observations for varying lead times and for various variables. We will compute bias, correlations, and skill scores. We will conduct the SOM analyses and compare with the reanalysis-driven SOM patterns. We will explore the connection between the climate variability (e.g., ENSO, NAO, monsoon) with local weather centered over Utah.

We will explore the application of bias correction to the CFSv2-driven simulations based on our analysis results. Finally, we will set up the operational daily run of sub-seasonal to seasonal forecasts for Utah.

Global Climatological Analysis Toolkit

BACKGROUND

RAL scientists continue to support the DoD’s National Ground Intelligence Center (NGIC) in its mission of assessing the consequences of the transport and dispersion of accidental and intentional releases of hazardous materials into the atmosphere. This is done by providing the agency with access to the RAL-developed GCAT (Global Climate Analysis Toolkit) system. GCAT is a fully automated dynamical downscaling system that allows NGIC scientists to remotely generate a high-resolution 30-year climatography for any region on the Earth. GCAT is based upon Climate Four-Dimensional Data Assimilation (CFDDA) technologies and can run with four domains, reaching a grid increment of 1.1 km. This enables NGIC to conduct transport-and-dispersion analyses at very fine scale.  GCAT has the capability to automatically classify WRF output fields into climatological regimes. The method is based on the self-organizing map (SOM) [1] artificial neural-network pattern-recognition technique. Figure 1 shows the results of a SOM classification, in which 30 months (May 1981-2010) of WRF 1.1-km hourly output was used to estimate the main six regimes of the wind flow over the Energetic Materials Research and Testing Center in Socorro, NM. The six regimes that have been identified are given with their frequency of occurrence and their most representative days, which are chosen based on their Euclidian distance to each SOM node. Weather data valid for the representative days provides better forcing to NGIC’s transport and dispersion climatological studies because the data didn’t undergo averaging, which can destroy important model physical properties (balance etc.) available with dynamical downscaling.

Figure 1 Typical days based on SOM classifications for downscaled historical flow during May over Socorro, NM.
Figure 1 Typical days based on SOM classifications for downscaled historical flow during May over Socorro, NM.

The Second-order Closure Integrated PUFF (SCIPUFF) transport-and-dispersion model is implemented for execution for each dynamical downscaling simulation upon user request. This way, SOMs can be built based on plume dosage, in addition to weather variables, when analyzing the past climate. The system makes use of the Climate Forecast System Reanalysis (CFSR) data set available on a 0.5-degree grid for initial and lateral boundary conditions.

FY2019 ACCOMPLISHMENTS

  • Ensemble intra-seasonal forecasting capability has been tested in the new version running on an HPC. This capability includes downloading Climate Forecast System (CFS) every day for the 6-months-ahead period, which can be used as initial and boundary conditions for WRF high-resolution simulations. The user can select any period between today and 6 months ahead to perform WRF simulations to downscale CFS forecasts in any part of the world.
  • Testing a novel, fast downscaling technique. NCAR has been investigating the possibility of performing downscaling climatological analysis at a high resolution (~0.51 km) using a limited amount of computational resources. High-resolution WRF simulations (~0.51 km) have be performed only for a period shorter than 30 years (12 years) while lower resolution simulations of the parent grid (~3 km) will cover the whole 30-year period. High-resolution simulations are extended to the 30-year period searching for matching analogs on the parent lower-resolution grid. For this purpose, a combination of SOM and analog techniques will be used.

FY2020 PLANS

  • Testing ECMWF-ERA-5 reanalysis. When running GCAT in the “climo” mode, an off-line comparison will be made using ERA-5 (ECMWF) reanalysis fields in addition to the CFSR (NOAA) fields currently in use. As a preliminary test, ERA-5 will be used to run WRF at 1-km resolution for 4 months over Dugway Proving Ground. A comparison with the available observations will assess the WRF performances with respect to using CFSR fields as initial and boundary conditions. A “climo” job will run using both re-analysis (CFSR and ERA5) fields and the performances compared. 
  • Analog Ensemble for downscaling. NCAR will deploy the analog ensemble (AnEn) downscaling tool into the operational GCAT system. In the next period, we will complete this implementation. So far, all scripts have been written in highly portable languages so they can run on many high-performance computing systems (i.e., Fortran, Python, and bash scripts). The remaining steps in this the GCAT deployment will include the following:
  1. Finalize the predictor weighting based on developmental testing described above;
  2. Couple the AnEn scripts with GCAT and offer it as a standard user-selected option;
  3. Test the updated GCAT climo system in a realistic HPC setting, which will include comparing with the 4-grid climo benchmark for accuracy and speed at various grid configuration settings; and
  4. Test the system out for a couple of other domains to examine its performance in other types of topography and climatological conditions.

As a more experimental part of development in the realm of computationally inexpensive downscaling, NCAR will examine as an alternative to the AnEn a machine learning approach to downscaling. In particular, the convolutional neural network (CNN) is well suited for this task since it can be used to identify important spatial patterns from the training data that relate to the patterns seen in the testing period. In other words, instead of conducting a grid-point based downscaling with the AnEn, the CNN would consider the entire domain in generating a downscaled, fine-scale meteorological field from coarse WRF grids. The training dataset (5 years of data for a month of interest) are sufficiently large for machine learning techniques. Moreover, the CNN may be able to provide an alternative to the SOM itself in defining typical days. The CNN has proven to be a powerful classification and diagnostic tool in various geosciences applications, including estimating hurricane intensity from satellite imagery, automatically classifying convective storm modes (e.g., supercell, bow echo, squall line) from radar imagery, and accurately estimating the probability of hail from radar imagery. It is now being used as a post-processing tool for numerical weather prediction as well.

  • Seasonal Forecasting Refinement. NCAR will keep optimizing and will implement the seasonal ensemble forecasting system on the Centennial supercomputer in the next period. We will extensively evaluate the seasonal ensemble forecasts using both the analyses fields and the observations that have been collected. The specific tasks will include:
  1. Implementing the seasonal ensemble forecasting system on Centennial.
  2. Testing with 1.5-month (sub-seasonal), 3-month (seasonal), and 6-month (2-seasonal) forecasting configurations.
  3. Evaluating the sub-seasonal, seasonal, and 2-seasonal ensemble forecasts using the analyses and observations.
  4. Issuing the sub-seasonal, seasonal, and 2-seasonal ensemble forecasts in near real-time.
  5. Testing the typical days' SOM classification based on seasonal forecasting. WRF simulations will use CSF forecast fields as initial and boundary conditions to generate downscaled predictions for a month in the future at different lead times (sub-seasonal, seasonal, 2-seasonal). The results will be compared to those obtained by the “standard” GCAT approach based on reanalysis of the last 30 years. We will focus this study on Dugway Proving Ground, where several weather stations are available.

 

REFERENCES

[1]  Kohonen T (1995) Self-organizing maps. Springer-Verlag, Heidelberg

[2] Jun Yan (2010). som: Self-Organizing Map. R package version 0.3-5. http://CRAN.R-project.org/package=som

[3] Alessandrini S, Hacker J, Vandenberghe F Definition of typical-day dispersion patterns as a consequence of a hazardous release, Proceedings of 34th International Technical Meeting on Air Pollution Modelling, Montpellier, France, May 2015.\

[4] Ferrero, E., Vandenberghe, F., Alessandrini, S. and Mortarini, L., 2016, December. Comparison of WRF PBL Models in Low-Wind Speed Conditions Against Measured Data. In International Technical Meeting on Air Pollution Modelling and its Application (pp. 129-134). Springer, Cham.

Atmospheric Transport and Dispersion of Hazardous Materials Research and Development

For many years RAL has received funding from the Department of Defense and other sponsors to provide expertise, techniques, and technology aimed at making society safer from dangerous gases, liquids, or particles in the air, which might come from a variety of sources.

  • Hazardous Material Source Term Estimation
  • Climatological Dispersion Patterns with Self-Organizing Maps

Hazardous Material Source Term Estimation

BACKGROUND

Figure 1.  Conceptual framework of the VIRSA system.
Figure 1.  Conceptual framework of the VIRSA system.

Atmospheric releases of hazardous materials, either accidental or intentional, continue to pose a threat to United States citizens and to troops abroad and at home.  To counter this threat, RAL is actively supporting research and the development of novel techniques and systems that  can be used to more accurately simulate the atmospheric state and evolution of the released material in both time and space, for planning, real-time response, and forensic purposes.

Hazardous Material Source Term Estimation

In addition to needing a representative description of the atmospheric state (past, present, and future), atmospheric transport-and-dispersion (AT&D) modeling systems also require precise specifications of the material release characteristics (e.g., location, time, and quantity).  For most real-time response scenarios, the specifics of the material release will be unknown, with only ancillary concentration measurements available.

Algorithms and techniques to characterize the source and material are actively being developed in RAL to quickly reconstruct and estimate the source release using these limited sensor observations.  In particular, RAL is actively developing a tailored source-term-estimation (STE) and hazard-refinement system, called the Variational Iterative Refinement STE Algorithm (VIRSA).  VIRSA is a source-term-estimation algorithm that requires minimal input from the user to produce an accurate STE quickly. A Gaussian static plume model is used to calculate a “first guess” source estimate based on available hazardous-material-sensor and meteorological observations.  The adjoint model is then used to iteratively refine the "first guess" source using variational minimization techniques.

A rebuilt version of VIRSA has been tested on data from three different field campaigns: Humble Jasmine 1, Fusion Field Trials 2007 (FFT-07), and Jackrabbit II (Fig. 2). Results have been presented at George Mason University and the International Technical Meeting on Air Pollution Modeling this year. The new system features a streamlined and faster approach that can also run on a standard laptop without a large amount of dependencies. 

Figure 2. Jackrabbit II field trial 6 results. FG is the First Guess, VIRSA is the refined solution, and Real is the actual amount released.
Figure 2. Jackrabbit II field trial 6 results. FG is the First Guess, VIRSA is the refined solution, and Real is the actual amount released.

Specific accomplishments since the last reporting period and plans for next fiscal year are summarized below.

ACCOMPLISHMENTS IN FY2019

  • Verification and validation of VIRSA with Fusion Field Trial 2007 (FFT-07), Jackrabbit II, and Humble Jasmine 1 field campaigns
  • Rebuilt code and workflow using Fortran and R statistical package
  • Improvements to the STE minimization algorithm to improve efficiency and accuracy
  • Additional verification and validation using FFT-07 and Jackrabbit field campaign data
  • Further development of a stand-alone VIRSA implementation, per sponsor and end-user requirements

PLANS FOR FY2020

Refinement of automatic evolution of error covariance parameters

 

Climatological Dispersion Patterns with Self-Organizing Maps

Background

Figure 1.  Conceptual SAHARA workflow, with input of HPAC project files and CFSR atmospheric reanalysis output (top row), into the SOM (middle row), to produce hazardous material dosages for typical days (bottom row).
Figure 1. Conceptual SAHARA workflow, with input of HPAC project files and CFSR atmospheric reanalysis output (top row) into the SOM (middle row) to produce hazardous material dosages for typical days (bottom row).

We have developed a software tool, the SOM-Assisted Hazard Area Risk Analysis (SAHARA), to reduce large climate datasets to more manageable sizes - yet statistically similar - which are then used to produce ensembles of potential hazard outcomes.

Figure 2.  Conceptual SOM workflow for the production of typical days.
Figure 2.  Conceptual SOM workflow for the production of typical days.

The self-organizing map (SOM) is a machine learning / data clustering algorithm that is well-suited for data that have strong topological properties. By employing the SOM algorithm to analyze topological patterns of climatological fields over a regional domain for a 30-year span, we can find a close statistical equivalent with fewer, non-contiguous input days. When using SOMs to cluster monthly climate data in this way, we find that by sampling only 150 days, it reduces computational time by greater than a factor of 6 compared to using the entire climate dataset. 

The SAHARA software can scale from a laptop to workstations to many-core, many-node clusters by using a modern microservice architecture to distribute the Climate Database (CSFR currently), the SOM Engine, atmospheric model ensembles (such as the SCIPUFF Transport and Dispersion model) and pre- and post-processing across available computing resources, either locally or remotely. 

Accomplishments in FY2019

  • “Lowest level” technique implemented to improve accuracy for near-surface events
  • Fulfillment of 28 client requests for SOM typical days.


Plans for FY2020

  • Parameter sensitivity study for chemical and biological (chem/bio) hazards, as the current SOM parameters are optimized for nuclear hazards
  • Transition to the WRA-5 data set
  • Downscaling using WRF for near surface events
  • Transition to a user-friendly interface for DoD community self-service on DoD HPC

 

Disease-Spread Modeling

Over the past several years, RAL has worked closely with the Centers for Disease Control and Prevention (CDC), the National Institutes of Health (NIH), and the Defense Threat Reduction Agency (DTRA) in a broad capacity that has included “boots on the ground” field work; the conduct of workshops on Weather, Climate and Health; and studies that support these agencies to better characterize environmental conditions that are linked to the onset and spread of a wide range of diseases. In 2019, we completed a study for CDC that entailed generating five years of WRF model output at a horizontal grid spacing of 4km over the Democratic Republic of Congo (DRC) and Nigeria, which CDC used to drive their statistical disease-spread models. We are currently jointly developing an updated MOU with CDC that should foster collaborations that could significantly enhance CDC’s understanding of environmental influences on the timing of disease outbreak and spread, including incorporating emerging techniques in artificial intelligence and machine learning.

    • Monkeypox Study


Monkeypox Study

BACKGROUND AND MOTIVATION FOR STUDY

Monkeypox is a viral zoonotic (animal-to-human) disease that can also spread human-to-human, though at a much lesser and unsustainable rate. It normally occurs in parts of central and west Africa near tropical rainforests, and recent outbreaks in Sudan and the United States have fueled new research that focuses on environmental factors that likely contribute to the expanded geographical spread of this disease. Climate change is believed to be a significant driver to the geographical shift in Monkeypox prevalence, either by direct effects on the pathogen resulting from environmental changes of near-surface and soil conditions, or indirectly from the migration of carriers such as rodents seeking more favorable conditions. To further study the environmental influences on the spread of Monkeypox, CDC asked RAL to produce and verify five years (2012-2016) of WRF model simulations over equatorial Africa to obtain near-surface meteorological fields, with which to support their predictive disease modeling efforts.

WRF Simulation Setup

Figure 1 shows the WRF spatial domains, with 12-km grid spacing on the outer domain of 637 x 509 grid points, and a nested 4-km inner domain (used exclusively in all analysis and production) of 910 x 781 grid points. There are 74 vertical levels up to the model top at 20 hPa. The model timestep is 72 s for the 12-km domain and 24 s for the 4-km domain.

Figure 1. WRF domains. “d02” is the 4-km domain.
Figure 1. WRF domains. “d02” is the 4-km domain.

Initial and lateral boundary conditions for the WRF simulations come from the ERA5 Reanalysis, the fifth-generation climate reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF). Output is obtained from the Research Data Archive (RDA) at the National Center for Atmospheric Research (NCAR). ERA5 output is at 31-km grid spacing on a 1280 longitude x 640 latitude N320 Gaussian grid, on 37 vertical pressure levels. ERA5 data is input to WRF every three hours.

WRF simulations are run for 7.5 days, with the first 12 hours discarded to give adequate time for the model to “spin-up.” Output is saved at hourly intervals. To help prevent the model from “drifting” from the observed state, spectral nudging is used. A small correction term is applied to the model solution in the top 12 model levels for geopotential, winds, temperature, and moisture every 6 hours, to “nudge” the model towards the ERA5 solution. Only upper tropospheric and stratospheric levels are nudged, to keep large-scale weather features in line with ERA5, while allowing WRF to generate its own solutions in the lower troposphere.

Model Verification at Boende Site

Initial and lateral boundary conditions for the WRF simulations come from the ERA5 Reanalysis, the fifth-generation climate reanalysis from the European Centre for Medium-Range Weather Forecasts (ECMWF). Output is obtained from the Research Data Archive (RDA) at NCAR. ERA5 output is at 31-km grid spacing on a 1280 longitude x 640 latitude N320 Gaussian grid, on 37 vertical pressure levels. ERA5 data is input to WRF every three hours.

WRF simulations are run for 7.5 days, with the first 12 hours discarded to give adequate time for the model to spin up. Output is saved at hourly intervals. To help prevent the model from drifting from the observed state, spectral nudging is used. A small correction term is applied to the model solution in the top 12 model levels for geopotential, winds, temperature, and moisture every 6 hours, to nudge the model toward the ERA5 solution. Only upper tropospheric and stratospheric levels are nudged, to keep large-scale weather features in line with ERA5, while allowing WRF to generate its own solutions in the lower troposphere.

Model Verification at Boende Site

A comparison was done between weather observations obtained from CDC staff at Boende and output from the nearest WRF grid point. The comparison period used is hourly from 0000 LST 6 September 2015 to 0000 LST 5 September 2016. The observation location is at latitude -0.282206° and longitude 20.883112°, and the nearest WRF grid point is centered at latitude -0.3095 and longitude 20.8604°. The distance between these points is approximately 4 km. Variables used in the comparison include near-surface air temperature, near-surface relative humidity, and rainfall. Wind speed was not used due to quality control issues.

Figure 2 shows daily average plots of temperature and relative humidity, and daily sum plots of precipitation, for the observations and for WRF at Boende throughout the verification period. The bias, RMSE, and correlation for the three variables, calculated across the verification period from hourly values, is shown in Table 1. WRF reproduces the seasonal march of temperature, increasing from the boreal winter dry season through the long-rains season before decreasing again into boreal summer. WRF is overestimating most nighttime low temperatures, typically by 1°C-2°C, but sometimes by more. Some daytime highs are underestimated by about 1°C as well. WRF underestimates daily average relative humidity, although this may reflect the nighttime positive temperature bias, as highest relative humidity values would be expected at night. The source of the larger negative model bias in relative humidity from May-August 2016 is not clear. Climatologically, this is typically a drier period for much of the DRC (e.g., Dezfuli 2017). WRF underestimates the magnitude of rainfall at Boende, more so than underestimating the number of rain days, as there are 147 rain days in the observations and 126 rain days in the WRF output. WRF was missing rain events in May and June that largely accounts for the negative bias.

Sources of the temperature and relative humidity biases may stem from differences in the physical environment around the observation site from that represented by the closest gridpoint in WRF. From Google Earth, the observation location appears to be inside Boende, a town of about 30,000 people. This location has built-up structures around the observation site, and is largely cleared of foliage. In contrast, the grid point from WRF is primarily representing deciduous broadleaf forest, which has a shade fraction of 80%. Effects from not only shading differences, but also differences in emission of longwave radiation from surrounding surfaces and other anthropogenic effects may be in play. Precipitation, and atmospheric moisture in general, is the most difficult atmospheric process to model and observe accurately. The negative bias in WRF may result from inaccurate input conditions, model deficiencies in representing the precipitation generating processes in this area, or local effects. Observational errors do not appear to be an issue, as CMORPH precipitation for the closest grid point over this time period is larger than the observation (2716.86 mm vs. 2238.49 mm). Local effects, and the lack of representation of such effects in the model, may be in play. Both observed and WRF-simulated rainfall can vary substantially over small areas, and a few large events can greatly alter the annual rainfall total. Further validation could be done with comparison between WRF precipitation and those from gridded datasets like CMORPH, however these datasets have their own uncertainties as well.

 

Figure 2. Plots of daily average near-surface temperature (top left), daily average near- surface relative humidity (top right), and daily sum precipitation (bottom left) for the September 2015-September 2016 verification period at Boende. Red line represents observations and blue line represents WRF.
Figure 2. Plots of daily average near-surface temperature (top left), daily average near-surface relative humidity (top right), and daily sum precipitation (bottom left) for the September 2015-September 2016 verification period at Boende. The red line represents observations and the blue line represents WRF.
 
Table 1. WRF bias, RMSE, and correlation with Boende observations for the 2015-2016 verification period.
Table 1. WRF bias, RMSE, and correlation with Boende observations for the 2015-2016 verification period.

 

Numerical Systems Testing and Evaluation

Maintain and expand a central collaborative function within NCAR and a distributive network of collaborators for developing, testing, and validating numerical forecast systems important to operational decision makers and the international research community.

  • Regional Modeling Systems
  • Advanced Verification Techniques and Tools
  • Global Modeling
  • Tropical Cyclone

Regional Modeling Systems

BACKGROUND

Regional modeling activities in the Joint Numerical Testbed (JNT; http://www.ral.ucar.edu/jnt) are focused primarily on the Developmental Testbed Center (DTC; http://www.dtcenter.org) activities, and real-time systems. The DTC is a distributed facility with components in the JNT at NCAR's Research Applications Laboratory (RAL), and the Global Systems Division (GSD) of NOAA's Earth System Research Laboratory (ESRL). It facilitates the transfer of research results into operations by providing the research community with Numerical Weather Prediction (NWP) system components for research. One of the DTC's focal points is regional forecasting systems, with a goal of accelerating the rate at which new technology is infused into operational weather forecasting. The DTC meets its goals by maintaining and supporting community codes that represent the latest NWP technology, performing extensive testing and evaluation of new NWP technology, maintaining a state-of-the-art verification package, and connecting the NWP research and operational communities through its visitor program. In addition to DTC activities, JNT staff have been working to transfer technologies in support of mesoscale weather prediction for the Colombian Civil Aviation Authority, Saudi Arabia weather service (General Authority for Meteorology and Environmental Protection: GAMEP), and sparsely observed regions of the world.

FY2019 ACCOMPLISHMENTS

Community Codes

Community code is a free and shared resource with distributed development and centralized support. The DTC's community code efforts are collaborative activities with developers at NCEP's Environmental Model Center (EMC), NCAR's Mesoscale and Microscale Meteorology (MMM) Division, NOAA/ESRL/GSD, NOAA's Geophysical Fluid Dynamics Laboratory (GFDL), National Aeronautics and Space Administration’s (NASA) Global Modeling and Assimilation Office (GMAO), National Environmental Satellite, Data and Information Service (NESDIS), the University of Rhode Island (URI), and NOAA’s Atlantic Oceanographic and Meteorological Laboratory (AOML) Hurricane Research Division (HRD). During 2019, the DTC worked with the following software packages:

  • Weather Research and Forecasting (WRF; http://wrf-model.org) – NWP model + pre-processors
  • WRF for Hurricanes (http://www.dtcenter.org/HurrWRF/users) – Coupled model capabilities (atmosphere and ocean) in support of tropical cyclone forecasting
  • Ensemble Kalman Filter (EnKF; https://dtcenter.org/EnKF/users/) Data Assimilation System
  • Gridpoint Statistical Interpolation (GSI; http://www.dtcenter.org/com-GSI/users) – Data Assimilation System
  • Unified Post Processor (UPP; http://www.dtcenter.org/upp/users) – NWP model post-processor
  • Model Evaluation Tools (MET; http://www.dtcenter.org/met/users) – Verification package including standard verification techniques, advanced techniques, and tools for use with tropical cyclone verification (MET-TC)

The DTC contributes to the software management and user support for publicly released versions of these systems, which include the latest developments of new capabilities and techniques. Prior to each official release to the user community, the DTC ensures the integrity of all community code software components through a broad range of testing. The DTC also strives for system evolution, in particular through increased interoperability of existing system components, as well as adding new capabilities or techniques. In addition, the DTC provides user support for these packages in the form of Users' Guides, webpages, email helpdesks, and online and on–site tutorials.

As NCEP’s Environmental Modeling Center (EMC) moves toward a unified modeling suite across both spatial (regional and global) and temporal (weather, sub-seasonal and seasonal) scales the DTC has also begun pivoting to the new modeling suite built around GFDL’s Finite-Volume Cubed-Sphere (FV3) dynamical core. While the Unified Forecast System (UFS) has not yet been officially released to the community, the DTC has been working to establish robust code management practices, regression testing, documentation, workflow infrastructure, and support forums to prepare for the upcoming release.

Testing and Evaluation (T&E)

The DTC provides a trusted facility that developers and the operational community can rely on for unbiased assessments of the operational prediction systems and potential new additions to those systems. Testing and evaluation undertaken by the developers of new NWP techniques from the research community are generally focused on case studies. However, in order to adequately assess these new technologies, extensive testing and evaluation must be performed to ensure they are indeed ready for operational consideration. Testing and evaluation by the DTC focuses on either extended retrospective time periods or real–time forecast experiments. These forecasts can be generated by the DTC or provided by external modeling groups. The DTC's evaluations include the use of standard verification techniques, as well as new verification techniques. All verification statistics include a statistical significance (SS) and practical significance (PS) assessment when appropriate.

During 2019, the focus within the regional modeling group was on ensemble systems. In most existing regional ensemble systems, model-related uncertainty is addressed by using multiple dynamic cores, multiple physics suites, or a combination of these two approaches. While these approaches have demonstrated potential, it is time-consuming and costly to maintain such systems, especially in operations. In order to move toward a more sustainable and unified system, stochastic parameter perturbations within the High-resolution Rapid Refresh (HRRR) physics suite are being investigated. Focus has been placed on adding stochasticity into planetary boundary layer (PBL) and land surface model (LSM) processes, along with microphysical processes.

To investigating the stochastic perturbation approach, the DTC partnered with the Hazardous Weather Testbed to address the merits of different approaches to representing model-related uncertainty by conducting an evaluation of a subset of the members of the Community Leveraged Unified Ensemble (CLUE) from the Spring Forecast Experiments (SFEs) held annually. The CLUE evaluation activity is focused on addressing the question of whether there is an advantage to using an ensemble composed of multiple microphysics and PBL schemes over a single physics suite ensemble, using initial and lateral boundary condition and stochastic perturbations. Prior studies of multi-physics, convective-allowing ensemble systems have focused on composite reflectivity, whereas this study is considering multiple verification metrics and methods. This approach demonstrates the need to consider a broader perspective when evaluating the merits of various ensemble approaches before reaching any conclusions as to which approach provides the best overall forecast skill.

Containers

Many times the biggest hurdle when running a new software system is getting it set up and compiled on the intended computer platform. Building complex systems that require a number of external libraries can be a prohibitive hurdle for users to overcome.  In order to reduce some of this difficulty, software containers are being exploited to ship complete software systems to users. The containers have everything that is needed to run a software application, including the necessary operating system components (tools and libraries) and compiled executable (or code and compiler), thus, allowing for the user to quickly produce output without being delayed by technical issues. DTC staff members created GSI, UPP, MET and METViewer (the database and display system for viewing MET statistical output) containers to supplement those containers that had already been established by others in the community (including, WPS, WRF, and NCL) so that an end-to-end NWP system can be fully employed through containers. Along with the software containers, datasets for several cases were bundled in a data container. In 2019, explicit instruction on using the established end-to-end NWP containers on a cloud server was detailed. In addition, DTC staff members coordinated with Metropolitan State University of Denver to teach a forecasting lab that included hands on instruction for running the NWP system on the cloud. By establishing these additional containers, the DTC is assisting the user community (especially students) with efficiently running NWP components and making connections with future collaborators.  To further assist the community, the DTC is offering an AMS short course in January 2020 geared toward raising awareness of these tools for testing and evaluation of NWP innovations.

Real-time modeling systems

JNT staff have participated in technology transfer activities in support of the Colombian Civil Aviation Authority’s weather prediction needs. A prediction system based on the WRF model and GSI has been developed and deployed in collaboration with a private partner called Sutron/Meteostar, which is responsible for providing operational support and visualization. The work has leveraged JNT capability developed under DTC funding. Workflows based on Rocoto provide a stable and modular deployment environment.  The focus in FY2019 has been on implementing the latest, stable version GSI data assimilation system for data assimilation of standard and local surface observations along with satellite fields and to develop a 10-member multi-physics ensemble system in an effort to improve forecast capabilities.  The final operational system was installed at Sutron/Meteostar in December 2018 and training on how to use and maintain the system is planned with Civil Aviation Authority.

The JNT is leading an effort to modernize Saudi Arabian’s weather service (GAMEP: General Authority for Meteorology and the Environment) forecast and display capabilities.  A new regional forecast system based on WRF has been developed with a 3-nest domain design with a high-resolution 2-km inner nest that covers the Saudi Arabian Peninsula.  The forecast system also includes real-time data assimilation of GAMEP’s local observations.  A multi-member ensemble is also being developed that utilizes GFS and ECMWF global models.  A dust-forecast system has also been developed using WRF-Chem.  The modeling team has also developed new forecast projects and a mobile application that interfaces with the forecast products.  An extensive training is being developed for GAMEP staff.

In addition, JNT staff supported the evaluation of observation data quality from a USAID-funded project to develop and deploy low-cost weather instrumentation in sparsely observed regions of the world.  The project uses innovative new technologies such as 3D printers, Raspberry Pi computing systems, and wireless communications to develop a sustainable system that can be built locally in under-developed countries.  The study evaluated data quality of temperature, pressure, humidity, wind, and precipitation observations collected for the NCAR and NOAA testbed sites in Boulder, CO and Sterling, VA. Results indicate the low-cost sensors provide high quality data that could be used for applications for agriculture, water resource monitoring, health, and monitoring of hazardous weather conditions.

FY2020 PLANS

In the coming year, the JNT through the DTC will continue to support various community codes, including NWP systems that will move to a UFS focus, GSI and MET. The DTC will also help organize and support tutorials on the community codes that it supports, as well as on regional models, data assimilation, hurricanes, and forecast verification.  Relevant workshops will be offered to stimulate discussion among the research and operational modeling communities on future directions of development. In addition, efforts will continue related to evaluating deterministic and ensemble-based probabilistic model output. Efforts to further broaden the usage of the NWP containers, both on local machines and on the cloud, will be undertaken.

 

Advanced Verification Techniques and Tools

BACKGROUND

Forecast verification and evaluation activities typically are based on relatively simple metrics that measure the meteorological performance of forecasts and forecasting systems. Metrics such as the Probability of Detection, Root Mean Squared Error, and Equitable Threat Score provide information that is useful for monitoring changes in performance of single aspects of forecast performance with time. However, they generally do not provide information that can be used to improve forecasts, or that can be helpful for making decisions. Moreover, it is possible for high-quality forecasts– such as high-resolution forecasts – to have very poor scores when evaluated using these standard metrics, while poorer quality forecasts may score higher. In response to these limitations, the RAL Verification Group develops improved verification approaches and tools that provide more meaningful and relevant information about forecast performance. The focus of this effort is on diagnostic, statistically valid approaches, including feature–based evaluation of precipitation and convective forecasts, and distribution–based approaches that can provide more meaningful information (for forecast developers as well as forecast users) about forecast performance. In addition, the RAL Verification Group develops forecast evaluation tools that are available for use by members of the operational, model development, and research communities. Development and dissemination of new forecast verification approaches requires research and application in several areas, including statistical methods, exploratory data analysis, statistical inference, pattern recognition, and evaluation of user needs.

FY2019 ACCOMPLISHMENTS

Spatial verification methods and the spatial method inter–comparison project

The initial forecast verification methods intercomparison project focused on comparing the capabilities of newly developed spatial forecast verification methods. That project was completed in 2011 and resulted in a special collection of articles in the journal Weather and Forecasting. A second intercomparison project, developed in partnership with international collaborators, has been implemented and is known as the Mesoscale Verification Intercomparison in Complex Terrain (MesoVICT; http://www.ral.ucar.edu/projects/icp/). Detailed MesoVICT planning took place at the European Meteorological Society annual meetings in September 2013, October 2014 (Vienna, Austria), September 2015 (Sofia Bulgaria) and most recently in September 2016 (Bologna Italy). The meeting was well-attended by key researchers and operational forecasts from various centers/institutions in Europe, as well as Russia and China. The cases for this project included more complex terrain and wind verification. Most of the test cases are already available and are described along with the goals of the project in an NCAR Technical Note TN-505+STR (Dorninger et al., 2013).

To simplify the use of many of the spatial verification methods for the MesoVICT and other efforts, the RAL verification group has developed a spatial verification methods package in the R programming language (SpatialVx; http://www.ral.ucar.edu/projects/icp/SpatialVx/), which continues to be developed. The package currently includes considerable functionality for features-based verification, neighborhood methods, kernel smoothers, and many other statistical and image-based verification approaches.  Many improvements to SpatialVx were made based on feedback from MesoVICT participants, who have been using the software, at the MesoVICT workshop in Bologna.  Initial results for the MesoVICT cases have been made in part because of the availablitiy of SpatialVx. NCAR staff continued to support several packages for the R project for statistical computing.  These include: distillery, extRemes, ismev, smoothie, SpatialVx, and verification packages.

Several papers related to fundamental verification research were accepted for publication or published during FY18.  Abatan et al (2018) discusses the use of the Method for Object-based Diagnostic Evaluation (MODE) for Climate prediction of multidecadal droughts; Dominger et al. (2018), discusses outcomes from MesoVICT. Coelho et al (2018) is a chapter on forecast verification in a book describes the key factors in sub-seasonal to seasonal prediction.  Ebert et al (2018), highlights work towards developing new forecast verification metrics through the World Meteorological Organization (WMO).

The Model Evaluation Tools (MET) Enhanced Verification Package (Metplus)

The Model Evaluation Tools (MET) (http://www.dtcenter.org/met/users/) is a freely available software package for forecast evaluation that was developed and is supported by RAL/JNT staff via the DTC. During FY19, additional MET tools were wrapped with Python and use-cases, or examples, were developed to help users set up systematic evaluation capability more easily.  The wrappers and examples constitute an extension of MET to what is now called METplus.  MET and METviewer are now considered core components METplus and all three are now supported to the community via DTC.

A tutorial was given during FY19 to train the community on the use of METplus between February 4-6, 2019 at NCAR.  Additionally, a tutorial as given at the Naval Research Laboratory (NRL) in Monterey, CA between July 30-August 1, 2019.  The NRL tutorial was recorded to provide “internal training tools” and will be turned into scripts for online video tutorials in FY20.

METplus

RAL staff continued to work with the NOAA/Environmental Modeling Center (EMC) to unify the verification system between the two organizations using MET and METViewer. The goal is to provide this to the community, as well, to help with research-to-operations transitions. This work has focused on addressing the requirements document released in September 2016. Much of the work during FY18 focused on standardizing tool wrappers and configuration files to make the tools easier to use.  Use-cases demonstrating how to set METplus up to evaluate gridded forecasts using gridded analyses (grid-to-grid) and gridded forecast to point observations (grid-to-obs) were established to replicate the verification capability at EMC.  The Quantitative Precipitation Forecast (QPF), Tropical Cyclone Track and Intensity, and Feature Relative use cases established in FY17 were augmented as coding standards and repository conformity were established.  The current METplus python scripts may be found at https://github.com/NCAR/METplus. The two releases made available during FY19 are also there. METplus version 2.1 in January 2019, and version 2.2 in May 2019.

MET

MET was first released in January 2008.  During a decade of community support, there have been 12 community releases.  In FY19, there was 1 community releases. METv8.1 was released to the community in May 2019.  Many bug fixes, enhancements, and optimizations to MET have been included during the past year.  Most notable include:

1)   Compliance with Fortify, a static code analyzer tool which identifies potential security vulnerabilities.

2)   Added support for defining thresholds as percentiles of data (see the user's guide for details and examples).

3)   Added support for the Gaussian interpolation and regridding method, including the sigma option to define the shape.

4)   Enhanced the NetCDF library support.

5)   Added the "-derive" functionality to PCP-Combine tool to compute the sum, min, max, range, mean, standard deviation, or valid data count of data from a series of input files.

6)   Updated support for computing GOES-16/17 pixel locations from metadata of the input files.

7)   Standardize configuration files compute time summaries for all point-data formats.

8)   Added support for land/sea masking, including "nearest" land or sea interpolation

METv8.1 available for download at http://www.dtcenter.org/met/users/downloads/index.php.  A list of the new capabilities can be found in the METv8.1 release notes.

METviewer

METviewer, is the companion database and display system for MET output and was first used by RAL in 2009 for work with the Hazardous Weather Testbed. During the nearly decade of development, it has become the quintessential data analysis tool for users of MET.  Plotting capability include: 1) time series, boxplots and histograms of summarized (mean, median or accumulated) statistics or aggregated statistics; 2) plots of ensemble definition statistics such as rank-histograms; 3)synthesis diagrams such as the Taylor and Performance Diagrams, scorecards and 2-d contour plots of statistics.  METviewer includes the ability to apply boot-strapped or normal confidence intervals and assess statistical significance.  During FY18, three releases of METviewer were made public to the community, including METviewer v2.9 through 2.11.  Releases were driven by necessary enhancements, bug-fixes, and support for MET releases. 

New features in METviewer include:

1)   Support for MariaDB and Aurora (for Amazon Web Services) databases.

2)   Support for several new statistics line-types, including those to comply with METv8.1 line types.

3)   Event Equalization logic is available for hist, roc, rely, ens_ss plots.

4)   Substantial expansion of scorecard capability to allow users to:

  • Put forecast/observed threshold in columns and forecast lead in rows, which is different than a traditional scorecard definition.
  • Combine several vx_masks and/or fcst_leads together into one column.
  • Display values for DIFF and symbols for statistical significance.
  • Use multiple time periods.
  • Use multiple databases

 

Air Force Verification and Validation

During FY18, the JNT continued a number of verification and validation exercises in partnership with the United States Air Force (AF). The AF is currently in the process of undertaking major upgrades to various components of their operational Global Air-Land Weather Exploitation Model (GALWEM) forecast system, including the land information system, data assimilation system, global deterministic and ensemble systems, high-resolution regional modeling system, and post-processing software system. To assist the AF with validating their implementations and/or verifying new implementations are producing quality forecast products that are as or more skillful than current operational products, the DTC has been tasked with helping design and carryout test plans that clearly articulate the targets needed to show necessary improvement for implementation. The design and execution of the test plans heavily leverages the JNT’s expertise in cutting-edge and advanced verification methods as well as the Model Evaluation Tools (MET). Progress on the various implementations is currently underway; it is expected work will continue into FY19.

Verification of Weather Hazards Prediction

RAL staff continued to work with NOAA on evaluating the prediction of severe weather hazards, including heavy rain and snow, strong winds, hail, and tornadoes.  The projects were collaborative between RAL, NOAA National Severe Storms Laboratory (NSSL), and NOAA Centers, including EMC, Storm Prediction Center (SPC), and Weather Prediction (WPC).  Two NOAA testbeds were targeted for this work, including the Hazardous Weather Testbed and the Hydrometeorology Testbed.

The overall goals of scorecard project with the Hazardous Weather Testbed are to (a) identify accepted measures that should be integrated into METplus, (b) explore the applicability to Convection Allowing Models (CAM)s of a new feature-relative methodology being transitioned to HMT this year (c) explore the use of forecast consistency measures that are being added to METplus, (d) develop a flexible scorecard to allow the community to define a useful one for CAM systems and (e) work with HWT to evaluate CAMs retrospectively to demonstrate the usefulness of the metrics (f) work with HWT to assess difference ensemble configurations of the Community Leveraged Unified Ensemble (CLUE). Emphasis was placed on evaluating deterministic and probabilistic products derived from storm-attribute fields and assessing their skill at predicting severe events (e.g., tornados, hail, wind). Particular focus in FY19 was refining the CAM scorecard and adding support in METplus to compute the Surrogate Severe field.  Figure 1 shows the final visual representation of the scorecard.  Colors are targeted to be most distinguishable by color-blind people.   Green or upward pointing arrows indicate model 1 is out-performing model 2. Purple or downward pointing arrows indicate the opposite. The amount of the statistical significance is indicated by the size of the arrow.

 Figure 1.  Example scorecards developed for the HWT and focused on the severe weather indictor, updraft helicity.  See text for description.
Figure 1.  Example scorecards developed for the HWT and focused on the severe weather indictor, updraft helicity.  See text for description.

NCAR also collaborated with AER, Inc for another project focused on the HWT experiment.  This project included the evaluation of the AER HailCast product.  Verification was performed on the prototype hail products and summarized in performance diagrams (see Figure 2). The Performance Diagram (Roebber, 2009), to provide a better understanding of how models perform on predicting RI.  A performance diagram includes four metrics on one plot: 1) Probability of Detection (POD), conditioned on observed events; 2) False Alarm Ratio (FAR), conditioned on forecasted events; 3) Critical Success Index (CSI), a function of POD and FAR; and Frequency Bias (FBIAS), which gives the ratio of forecasted events to observed events.  A forecast is considered to be performing optimally when the symbol is closest to the upper right hand corner.

Figure 2 Example of a Performance Diagram generated for the AER HailCast product.
Figure 2. Example of a Performance Diagram generated for the AER HailCast product.

NCAR RAL and MMM wrapped up a United States Weather Research Program (USWRP) project in collaboration with NOAA/Earth Research Systems Laboratory (ESRL) by submitting a journal article to Monthly Weather Review.  Work continued between RAL, MMM, and HMT is to improve extreme quantitative precipitation forecasts (QPF) that leads to flash flooding by integrating verification research with social science research conducted with National Weather Service (NWS) forecasters. This work was focused on the run-to-run model consistency of forecast. This was initially explored by looking at the trends for QPF and related fields at different output frequency (e.g., hourly output, 3-hourly output) using the Method for Object-based Diagnostic Evaluation extended to the Time Domain (MODE-TD), shown in Figure 3. NCAR also worked with HMT on developing METplus use cases to validate model output against currently available observations as soon as possible.

Figure 3.  Revision series accumulation box plots to visualize forecast consistency for object areas for probability of 1 hour precipitation accumulation > 12.7mm.
Figure 3.  Revision series accumulation box plots to visualize forecast consistency for object areas for probability of 1-hour precipitation accumulation > 12.7mm.

FY2020 PLANS

There will be at least two major releases of METplus and its components in FY2020.  Future releases of MET will include enhancements necessary for the unification efforts described above, as well as capabilities needed for testing and evaluation activities within the DTC. Enhancements will be focused on the following application areas: Aerosols, Air Quality, Hail, Marine, Sea-Ice, Space Weather, Subseasonal-to-Seasonal, and Tropical Cyclone Genesis, and Tropical Environment. Air Force and Hazards Assessment verification and validation work will continue as well as both HWT and HMT, and finally work towards unifying NCAR verification and validation capability through NCAR’s unified modeling framework project, called the System for Integrated Modeling of the Atmosphere (SIMA) will start gaining momentum.

Global Modeling

BACKGROUND

A research to operations (R2O) initiative was established in 2014 by NOAA to upgrade the current operational Global Forecast System (GFS) to run as a unified and fully coupled Next Generation Global Prediction System (NGGPS). NOAA’s long-term plan seeks to integrate the capabilities of its short-term (GFS), ensemble (GEFS), and sub-seasonal (CFS) NWP applications under the infrastructure of NGGPS. A key challenge during this process is to develop a common physics infrastructure that works across all temporal and spatial scales as well as to accommodate an efficient R2O pipeline that effectively uses the expertise in both the research and operational communities. As part of this effort, the Global Model Testbed (GMTB) team was established within the Developmental Testbed Center (DTC) to facilitate community involvement in the development of NGGPS through several avenues: contributing to select aspects of code management and infrastructure for the community to interact with the system, supporting a hierarchical testing framework to NGGPS developers, and facilitating and performing testing and evaluation of innovations for the operational system. The GMTB consists of scientists and software engineers within RAL’s Joint Numerical Testbed (JNT) who take active roles in supporting R2O for global numerical weather prediction (NWP) by closely collaborating with NOAA’s Environmental Modeling Center (EMC) and the research community to develop a Common Community Physics Package (CCPP) and a physics testbed.

FY2019 ACCOMPLISHMENTS

Common Community Physics Package (CCPP)

A modular physics suite accessible both in-line as part of a prediction model, and off-line for isolated testing, will enable physics innovation and contribution from the broader community.  In support of this goal, the DTC and NGGPS put forth the concept of a Common Community Physics Package (CCPP) consisting of a library of physical parameterizations that are either currently operational or are candidates for an upcoming implementation and a generalized software framework for connecting a set of physical parameterizations with a host application (see, e.g. Figure 1). Over the past fiscal year, version 3 of this software was released to the public and was successfully transitioned to NOAA EMC’s UFS Atmosphere master repository.

Figure 1. Representation of the CCPP embedded within UFS Atmosphere as a host model. The gray box represents the CCPP library and the green “Physics Driver” box consists of a software cap for the host model and the CCPP software framework.
Figure 1. Representation of the CCPP embedded within UFS Atmosphere as a host model. The gray box represents the CCPP library and the green “Physics Driver” box consists of a software cap for the host model and the CCPP software framework.

This year witnessed significant growth in the CCPP physics library, corresponding to new schemes added by GMTB staff at NCAR RAL and NOAA GSD as well as schemes added by third parties with support from GMTB staff. An incomplete list of NOAA operational candidate schemes added includes the Chikira-Sugiyama and Grell-Freitas convection schemes, Simplified Higher-Order Closure (SHOC), Mellor-Yamada-Nakanishi-Niino, and scale-aware TKE eddy-diffusivity mass flux planetary boundary layer parameterizations, GFDL, Thompson, and Morrison-Gettelman microphysics schemes, the unified and RAP/HRRR gravity wave drag parameterizations, and the RUC and NoahMP land surface models. These parameterizations are assembled into four supported suites, each of which were subject to a large-scale multi-institutional testing and evaluation activity undertaken to inform the selection of physics for future UFS applications.

The CCPP software framework continues to evolve in order to improve its generality and its applicability to third-party models such that physics can be easily shared seamlessly across modeling platforms across the community. Through collaboration with NCAR CGD and MMM labs as well the Naval Research Laboratory, significant strides toward this goal have been met and continue into the current fiscal year. Improvements include the choice of build modes, with research-oriented dynamic libraries and runtime specification of physics suites or performance-oriented static libraries with compile-time specification of a set of possible physics suites, and an improved and extensible variable metadata format. Comprehensive documentation in the form of a release website, a User’s/Technical Guide, a Developer’s Guide, a software design document, and scientific physics documentation are included with the software and in-person trainings at NOAA EMC were conducted to familiarize the operational community with this community-oriented approach to calling physics.

Hierarchical Testing

To facilitate the development of an advanced physics suite for NGGPS, the JNT, working through the DTC, is developing a uniform ‘test harness’ to enable in-depth investigation of various physical parameterizations. The principal purpose of this physics testbed is to assist the research and operational communities in streamlining the testing process to accelerate the transfer of worthy improvements into operations. The testbed should see use as both a tool for physics developers to display merit and further improve upon their schemes and as an addition to EMC’s physics development decision-making arsenal. The test harness represents the logical progression for testing newly developed parameterizations that typically takes place within the scientific community. Components and complexity are gradually added and iterated upon as one moves through the hierarchy until the full forecast model complexity is reached. The hierarchy is designed to complement both the existing testing protocol at operational centers and independent testing typically performed by parameterization developers. The natural sequence of testing new physics schemes tends to follow tiers of progressively difficult and computationally intensive model runs as merit warrants, and the GMTB testbed mimics this progression (see, e.g. Figure 2).

Figure 2. Diagram illustrating the testing hierarchy plan.  LR indicates low resolution, MR medium resolution, and HR high resolution. Shading indicates where groups are anticipated to focus their efforts
Figure 2. Diagram illustrating the testing hierarchy plan.  LR indicates low resolution, MR medium resolution, and HR high resolution. Shading indicates where groups are anticipated to focus their efforts

Single Column Model (SCM)

A SCM that makes use of the CCPP was developed in FY17 to serve dual purposes: as an example host model to use with the CCPP and as a component of a physics testing harness. Given its connection to CCPP, it can serve as a continually up-to-date column version of the NOAA operational forecast model and as a tool to experiment with other candidate CCPP-compliant physics. Its current capability is limited to running individual case studies based on observational field campaigns, such as those created as part of the Global Atmospheric System Studies (GASS) project. The library of cases is relatively small but growing and users are encouraged to generate and share new ones. One set of case studies to highlight is based on the ongoing LES ARM Symbiotic Simulation and Observation (LASSO) project. This project provides both forcing and comparison data from observations and LES for SCMs. Although most of the cases focus on shallow continental cumulus conditions, the SCM is set up to run any of the potentially hundreds of experiments based on this dataset.

Workflow for Low-Resolution/Medium-Resolution Global Forecast Mode

To facilitate three-dimensional testing that provides information about the interaction between the physics packages and feedback on the large-scale flow, the GMTB maintained an end-to-end workflow for the atmospheric component of the FV3GFS in FY19 that includes post-processing, comprehensive verification, and production of graphics. The DTC-contributed workflow components for creating Python-based forecast plots (e.g. temperature, moisture, convective vs. non-convective precipitation) and verification results (e.g., near-surface, upper-air, and precipitation verification) continued to be upgraded to include additional features and flexibility.

Work is also underway to expand the testbed capabilities to equip physics developers with a wide range of tools to assess strengths and deficiencies of physics. For example, an inventory of diagnostics has been started to identify and implement the highest priority diagnostic tools. Some of the tools that have been implemented include software to compare radiation output to SURFRAD and CERES data, code to produce bias information from GSI diagnostic files which provide “O-B” or (observation – background) information, and subseasonal to seasonal (S2S) oriented diagnostics developed by Drs. Zhuo Wang and Weiwei Li at University of Illinois Urbana-Champaign.

Model Evaluation for Research Innovation Transition (MERIT)

Another project completed during FY19 within the DTC named MERIT (Model Evaluation for Research Innovation Transition) dovetails nicely with the GMTB physics testing harness. Its purpose is to provide the research and operational communities with an end-to-end framework that will streamline the testing process, encourage community engagement, and provide an infrastructure that supports R2O and O2R. MERIT currently includes three cases of interest and used an early version of the FV3 public release. All cases were run through the testing framework previously discussed for the FV3-based configuration to produce baseline results using the Model Evaluation Tools (MET) and python plotting tools. Week-long forecasts were run with 6-hourly output. Objective verification was conducted for surface and upper-air temperature, specific (relative) humidity and wind speed as well as 6-hr precipitation accumulation.  Qualitative analysis and diagnostic investigations of select fields was also conducted, including 500-hPa geopotential height, mean sea-level pressure, and 24-hr precipitation accumulation. Select results of the verification evaluation are available for each forecast on the MERIT cases website (https://dtcenter.org/eval/meso_mod/merit/cases.php). 

FY2020 PLANS

During FY20, work will continue toward making the CCPP software framework more robust and general to truly serve as a common framework amongst NOAA and NCAR earth system models. This year will also see a concerted effort to complete the transition of this framework to NOAA operations by providing appropriate training to EMC scientists and engineers engaged in the physics effort. The CCPP physics and framework are currently slated to be included in the first UFS public release. In addition, work will continue toward expanding the capabilities in the physics testbed in order to equip physics developers with a wide spectrum of tools to assess strengths and deficiencies of physics parameterizations.

 

Tropical Cyclone

BACKGROUND

RAL’s Joint Numerical Testbed Program (JNTP) has a number of efforts related to tropical cyclone (TC) forecasting.  These will be described below as they relate to the JNTP’s Developmental Testbed Center (DTC); the Tropical Cyclone Modeling Team (TCMT); the Tropical Cyclone Guidance Project (TCGP); the Tropical Cyclone Data Project (TCDP); and a new initiative called the Hurricane Risk Calculator.

The Developmental Testbed Center works closely with NCEP’s Environmental Modeling Center (EMC) to support the Hurricane Weather Research and Forecasting (HWRF) system to the research community. The team also tests new capabilities coming from the research community to determine their potential for improving the forecast skill of HWRF.  The goals of this work are to accelerate the improvement in TC forecasts by providing a mechanism for efficiently transitioning research into operations, and through extensive testing of new capabilities to determine their impacts on operational predictions.

The focus of RAL’s Tropical Cyclone Modeling Team is the development of new diagnostics and evaluation tools for tropical cyclone forecasting. Currently, the primary sponsor of the TCMT is NOAA’s Hurricane Forecast Improvement Project (HFIP).  In coordination with the HFIP teams, the TCMT collects, evaluates, and provides results of tropical cyclone track forecasts to the broader HFIP community. Statistical approaches and new graphical displays are being developed by the TCMT. Current efforts are focused on developing new methods of evaluation for ensemble tropical cyclone forecasts of rapid intensification and development of a specialized display and diagnostic evaluation system for use at the National Hurricane Center (NHC).

RAL’s Tropical Cyclone Guidance Project (TCGP) serves as a platform for the collection and dissemination of real-time TC forecast data and products, for the development and testing of new prediction methods, and for the development and testing of new forms of risk communication. TCGP collects real-time tropical cyclone guidance data from numerical prediction centers around the world and collates these data into a publicly-available global repository of TC forecast aids. TCGP’s public-facing web page features the real-time data as well as visualizations of the forecast data. The site receives millions of hits each year from a wide range of users including forecasters, emergency managers, government agencies (e.g., NOAA, FEMA, DHS), private-sector firms (e.g., ship-routing, transportation and logistics, energy producers, energy and risk trading, media), weather enthusiasts, and the general public. TCGP is currently supporting several projects at NCAR funded by NOAA’s Hurricane Forecast Improvement Project, including TCMT’s HFIP Ensemble Rapid Intensification (RI) task and a separate HFIP-funded project to develop new frameworks for predicting extreme rapid intensification. TCGP is also the foundation on which the Hurricane Risk Calculator is being built.

RAL’s Tropical Cyclone Data Project (TCDP) develops and maintains datasets of tropical cyclone observational data for the research community. The extensive data processing, curation, and quality control make these data more accessible for a wide variety of research uses. Currently, TCDP hosts three research-grade datasets and a new historical database of TC wind structure parameters:

VDM+: The Enhanced Vortex Message Dataset: Structure, Intensity, and Environmental Parameters from Atlantic Tropical Cyclones

FLIGHT+: The Extended Flight Level Dataset for Tropical Cyclones

QCAR-R: The QuikSCAT Tropical Cyclone Radial Structure Dataset

TC-OBS: The Tropical Cyclone Observations-Based Structure Database.

These datasets were first released to the public in FY2016 and have undergone several updates. The TC-OBS database uses objective methods to optimally estimate the various database parameters, as well as to provide time-dependent error bounds on the estimated parameters. It is intended to provide the highest quality database possible for parametric wind modeling applications and model evaluation activities (e.g., verification), and to support basic and applied research on TC intensity and structure change.

The Hurricane Risk Calculator began as a small exploratory project funded by the RAL Opportunity Fund in FY2018. It has since been expanded by substantial funding from the UCAR President’s Strategic Initiative Fund. The goal of the project is to explore novel methods of calculating and disseminating hurricane risk information and demonstrate their deliver to mobile apps. The approach involves intersecting the location-specific wind hazard information with the structure-specific vulnerability at the user’s location. When both the wind hazard and vulnerability are described probabilistically, it is then possible to calculate the user’s risk due to hurricane winds. This risk can then be translated into various forms that are relevant and meaningful for the user. Examples could include the risk of incurring major structural damage to their dwelling or the likelihood that their dwelling will be habitable following the storm. That risk information can then be contextualized against the risk and costs of long-distance evacuation, providing the user with actionable guidance on whether they should shelter-in-place, evacuate to a local shelter, or evacuate to a more distant location. The ultimate goal of the project is to wrap these capabilities into a mobile app to provide life-saving alerts to residents in areas impacted by tropical cyclones. 

FY2019 ACCOMPLISHMENTS

Developmental Testbed Center

Advancing HWRF physics

During FY2019, the DTC’s Hurricane team partnered with DTC Visitor Program Principal Investigators and subject area experts to help coordinate and test the performance of alternate physics schemes and innovations relative to the current parameterizations within the HWRF physics suite. To determine which innovations should be considered for operational upgrades, tests are performed with alternate configurations to isolate which innovations provide improved tropical cyclone (TC) prediction. The Mellor-Yamada-Nakanishi-Niino (MYNN) planetary boundary layer (PBL) parameterization became available for testing within the HWRF system from a DTC Visitor Program project by Robert Fovell (SUNY-Albany). A full report of this project can be accessed on the DTC website:https://dtcenter.org/sites/default/files/visitor-projects/DTC_HWRF_proposal_2015_REPORT_v2.pdf

Figure 1. Mean track errors (a), absolute intensity errors (b), and mean intensity errors (c) in the AL basin with respect to lead time. The CTRL (operational) is in black, and MYNN is in red. Pairwise differences (experiment minus control) are shown in light shades with 95% confidence intervals.
Figure 1. Mean track errors (a), absolute intensity errors (b), and mean intensity errors (c) in the AL basin with respect to lead time. The CTRL (operational) is in black, and MYNN is in red. Pairwise differences (experiment minus control) are shown in light shades with 95% confidence intervals.

Additional testing and evaluation was conducted for real HWRF cases, in order to assess the performance of the MYNN PBL scheme relative to the current HWRF operational Global Forecast System (GFS) Eddy-diffusivity Mass-flux (EDMF) PBL scheme. The control configuration (CTRL) was run using the full operational HWRF physics suite (including the GFS-EDMF PBL), whereas the experimental configuration (MYNN) replaced the operational scheme with the MYNN PBL, holding all other physics parameterizations constant.

The track and intensity results indicate fairly neutral results (Figure 1). The differences in track error are statistically indistinguishable, with small SS differences favoring the CTRL configuration at early lead times for absolute intensity error (forecast hours 6, 12, 24, and 36). Although not SS, the mean differences in the intensity bias suggest the MYNN configuration produces slightly less over intensification relative to the CTRL configuration (Figure 1). Most notably, the MYNN configuration produces a slightly larger spin-down in the initial forecast hours, corresponding to the SS differences favoring the CTRL configuration in the absolute intensity errors.

Hurricane Michael was a high-impact 2018 Atlantic hurricane that made landfall in the panhandle of Florida as a category-five storm in October. Early in the lifecycle of the storm, the operational HWRF showed the potential for the storm to intensify rapidly and make landfall as a major hurricane. In fact, three consecutive HWRF cycles (2018100718–2018100806) forecasted Michael to be a category-four hurricane at landfall. When these cycles were initialized, Michael was still a tropical storm. Since Hurricane Michael was such an important storm to forecast accurately, a case study analysis using the HWRF configurations with the GFS EDMF and MYNN PBL schemes was conducted to quantify how the model forecast differed between the two configurations.

Figure 2. Eyewall-averaged vertical profiles of normalized a) tangential and b) radial wind speed in Hurricane Michael from the 36-hour forecast of the 20181009 00 UTC cycle of CTRL (red solid line) and MYNN (red dashed line). The mean air temperature profile from the eyewall dropsondes is shown in black. The error bars represent +/- one standard deviation.
Figure 2. Eyewall-averaged vertical profiles of normalized a) tangential and b) radial wind speed in Hurricane Michael from the 36-hour forecast of the 20181009 00 UTC cycle of CTRL (red solid line) and MYNN (red dashed line). The mean air temperature profile from the eyewall dropsondes is shown in black. The error bars represent +/- one standard deviation.

While the thermodynamic comparison between the two simulations implied similar storm structures (not shown), the kinematic comparison reveals much larger differences. Figure 2 shows eyewall-averaged vertical profiles of tangential and radial wind. For both components of the wind, the MYNN configuration provides a superior match to the dropsonde observations. The height of the peak tangential wind in MYNN (z=400 m) exactly matches observations, but is too close to the surface in the CTRL (z=200 m). Above the height of the peak tangential wind, the reduction with height is much slower in MYNN than in the CTRL and closely mirrors the observations. For radial wind, the inflow is much stronger throughout the boundary layer in MYNN than in the CTRL and is nearly identical to the observations from 0–900 m.

The operational HWRF did not undergo a full implementation during 2019, therefore testing and evaluation conducted by the DTC during this period was to inform HWRF developers of promising upgrades for future operational implementations. A description of the full evaluation can be found in the final report on the DTC website located at:https://dtcenter.org/eval/hwrf_MYNN_2018/

Tropical Cyclone Modeling Team

Development of a Tropical Cyclone Display and Diagnostic System

 Examples of the NHC-Display tool showing the F-Deck editor (top panel), B-deck editor (bottom panel).
Figure 3. Examples of the NHC-Display tool showing the F-Deck editor (top panel), B-deck editor (bottom panel).

A next-generation display and diagnostic system has been developed to support evaluation needs of the U.S. National Hurricane Center (NHC) and the broader tropical cyclone (TC) research community.  The new hurricane display and diagnostic capabilities allow forecasters and research scientists to more deeply examine the performance of operational and experimental models.  The system is built upon modern and flexible technology, including OpenLayers Mapping tools that are web-based and platform independent. The forecast track and intensity along with associated observed track information are stored in a MySQL database.  The system provides an easy-to-use interactive display system, and provides a variety of diagnostic tools to examine tropical model forecast skill.  Consensus forecasts can be computed, displayed and saved to a standard forecast output file.  The system is designed to display observed (best track) and model forecast track and intensity information for both real-time and historical TC cyclones.  The display is also capable of displaying both observed and forecast product model fields.  Display configurations are easily adaptable to meet the needs of the end-user preferences.

 Example of the wind radii display tool.
Figure 4. Example of the wind radii display tool.

New technologies implemented into the display system this year include an advanced tool for editing the hurricane fix-position database (F-deck) and the best-track database (B-deck).  The F-deck editing tool allows NHC staff to add or edit the estimated location of hurricanes using fixed-position information from aircraft analysis, radar, satellite, microwave, and scatterometer observations.  The application can be used to edit best-track points and fix data pointed that are stored in a separate database.  The tool functionality include visual editing of best-track points location and form based editing of best-track and fix points using map or plot.  Once the new best-track and fix points are finalized, they can be updated on the official database.  This information is used to improve the location of the hurricane in the B-deck database during post-hurricane season analysis.  An example of the editing tools is shown in Figure 3.

 Example of the gridded moisture field and wind shear vector forecast products.
Figure 5. Example of the gridded moisture field and wind shear vector forecast products.

A wind radii tool has been added to the available features this past year.  Users can view wind radii graphics for 34kt, 50kt, 64kt or all thresholds on the maps.  Wind radii display capability is available for both best track and/or model data.  Example of the wind radii display capabilities is shown in Figure 4.  Gridded forecast and observation fields have been added to the display system.  The new gridded products include: wind_shear, precipitable_water, moisture, and SST contour products.  The display system calculates derived fields using GFS model output and stores the output in netCDF files.  The fields are extracted from thehttps://nomads.ncep.noaa.gov and ftp://ftp.ncep.noaa.gov/pub/data/nccf/com/gfs/prod/ servers.  Products are available for 120 hours into the future and the past 24 hours centered on the current time.  An example of the moisture product overlaid with the wind shear vectors is shown on Figure 5.  The display system has improved functionality including: pan zoom, configurable labeling, and meta data information pop-up windows.

The latest version of the display system is available at the web-link:http://www.hfip.org/nhc-display

Ensemble Rapid Intensification Products

Building on last year’s HFIP Demo Rapid Intensification (RI) activities, the RI probability for all available model configurations were computed and displayed for each initialization time in 2019. Ensemble RI products were generated and posted online for distribution to the community are available from website: https://www.ral.ucar.edu/projects/hfip/d2019/ensRI/.

New diagnostic products are being developed to improve understanding of ensemble rapid intensification (RI) forecasts uncertainty.  The project development focused on several new prototype visualization products colored by a selected diagnostic parameter.  Diagnostic products focused on environmental conditions (wind shear, maximum potential intensity, sea surface temperature, etc.) and inner core storm structure (precipitation symmetry, radius of maximum winds, inertial stability, etc.) were developed.  Prototype visualizations were developed using parameters available from the ATCF a-decks (intensity, minimum sea level pressure, forecast lead time).  Example diagnostic products are shown in Figs. 6 and 7.

 Example plot of the trajectory of each forecast model colored by forecast lead time.
Figure 6. Example plot of the trajectory of each forecast model colored by forecast lead time.
 Example plot of the trajectory of each forecast model colored by its predicted intensity converted to the Saffir-Simpson Hurricane scale.
Figure 7. Example plot of the trajectory of each forecast model colored by its predicted intensity converted to the Saffir-Simpson Hurricane scale.

Tropical Cyclone Guidance Project (TCGP)

In past years, TCGP was mainly a platform to collect and disseminate real-time TC forecast data and associated visualizations. In FY2019, support from multiple projects allowed the first steps of an expansion of TCGP into a platform that can support a wider variety of TC-related activities, such as probabilistic wind exceedance modeling and impact-based risk communication.

New visualization informed by social science

During FY2019, TCGP continued to provide reliable visualizations of the publicly available TC guidance products. While these products are aimed at expert users (e.g., forecasters), they are also used by people in many other disciplines, as well as general publics around the world. There is always the possibility that users may mis-interpret products in decision making. One person asked a question about a TCGP product on Twitter and it caught the attention of social scientists. The user had wondered whether the models were listed in order of accuracy. This query was forwarded to the TCGP developer, Dr. Jonathan Vigh. Then, based on suggestions from Melissa Bica, a PhD candidate in computer science at CU Boulder, a new prototype visualization was created that emphasizes the more accurate forecast aids (e.g., models) and official forecasts, while de-emphasizing forecast aids that have limited skill, but are technically useful to forecasters (e.g., climatology and persistence models which serve as baselines for skill, beta and advection models which are useful diagnostics, and global ensembles which may have poor intensity skill). Figure 8 shows a prototype example of the new visualization. Thick black solid and dashed lines represent the current and previous official forecasts, respectively. Prominent colors and thick lines are used to represent the forecasts of skillful regional models. Less prominent colors (e.g, light pastel colors) with thinner lines represent the forecasts of forecast aids that have less intensity skill, such as global ensembles and climatology and persistence aids. This feedback loop, in which social science informs the design of products, is an example of the type of integrated research needed for science to more effectively serve the public.

 Example of TCGP’s current intensity forecast product (top) and a prototype of the new visualization (bottom).
Figure 8. Example of TCGP’s current intensity forecast product (top) and a prototype of the new visualization (bottom).

Implementing a probabilistic prediction framework for intensity and wind

During FY2019, an HFIP-funded collaboration between the Massachusetts Institute of Technology and NCAR resulted in the addition of a probabilistic framework for predicting TC intensity and wind exceedances. Through supplemental funding from the NCAR Advanced Study Program’s Graduate Visitor Program, Dr. Kerry Emanuel’s PhD student, Jonathan Lin, visited NCAR for three months and implemented his “Forecasts of Hurricanes Using Large-Ensemble Output” (FHLO) framework on TCGP. FHLO uses the tropical cyclone tracks of global ensemble prediction systems to simulate 1000 synthetic tracks with similar characteristics, but which more fully sample the various track scenarios. Then it uses quantities derived from the fields of the global ensemble members to run an intensity emulator to develop physically-realistic intensity forecasts for each of the 1000 tracks. A wind model is then run, allowing wind exceedance probabilities to be computed. Figure 9 shows an example of the 1000 FHLO track forecasts for Hurricane Dorian, while figure 10 shows an example of the intensity forecasts. The effect of the timing of landfall can be seen in the individual ensemble intensity forecasts. The advantage of this framework is that the spatial variability and flow-dependent uncertainty of the large scale environment can be more fully sampled, leading to more realistic probabilistic forecasts than would be possible from a simpler Monte Carlo-type simulation.

 Example of ensemble track forecasts for Hurricane Dorian. Red lines show the tracks from the underlying global ensemble. Thin grey lines show the 1000 synthetic tracks of the FHLO ensemble.
Figure 9. Example of ensemble track forecasts for Hurricane Dorian. Red lines show the tracks from the underlying global ensemble. Thin grey lines show the 1000 synthetic tracks of the FHLO ensemble.
 Example of ensemble intensity forecasts for Hurricane Dorian. Thin grey lines show the intensity forecasts of the 1000 FHLO ensemble members. Red lines show the percentiles (5th, 25th, 50th, 75th, and 95th) of the FHLO intensity ensemble. Blue line shows the intensity forecast from HWRF.
Figure 10. Example of ensemble intensity forecasts for Hurricane Dorian. Thin grey lines show the intensity forecasts of the 1000 FHLO ensemble members. Red lines show the percentiles (5th, 25th, 50th, 75th, and 95th) of the FHLO intensity ensemble. Blue line shows the intensity forecast from HWRF.

TCGP website: http://hurricanes.ral.ucar.edu/

Tropical Cyclone Data Project (TCDP)

Research Use of TC Datasets of Aircraft and Satellite Observations

In FY2019, TCDP continued to support the research community by hosting and maintaining high-quality datasets of aircraft flight level data and a historical database of TC wind structure parameters derived from these. As shown by counts of registered users in Table 1, research uses of these datasets has continued to increase. Many of the users are graduate students who are using the datasets in their thesis or doctoral research. Each of these datasets and the historical database were first released in FY2016. Since then, a number of papers have been published using TCDP datasets.

 Registered user statistics for TCDP datasets as of 14 Nov 2019.
Table 1: Registered user statistics for TCDP datasets as of 14 Nov 2019.

FLIGHT+ dataset extension and development of a new data product

Through funding from HFIP, work commenced in FY2019 to extend FLIGHT+ to include the recent 2016 - 2018 seasons. These seasons, which included impactful TCs such as Hurricanes Harvey, Irma, Maria, and Michael, are of considerable interest to researchers. The codeset used to process the data was transitioned to a GitHub repository and updated to support a multi-developer environment. Also, a new Level 4 data product was added to provide users with the azimuthal means of flight level data. An example is shown in Figure 11, in which an azimuthal mean is computed from the various constituent radial legs in each of the four quadrants of Hurricane Maria. Beyond ~55 km, no azimuthal mean is computed because radial data were missing in at least one quadrant. Evidence of multiple wind maxima associated with an eyewall replacement cycle is evident in both the individual radial legs and the azimuthal mean. This new capability can help to understand inner core structure changes, further increasing the usefulness of FLIGHT+ for process studies and development of new prediction methods.

TCDP website: https://verif.rap.ucar.edu/tcdata/

 Example showing azimuthal mean of tangential wind for Hurricane Maria. Individual flight legs are shown in red. Azimuthal mean for this group time is shown in blue.
Figure 11. Example showing azimuthal mean of tangential wind for Hurricane Maria. Individual flight legs are shown in red. Azimuthal mean for this group time is shown in blue.

The Hurricane Risk Calculator

Work commenced on the several components of this UCAR-funded project, including refactoring TCGP to support the probabilistic prediction framework (FHLO) that will be used to drive the wind hazard side of the Hurricane Risk Calculator. Also, a researcher collective was established to bring a wide range of experts together from various disciplines including meteorology and physical modeling, structural engineering, web and cloud engineering, social science, emergency management, and verification. The collective is organized into teams around their respective disciplines and will share results with the wider collective as the project moves along. We expect that the various teams will work to secure further funding to take each component of this vision forward.

FY2020 PLANS

Developmental Testbed Center

For FY2020, the DTC will continue its work aimed at improving the HWRF physics through partnerships with physics developers. The performance of alternate physics schemes and innovations to the current parameterizations within the HWRF physics suite will be investigated. Retrospective forecasts using the most recent HWRF model version will be conducted to evaluate the performance of each innovation. Upgrades to the atmospheric component of HWRF will be passed to EMC to be included in its pre-implementation testing for the HWRF 2020 implementation. Additionally, efforts will transition from the operational HWRF system to the Unified Forecast System (UFS) hurricane application, Hurricane Analysis and Forecasting System (HAFS).

Tropical Cyclone Modeling Team

For FY2020, the TCMT will continue to enhance graphical display diagnostic tools with additional analysis features and continue to include the additional gridded fields (satellite, forecast products, SST observations). New diagnostic tools are being developed using input from the National Hurricane Center (NHC) staff.  Additionally, ensemble rapid intensification (RI) forecasts for the 2020 hurricane season will be evaluated and made available through the TCMT website.  The TCMT is also developing new techniques to evaluate ensemble RI forecasts.

Tropical Cyclone Guidance Project

For FY2020, TCGP will continue to expand into a platform that support development and evaluation of additional TC-related data services, prediction tools, and risk communication methods. TCGP is collaborating with the Shanghai Typhoon Institute to set up an international multi-nodal network for sharing TC forecast and observational data. TCGP is also being refactored to run as a cloud service in the Amazon Web Service (AWS). This should open the door to commercialization possibilities. TCGP will begin providing probabilistic data products from FHLO. TCGP will also support real-time testing of forecast aids based on machine learning techniques, such as a logistic regression-based forecast aid (HLOG).

Tropical Cyclone Data Project

In FY2020, TCDP will release a major update to the FLIGHT+ Dataset, extending the dataset to cover the years 2016 - 2018. The release will also include the new azimuthal mean data product and fix several known issues with the data processing of certain storms and data sources. It is also a goal to begin processing the flight level data in real-time and providing this as a data service to support prediction tools in TCGP.

Hurricane Risk Calculator

In FY2020, the Hurricane Risk Calculator will demonstrate the new risk calculation and communication framework in a web application. A retrospective study will be conducted to determine the accuracy of the framework by comparing predicted damage states for retrospective forecasts of major past events with the actual assessed damage for approximately 1000 structures. A key goal is to find a commercial partner to demonstrate these capabilities in a consumer-facing mobile app.

Hydrometeorological Applications

Provide relevant information to water resource decision makers through directed and basic research and development in hydrometeorology, aerosol-precipitation interactions, precipitation nowcasting, microphysical modeling, and winter weather.

  • Water System Program
  • Short-Term Explicit Prediction
  • WRF-Hydro and the National Water Model
  • Water Resources Applications
  • Winter Weather
  • Land Atmosphere Interactions
  • Climate and Managed Water Systems
  • Hydrometeorological Observations


Water System Program

BACKGROUND

The Water System Program is an NSF base-funded effort involving scientists from RAL, CGD, MMM, and EOL.  The program conducts research aimed at improving the representation of the water cycle in local, regional and global climate models. Focusing on the diurnal cycle of precipitation, research has shown that current global climate models do not accurately simulate the frequency, intensity, and timing of summertime convection over much of the globe, including continental regions, despite reasonable simulations of precipitation amount. This model deficiency greatly hampers climate models' ability to predict future changes in intense storms, flash floods, tornados, hurricanes, and other severe weather events that likely have the largest impact on society under global warming, including agriculture and water resources.  Water System funding supports a number of research efforts to advance our understanding and modeling of the water cycle and improve simulations of severe weather events including winter storms); several of these efforts are described below and links to projects described more fully elsewhere in this report links are provided.

Contiguous United States (CONUS) High-Resolution Climate Modeling

The primary goals of the “CONUS project” are to: 1) examine how key physical processes such as precipitation, snowfall, snowpack, runoff and evapotranspiration are influenced by climate change over a significant part of North America using a model with sufficient resolution to capture them (4-km horizontal grid size), and 2) to examine the impact of climate change on severe weather and water resources over North America.

This effort was made possible through an award of 27.5 M core hours on the NCAR Yellowstone computer from the CISL Advanced Science Discovery grant process.  The first year of the project tested and evaluated the model configuration and parameterizations necessary to produce a faithful simulation of the current climate.  During the second and third years of the project, 13 years of the current and future climate simulation at 4-km resolution (Oct. 2000 – March 2013) were completed.  The simulations for the future climate were forced by a modified ERA-Interim reanalysis achieved by adding the CMIP5 climate model monthly mean perturbations of temperature, humidity, winds, and geopotential height to the re-analysis.

A paper describing the simulation and verification as well as some preliminary results was published in September 2017 in the journal Climate Dynamics (Liu et al. 2017). This paper already has already been cited 96 times (see Figure 1).  The dataset has been hosted by CISL on its web site and is available to the community through the following link:

DOI : https://rda.ucar.edu/datasets/ds612.0/

Info on the DOI is at https://ezid.cdlib.org/id/doi:10.5065/D6V40SXP

The output of the model runs is being used by NCAR Water System and university scientists to examine western snowfall and snowpack changes in a future climate, as well as convection in the central U.S. and other severe weather phenomena such as hurricanes.   Key recent papers include:

Musselman, K.N., F. Lehner, K. Ikeda, M. Clark, A. Prein, C. Liu, M. Barlage and R. Rasmussen, Projected increases and shifts in rain-on-snow flood risk over western North America (2018), Nature Climate Change, 8, pp. 808–812.

Gutmann, E., R.M. Rasmussen, C. Liu, K. Ikeda, C.. Bruyere, J. Done, L. Garre, P. Friis-Hansen, V. Veldore (2018): Changes in Hurricanes from a 13 Year Convection Permitting Pseudo-Global Warming Simulation, J. Climate, D-17-0291.

Eidhammer, T., V. Grubišić, R.Rasmussen, & K. Ikeda (2018). Winter precipitation efficiency of mountain ranges in the Colorado Rockies under climate change. Journal of Geophysical Research: Atmospheres, 123. https://doi.org/10.1002/2017JD027995

Liu, C., K. Ikeda., R. Rasmussen, M. Barlage, A. J. Newman, A. F. Prein, F. Chen, L. Chen, M. Clark, A. Dai, J. Dudhia, T. Eidhammer, D. Gochis, E. Gutmann, S. Kurkute, Y. Li, G. Thompson, D. Yates, 2017:  Continental‑scale convection‑permitting modeling of the current and future climate of North America, Climate Dynamics, DOI 10.1007/s00382-016-3327-9.

Prein, AF, RM Rasmussen, K Ikeda, C Liu, MP Clark, GJ Holland (2017) The future intensification of hourly precipitation extremes. Nature Climate Change, 7(1), DOI: 10.1038/nclimate3168

Rasmussen, K. L., A. F. Prein, R. M. Rasmussen, K. Ikeda, and C. Liu, 2017: Changes in the convective population and thermodynamic environments in convection-permitting regional climate simulations over the United States. Climate Dynamics, https://doi.org/10.1007/s00382-017-4000-7.1038/s41558-017-0007-7

Prein AF, C Liu, K Ikeda, R Bullock, RM Rasmussen, GJ Holland, M Clark (2017) Simulating North American Mesoscale Convective Systems with a Convection Permitting Climate Model. Climate Dynamics. doi:10.1007/s00382-017-3947-8

Dai, A., RM Rasmussen, C Liu , K Ikeda , AF Prein (2017) A new mechanism for warm-season precipitation response to global warming based on convection-permitting simulations. Climate Dynamics, DOI 10.1007/s00382-017-3787-6.

Dai, A., R.M. Rasmussen, K. Ikeda, and C. Liu (2017) A new approach to construct representative future forcing data for dynamic downscaling. Climate Dynamics, DOI 10.1007/s00382-017-3708-8

Prein AF, RM Rasmussen, G Stephens (2017) Challenges and Advances in Convection-Permitting Climate Modeling. BAMS; doi:10.1175/BAMS-D-16-0263.12/5

Liu C, K Ikeda, RM Rasmussen, M Barlage, AJ Newman, AF Prein et al. (2017), Continental-scale convection-permitting modeling of the current and future climate of North America. Climate Dynamics, doi:10.1007/s00382-016-3327-9

Musselman, K.N., M. P. Clark, C. Liu, K. Ikeda and R. Rasmussen (2017), Slower snowmelt in a warmer world. Nature Climate Change. 7(3), 214-219. DOI: 10.1038/nclimate3225

Scafe, L. A. Prein, Y. Li, C. Liu, K. Ikeda and R. Rasmussen: 2019: Simulating the diurnal cycle of convective precipitation in North America's current and future climate with a convection-permitting model, Climate Dynamics, DOI: 10.1007/s00382-019-04754-9.

Letcher, T.W., J.R. Minder, 2017: The simulated impact of the snow albedo feedback on the large-scale mountain-plain circulation east of the Colorado Rocky Mountains. Journal of the Atmospheric Sciences, doi.org/10.1175/JAS-D-17-0166.1

Ikeda, K., R. M. Rasmussen, C. Liu, F. Chen, M. Barlage, A. Newman, E. Gutmann, J. Dudhia, D. Gochis, A. Dai, C. Luce and K. Musselman, 2018: Projected Future Changes inb Snowfall and Snowpack Trends in the Western U.S. as Captured by a Convection Resolving Climate Simulation: Mesoscale and Microscale Factors (To be submitted to Climate Dynamics).

Chen, L., Y. Li, F. Chen, M. Barlage, Z. Zhang, and Z. Li, 2019: Using 4-km WRF CONUS Simulations to diagnose surface coupling strength, Clim. Dyn., https://doi.org/10.1007/s00382-019-04932-9.

Zhang, Z., Y. Li, F. Chen, M. Barlage, and Z. Li, 2018: Evaluation of convection-permitting WRF CONUS simulation on the relationship between soil moisture and heatwaves. Climate Dynamics, pp.1-18, Clim. Dyn., http://doi.org/10.1007/s00382-018-4508-5.

 Annual number of papers mentioning convection-permitting climate modeling from 2000 through August 2019. Years in which NCAR’s Water System simulations over the Colorado Headwaters (2010) and CONUS1 (2014) were completed are highlighted in red. The inlay shows citation rates of key publications from the Water System group on convection-permitting climate modeling from 2011 to 2019. Blue bars show studies using the Colorado Headwaters simulations and warm colors show publications based on the CONUS simulations.
Figure 1. Annual number of papers mentioning convection-permitting climate modeling from 2000 through August 2019. Years in which NCAR’s Water System simulations over the Colorado Headwaters (2010) and CONUS1 (2014) were completed are highlighted in red. The inlay shows citation rates of key publications from the Water System group on convection-permitting climate modeling from 2011 to 2019. Blue bars show studies using the Colorado Headwaters simulations and warm colors show publications based on the CONUS simulations.

A key element throughout all of these papers is a comparison of the model simulation to observations during the 13-year historical period.  For the most part, the comparison shows excellent agreement down to the hourly and 4-km horizontal scales (see below for a notable discrepancy).  Given this confirmation, the papers go on to address the impact of future climate on convection, extreme convection (i.e., Mesoscale Convective Complexes), hurricanes, snowfall and snowpack in the Western U.S., behavior of rain on snow events, and changes in the frequency and intensity of rainfall and hail. Many of these studies build on the success and experience gained from the Colorado Headwater high-resolution climate modeling simulations (Rasmussen et al. 2011; see Figure 1) and allowed the expansion of the Water System’s research into new phenomena and regions. The impact of the Water System’s activities in high-resolution climate modeling can be seen in the high citation rates of literature from Water System scientists (Figure 1).

At this time only the thermodynamic future climate impacts can be addressed through the PGW approach. Future work will extend these results to include climate change impacts on the large-scale flow (internal dynamics of the flow).

Scientists at the University of Saskatchewan are using the model output to examine climate change and water in the Canadian prairies, including the effect of land-atmosphere coupling strength and the connection between soil moisture and heatwaves.  University of Quebec at Montreal scientists are examining climate-change impacts on extreme winter storms, while University of Albany scientists are examining the impact of future climate change on the water cycle in the Northeast U.S. and snow albedo. Many other researchers are using the CONUS data as well as can be seen from the more than 130 internet downloads form NCAR’s Research Data Archive (https://rda.ucar.edu/datasets/ds612.0/#!metrics).

The notable deficiency of the simulation was a significant warm and dry bias in the central U.S. in the summertime.  This is a well-known problem with many weather and climate models and is an active research area for the Water System program and for the community (e.g., Lin et al. 2017).  A team was formed to investigate the cause of this bias and they determined it be due to the need to include groundwater at fine model grid spacings (< 4 km). The team found through a series of simulations with and without groundwater at varying spatial resolutions that at the convective-permitting scale (~4km) the resolved riparian regions in the central U.S. become an important local moisture source. This reduces the warm bias through direct effects of shifting surface energy budget components and also in feedbacks, such as increased cloudiness. A paper on this important result is currently in process.

The CONUS team is currently conducting a second set of current and future simulations at high resolution (4 km horizontal grid spacing) over North America (called “CONUS2”) that will be forced by the CMIP5 ensemble mean using a novel way to force the future climate simulations using 6-hourly output of the NCAR CESM model 6-hourly output from the CMIP5 archive (Dai et al. 2017). The model domain will be expanded northward to include Canada and the Canadian Arctic.  The length of both the current and future simulations will be twenty years and will include the new ground water treatment developed by water system scientists to eliminate the warm, dry bias over the central U.S. (and likely over central Canada). This effort is being led by Professor Aiguo Dai from the State University of New York, Albany in collaboration with scientists in the NCAR Water Systems program. This effort enables to address systematic large-scale flow changes due to climate change, but more simulations are needed in order to address the known sensitivity of the flow dynamics to small changes in initial conditions revealed by the work by Clara Deser (e.g., Deser et al. 2014).

A two-day Water System retreat was held on January 29-30th 2019 with approximately 50 NCAR scientists participating. The goal of this workshop was to provide a forum for the discussion of NCAR water related science, to foster new collaborations and to refine goals for the next year of NCAR water-cycle research.  The workshop was well attended with excellent keynote talks by Andreas Prein and Flavio Lehner.   Two of the major outcomes of the workshop was: 1) a draft strategic plan for the water system program and 2) an agreement by participants to form an affinity group regarding research related to South America.  This region was chosen as it represents a challenge for climate simulations for both CESM and WRF/MPAS, especially considering that it spans a variety of climatic regions, from tropic convection, Amazon rainforest, and one of the tallest mountain ranges in the world, the Andes. The next Water System retreat will be held for two days on February 10 and 11, 2020 and will include topics such as activities supporting the movement towards global convective permitting simulations, linkages to the GEWEX Water for Foodbaskets Grand Challenge, and results from the developing South America initiative.  All three of these topics are highlighted in the Water System draft plan.

FY2019 ACCOMPLISHMENTS and FY2020 Plans

A major event for the Water System program was supporting the LATSIS international workshop in late 2019 at ETH, Zurich titled: LATSIS SYMPOSIUM 2019 High-Resolution Climate Modeling: Perspectives and Challenges August 21 – 23, 2019, ETH.  This workshop was primarily funded by the LATSIS Foundation and partially sponsored by GEWEX and the NCAR Water System program.   Christoph Schaer was the primary convener of this workshop, with help from Roy Rasmussen and Andreas Prein.  The workshop focused on both the scientific and computational changes to perform high resolution climate modeling (called storm resolved or kilometer-scale modeling at the workshop).  This workshop was the third in a series of workshops co-sponsored by the Water System program and GEWEX and the first outside the U.S.  This was an outstanding workshop with over 10 high profile invited speakers.  This scientific area continues to grow rapidly, with over 160 papers published in 2018 on this topic (see Figure 1).  Next year Japanese scientist have requested to host the workshop in Kyoto, Japan on September 2-4, 2020. Roy Rasmussen and Andreas Prein are actively working with the Japanese on the logistics and agenda for this workshop. The NCAR Water System program has been hosting AGU sessions on this topic for the past three years and will host another session this year.

Water System scientists worked with Aiguo Dai of the University of Albany to submit a proposal to the NSF call “Accelnet”.  This call is designed to provide funding to support networks of scientists working around the world.  While the proposal did not get funded, the discussions associated with creating the proposal ultimately helped engage the Japanese CPCM contingent to host a workshop next year.  This NSF call is expected to occur every year, and we are currently considering whether to re-apply.

In FY2020, the Water System program will support a major WRF simulation effort called CONUS2 expanding the CONUS thirteen year simulation both in size (extending into northern Canada collaborating with the Global Water Futures program at the University of Saskatchewan) and period of time considered (twenty years current and future).  The horizontal grid spacing will be 4-km, and the model will be forced with a bias corrected current and future climate 6 hourly weather output for the RCP8.5 scenario out to 2100.  This simulation will include the updated ground water treatment discussed above in order to significantly reduce the warm and dry central U.S. bias. The primary difference from the previous simulation will be the use of transient current and future weather from one select Global Climate Model instead of the weather from current re-analysis and a significant extension of the domain to the north. We anticipate that these simulations will be completed during FY2020 and scientific analysis to be well underway by the end of FY2020.

GLOBAL WATER CYCLE AND DROUGHT

Global water cycle studies conducted by Aiguo Dai focused on historical and future changes in precipitation, drought, streamflow, and continental discharge by analyzing observations and model simulations, including WRF-based downscaling of future climate projections on 4-km grids over the contiguous U.S. (CONUS). One particular study area examines how precipitation frequency for light, moderate, and heavy precipitation events may respond to future GHG changes. Another focus area is the separation of natural variations associated with the Pacific Decadal Oscillation (PDO) or the Inter-decadal Pacific Oscillation (IPO) on decadal to multi-decadal time scales from GHG-forced long-term changes in observational records and model simulations for precipitation and other fields. How the rising air temperature may affect surface aridity and drought is another research area of Dai’s group.

ACCOMPLISHMENTS IN FY2019 AND PLANS FOR FY2020

In FY2019, Aiguo Dai helped with the analyses of the PGW CONUS simulations and played a key role in preparing for the Phase II of the CONUS simulations, including obtaining more computer time on the new Cheyenne supercomputer, preparing the boundary forcing data, and choosing the updated domain. In addition, he has published over 10 journal articles related to precipitation, drought during the review period as well as other aspects of the water cycle. These include two first-author papers by Dai et al. in Climate Dynamics directly related to the WRF-based CONUS simulations. In FY2020, the Water System program, working with Aiguo Dai, will focus on Phase II CONUS WRF simulations to downscale the CMIP5 model projections onto a 4-km grid over a large domain covering most of North America.

The major accomplishment in FY2019 was the publication of a number of papers using data from the CONUS1 simulations. In particular, a paper co-authored by Changhai Liu, Kyoko Ikeda and Roy Rasmussen (submitted to Climate Dynamics and currently under review) investigated high-impact Severe Convective Weather (SCW; such as tornadoes, thunderstorm winds, and large hail) in the central and eastern United States for the current and future warmed climate. It was shown that the spatial distributions and seasonal variations of the observed SCW events were reasonably well captured by the retrospective simulation. In a warmer-wetter future, most regions were projected to experience intensified SCW activity and severity most notably in the early-middle spring, with the largest percentage increase in the foothills and higher latitudes. In addition, in FY2019 the water system team conducted a series of 11-year test runs at 12-km grid spacing over North America to 1) quantify the value of ERA-Interim reanalysis based bias-correction to CESM data; 2) assess the impact of different lake water temperature and ice treatments, usage of a lake model, monthly versus daily sea ice, snow fraction treatment, and cloud fraction treatment; 3) evaluate the sensitivity to domain configuration and model physics; and 4) determine and mitigate the sources of the detected model deficiencies (in particular, the winter-spring cold bias in northern U.S. and Canada and year-round low-precipitation bias in Deep South). These test runs and significant efforts to improve the physics of the WRF model enabled us to come up with an optimal model setup for 4-km-resolution production runs of CONUS2.

The plans for FY2020 include continuing analyses of the PGW CONUS1 simulations, conducting and completing the new transient-climate high-resolution CONUS2 simulation, upgrading WRF-Hydro, further enhancement to NOAH-MP, and establishing an affinity group on South America research.

ADDITIONAL AREAS OF RESEARCH FUNDED BY THE WATER SYSTEM

The following research areas leveraged Water System funding with external funds from NOAA, the Bureau of Reclamation, and the Army Corps of Engineers.

WRF-HYDRO

A cornerstone of the NCAR/RAL Water Systems program is the development and support of community modeling tools for both process-based research and hydrometeorological forecasting applications.  These tools are co-developed by NCAR in close collaboration with University researchers and government agencies in the U.S. and around the world.  NCAR/RAL and the Water Systems program serve as focal points for training and collaboration with the hydrometeorological community.  The hCommunity WRF-Hydro System provides scientists and forecasters extensible modeling tools to engage in process-based research into land-atmosphere coupling, hillslope routing processes, surface-water/groundwater interactions, and multi-scale hydrologic evaluations.  As a forecasting tool the WRF-Hydro System can run coupled or uncoupled to atmospheric prediction models and provide so-called “hyper-resolution”’ forecasts of terrestrial hydrologic conditions such as soil moisture, snowpack, shallow groundwater, soil ice, streamflow, evapotranspiration, and inundating waters.  A major accomplishment is the implementation of Version 2 of the National Weather Service National Water Model based on WRF-Hydro. 

FY2019 accomplishments and FY2020 plans are described in the “WRF-Hydro Community Modeling” section of this report.

WRF-URBAN

Global populations have become increasingly urbanized. Currently 52% of the world’s population live in cities, and this proportion is projected to increase to 67% by 2050. Urbanization modifies surface energy and water budgets and has significant impacts on local and regional hydroclimate. In recent decades, a number of urban canopy models (UCM) have been developed and implemented into the WRF model to capture urban land-surface processes, but those UCMs were coupled to the simple Noah land surface model (LSM). A coupled Noah-MP LSM and WRF-Urban was released in WRF in 2018. During 2019 this model was used to perform a number of fundamental scientific studies and has been the basis for a number of scientific proposals to various agencies.

FY2019 accomplishments and FY2020 plans are described in the “Land Atmosphere Interactions” section of this report.

WRF-CROP

This project aims to improve the representation of cropland-atmosphere interactions in the community Noah-MP LSM with the ultimate goal to integrate it in a coupled model to improve seasonal weather forecasts and regional climate simulations for the NCAR Water System Program and to support the GEWEX Water for Foodbaskets effort co-led by the Water System program. Croplands cover 12.6% of the global land surface and 19.5% of the continental United States. Through seasonal change in phenology and transpiration, crops can efficiently transfer water vapor from the crop root zones to the atmosphere. Crops have a detectable influence on regional distributions of atmospheric water vapor and temperature, and can affect convective triggering by modifying mesoscale boundaries. Therefore, croplands can significantly influence land-atmosphere coupling, surface exchanges of heat, water vapor, and momentum, which in turn can impact boundary-layer growth and mesoscale convergence/convection.

FY2019 accomplishments and FY2020 plans are described in the “Land Atmosphere Interactions” section of this report.

ICAR

A joint project between the NCAR Water System program and the U.S. Army Corps of Engineers has led to the development of the Intermediate Complexity Atmospheric Research model (ICAR).  ICAR combines a simplified representation of atmospheric dynamics with physical parameterizations including microphysical and land-surface processes.  The model simplifications permit ICAR to perform high-resolution simulations 100 to 1,000 times faster than a traditional atmospheric model such as the Weather Research and Forecasting model (WRF). This is particularly important for climate downscaling applications.  Such applications are computationally constrained because end-users desire large ensembles of simulations to adequately represent the uncertainty in future climate projections. A key challenge is to adequately capture convection in this hybrid downscaling system. FY19 accomplishments and FY2020 plans are described in the Water Resources Research portion of this report.

Regionally-refined CESM

Figure 2.  Spectral elements from an SE-dycore grid refined to a resolution of around 25km over South America from an outer global grid with Dx~100km.  This grid was generated on NCAR’s HPC cluster using a GUI-driven package.  The grid depicted here possesses around 0.14 as many grid points as a full global grid with Dx~25km.
Figure 2.  Spectral elements from an SE-dycore grid refined to a resolution of around 25km over South America from an outer global grid with Dx~100km.  This grid was generated on NCAR’s HPC cluster using a GUI-driven package.  The grid depicted here possesses around 0.14 as many grid points as a full global grid with Dx~25km.

Support from the NCAR Water System Program has led to the development of a flexible regional-refinement (RR) “tool chain” for use with the Community Earth System Model (CESM) Spectral Element dynamical core (SE-dycore).  A GUI-driven interface for the creation of RR grids has been implemented on NCAR’s HPC systems and will be generalized to run on a variety of systems, including Mac OS, during FY20.  The SE-dycore is a state-of-the-art core based on a high-order piecewise spectral representation on rectangular elements arranged on a cubed-sphere grid.  It is both highly-scalable and highly-accurate.  Refinement factors of 8 or more have been tested successfully in dry dynamical test suites.  The RR SE-dycore in CESM will be used in FY20 to perform high-resolution simulations over South America (Figure 2) for comparison with WRF simulations over the same region.  

Literature

Deser, C., Phillips, A.S., Alexander, M.A. and Smoliak, B.V., 2014. Projecting North American climate over the next 50 years: Uncertainty due to internal variability. Journal of Climate, 27(6), pp.2271-2296.

Lin, Y., Dong, W., Zhang, M., Xie, Y., Xue, W., Huang, J. and Luo, Y., 2017. Causes of model dry and warm bias over central US and impact on climate projections. Nature communications, 8(1), p.881.

Rasmussen, R., Liu, C., Ikeda, K., Gochis, D., Yates, D., Chen, F., Tewari, M., Barlage, M., Dudhia, J., Yu, W. and Miller, K., 2011. High-resolution coupled climate runoff simulations of seasonal snowfall over Colorado: a process study of current and warmer climate. Journal of Climate, 24(12), pp.3015-3048.

Short-Term Explicit Prediction

Background

The Short-Term Explicit Prediction (STEP) Program is a multi-NCAR Laboratory activity with the overarching goal to improve the short-term (0-36 hours) forecasting of high-impact weather events such as severe thunderstorms (heavy rain, tornados, downburst, flash flood, lightning and hail), winter storms (snow, freezing rain and drizzle), and hurricanes. The STEP program emphasizes several research areas that are crucial for advancing the science and application of the short-term prediction of high-impact weather, through collaborative effort incorporating national and international scientists, engineers, and operational personnel from universities, government institutions and the private sector. Most of the forecasting/nowcasting systems and analysis tools developed under STEP are available to the communities for the support of research and real-time operations.

Figure 1. Flowchart for the STEP Hydromet Prediction System.
Figure 1. Flowchart for the STEP Hydromet Prediction System.

In FY19, RAL’s STEP effort emphasized development of nowcasting techniques based on advanced data assimilation and machine learning techniques, improving microphysics scheme for more skillful mail prediction, further improvement of WRF-hydro system. All three research areas are crucial components for the integrated Hydromet Prediction System (Figure 1). The overarching objective of these research efforts are to advance the prediction of heavy rainfall, flash floods and streamflow through the integration of state-of-the-art rainfall estimation, precipitation forecasting/nowcasting, and hydrology modeling techniques into one seamless system.

Error characterization of model precipitation forecasting

In the past year, efforts to improve data assimilation and nowcasting systems have been focused on evaluation of data from the recent STEP hydromet test bed experiment and from the PECAN field campaign. Some additional work supported the RELAMPAGO field campaign. 

 orange) and WRF forecasts (green).
Figure 2. Illustration of the parallel motion of a convective object identified in both observations (MRMS: orange) and WRF forecasts (green).

RAL has evaluated the large collection of WRF 0-24 hour forecasts of convective precipitation in comparison to the EOL radar QPE product, and the NOAA MRMS precipitation product.  In these evaluations, the Method for Object based Diagnostic Evaluation (MODE) and MODE-Time Domain (MODE-TD) tools have been used to characterize more than just the error in precipitation fields.  These tools have been used to identify the spatial structure of the storms and evaluate weaknesses in the prediction of storm size, magnitude, location, and translation. In addition, raw grid-point statistical characteristics (e.g. mean square error and correlation) have been computed over the larger great plains domain.  In this region, we identified regions with weaker correlation between model and observed precipitation closer to the Rocky Mountain front range, and maximums in predictability (correlations up to 0.9) occurring in western Kansas.  Other patterns identified include a known tendency of WRF to over predict the number of light precipitation events, and under predict the number of heavy (>15mm/hr) events. The MODE object based verification analysis showed that errors in position, translation and intensity, had persistence for up to 6 hours (Figure 2).  This implies corrections to the position and translation of a convective system can be derived from comparisons to radar observations and applied to future WRF forecast period.  

Working with the streamflow forecasting group, several major case studies were identified for further investigation.  These cases include two major floods along Cherry Creek near Parker, one of which (June 13, 2016) resulted in streamflow two orders of magnitude greater than the background flow (Figure 3). Comparisons to streamflow are challenging because river systems are commonly modified by human infrastructure, and finding a basin with minimal human modifications, an operational stream, and major flooding events in the STEP hydromet test period was difficult.  The WRF forecasts for this period were shown to predict the evolution of a thunderstorm in the region, but failed to locate it with the correct intensity over the water shed.  The WRF-Hydro model was shown to be able to reproduce a major flood when reliable precipitation data were available, although some errors in timing were consistent regardless of the precipitation event, and work is ongoing to examine the tradeoff between hydrologic model parameters, precipitation errors to identify an ensemble of streamflow forecasts that can be used to make a more statistically reliable flood forecast.

 Observed streamflow on Cherry Creek near Parker, CO for a flood event caused by intense convective rainfall on June 13, 2016, median flow for this period is 8 cubic feet per secton (CFS) and observed peak discharge was over 800 CFS.
Figure 3. Observed streamflow on Cherry Creek near Parker, CO for a flood event caused by intense convective rainfall on June 13, 2016, median flow for this period is 8 cubic feet per secton (CFS) and observed peak discharge was over 800 CFS.

In addition, RAL has completed the analysis of the refractivity fields derived from radar observations for the duration of the PECAN experiment and drafted a paper for publications.  This work has been presented, at the AMS radar conference in Japan in September 2019, and the paper will be an important contribution to the literature.

FY20 Plans

  • Develop machine learning algorithms to increase the information available to data assimilation systems.  This work will leverage the 3D patterns of radar reflectivity and background characteristics derived from large existing datasets of convection permitting WRF model simulations. 
  • Conduct OSSE forecast experiments to understand the possible forecast improvements if machine learning algorithms can be used to improve the initialization of wind fields around thunderstorms.
  • Develop and test algorithms to merge WRF forecasts of storm translation and intensification with radar extrapolation algorithms to improve 0-3hr forecasts. 
  • Examine GOES-16 data for improved detection and nowcasting of thunderstorms.  With the additional channels, higher spatial resolution, and higher frequency of imagery, GOES-16 has the potential for greater applicability to short-term nowcasting.   RAL will compare these observations to radar observations, EOL's micropulse DIAL, and the numerous soundings collected during the RELAMPAGO field campaign.   RAL will test machine learning algorithms to automatically identify robust, reliable patterns in the storm initiation and evolution using a combination of these observations. 
  • Further analyses of data from the RELAMPAGO field campaign to better understand convective initiation (CI) and severe weather occurring in Argentina along the Sierras de Cordobas. This will be compared and contrasted with the weather evolution along the Rockies and over the Great Plains.

 

Data assimilation to improve model-based nowcasting

Figure 4. Comparison of 2-h precipitation forecasts by FINECAST. (a) QPE; (b) forecasts from an experiment assimilating radar alone; (c) same as (b) but assimilating both radar and lightning data; (d) performance diagram for precipitation threshold of 14mm/2hr; and (e) ETS score comparison.
Figure 4. Comparison of 2-h precipitation forecasts by FINECAST. (a) QPE; (b) forecasts from an experiment assimilating radar alone; (c) same as (b) but assimilating both radar and lightning data; (d) performance diagram for precipitation threshold of 14mm/2hr; and (e) ETS score comparison.

In the past year efforts on data assimilation focused on the assimilation of new observations and development of a hybrid system that merges WRFDA with DART based on an ensemble variational data assimilation framework (EnVAR). A prototype  of the hybrid system is now ready for further testing. Recently we have started to apply the system to convective-scale radar data assimilation.

Studies on the assimilation of new observations include development of a technique for lightning data assimilation and evaluate the impact of rainfall (QPE) on convective-scale data assimilation and forecasting. Both types of new observations were simultaneously assimilated with radar observations and their added benefits were evaluated.

Figure 5. 6-hour accumulated rainfall forecasts from an experiment assimilating rainfall only (second from left), radar only (third), and rainfall and radar (right) for the Meiyu MCS case that occurred on June 2 2017.
Figure 5. 6-hour accumulated rainfall forecasts from an experiment assimilating rainfall only (second from left), radar only (third), and rainfall and radar (right) for the Meiyu MCS case that occurred on June 2 2017.

The lightning data assimilation method attempts to obtain updraft information from the observed lightning flash rate. Since the lightning flash rate is only correlated with the maximum updraft in an air column, a vertical velocity profile is needed to map the updraft in the whole column. We tested a few approaches to obtain the profile and evaluated their impact on the lightning data assimilation. Another challenge in lightning data assimilation is to understand the representativeness of the lightning observations. Since the lightning flash is an instantaneous and point quantity but NWP models have much coarser resolution spatially and temporally, it is important to understand the representative error of the model. We used a method to match the scale of the lightning data to that of the NWP model such that the lightning data can be effectively assimilated into the model. Our results show that the lightning data assimilation in combination with radar data significantly improves the convective-scale analysis and very-short-term forecasting. The new lightning data assimilation method have been implemented to both WRFDA 3DVar and the 4DVar-based nowcasting system FINECAST. Figure 4 shows the positive impact of the lightning data assimilation from FINECAST by comparing the combined radar and lightning data assimilation experiment (RAD+LTN) with  the radar alone experiment (RAD). The RAD+LTN experiment successfully predicts the newly initiated storm cell in front of a decaying convective system. The 2 hour forecast skill is significantly increased.

The assimilation of QPE data were conducted using WRFDA 4DVar. A case of MCS embedded within a Meiyu front that affected Taiwan with heavy rain and severe flood was used for the study. Our study shows that while radar observations are crucial to analyze the air motion and its associated convergence through data assimilation, it has not positive effect (sometimes negative effect) on the humidity field. We further demonstrated that the assimilation of QPE data can greatly improve the low- to mid-level humidity analysis, hence result in more skillful short-term heavy rain forecasts. Figure 5 shows that the addition of the rainfall data assimilation to radar improves the pattern, location, and intensity of the rainband over the Taiwan island.  

Figure 6. Water vapor analyses from WRFDA 4DVar on the eighth model level from the radar alone experiment (left) and radar + rainfall experiment (right).
Figure 6. Water vapor analyses from WRFDA 4DVar on the eighth model level from the radar alone experiment (left) and radar + rainfall experiment (right).

Figure 6 compares the humidity analysis fields between the radar alone experiment and the radar plus rainfall experiment. The impact of the added rainfall assimilation on the humidity analysis in the rainband region is significant, which is the main reason for the improved heavy rain forecast.

In the past year, we also further improved the performance of FINECAST including the improvement of surface data assimilation and terrain scheme, and adding a scheme to blend the advection only forecast and the full-model forecast to address the issue of model spin up. 

FY20 plans

 

  • Continue the testing and improvement of model-based nowcastingusing FINECAST
  • Design and test a hybrid convective-scale data assimilation system based on NCAR’s WRFDA and DART systems
  • Continue the studies to assess impacts of new high-resolution observations including cellphone pressure observations and radar refractivity observations (latter is in collaboration with EOL)
  • Collaborate with EOL on OSSE studies to evaluate observation system design concerning EOL’s new multi-pulse dial (MPD)

 

Evaluation and improvement of model microphysics parameterization    

Development of prototype multi-moment graupel/hail category in Thompson microphysics parameterization

Figure 7. Lowest model level simulated reflectivity at 1500 UTC on 20 June 2015 over South Dakota from a test of the new y-intercept diagnostic in the original (mp=28) Thompson single-moment graupel/hail scheme (left) versus that from a test of the new (mp=38) Thompson multi-moment graupel/hail scheme (right).  The maximum hail size is denoted by the contour lines; 0.5 cm in black, 1.0 cm in blue, and 2.0 cm in magenta.  The white lines denote the area for line-averaged cross sections shown in Figure 8.
Figure 7. Lowest model level simulated reflectivity at 1500 UTC on 20 June 2015 over South Dakota from a test of the new y-intercept diagnostic in the original (mp=28) Thompson single-moment graupel/hail scheme (left) versus that from a test of the new (mp=38) Thompson multi-moment graupel/hail scheme (right).  The maximum hail size is denoted by the contour lines; 0.5 cm in black, 1.0 cm in blue, and 2.0 cm in magenta.  The white lines denote the area for line-averaged cross sections shown in Figure 8.

In FY17-FY18, a multi-moment graupel/hail category was added in the Thompson microphysics scheme.  For this multi-moment graupel/hail category, number concentration and bulk volume mixing ratio are now predicted variables, in addition to the bulk mass mixing ratio. This allows for the graupel density to vary in space and time and be diagnosed from the new predicted variables.   Based upon initial testing of the new scheme, the code was refined and prepared for use in a real-time forecast model that was run during the RELAMPAGO field campaign in FY19.  In addition, hail pads were deployed during RELAMPAGO in order to obtain ground truth hail size measurements. 

Improvements to the single-moment graupel/hail category in Thompson microphysics

Figure 8. Line averaged cross sections of model simulated reflectivity from the two simulations shown in Figure 7.
Figure 8. Line averaged cross sections of model simulated reflectivity from the two simulations shown in Figure 7.

The original Thompson single-moment graupel/hail category only predicted graupel/hail mass mixing ratio, however it diagnosed the y-intercept of the size distribution allowing it to vary and not be prescribed as a constant value.  A recent study was published by Field et al. (2019) that showed observed relationships between the slope and y-intercept parameters of hail size distributions based upon in situ hail measurements from 18 flights of the T-28 armored aircraft between 1995-2003.  This study also revealed that the original Thompson single-moment graupel/hail diagnostic y-intercept values were far too large compared to observed values.  Therefore, using these data as guidance, new diagnostic relationships were developed and tested.  This work in ongoing and in parallel with the multi-moment graupel/hail scheme testing and evaluation, by using the same case studies and observations to compare with both simulations (Figs. 7-8). 

Established methods for baseline performance evaluation

In order to evaluate impacts on storm structure, QPF, and, in particular, storm evolution due to physics parameterizations, including updates made to the Thompson microphysics scheme, an object-based evaluation tool that tracks storms over time is needed.  The Method for Object-based Diagnostic Evaluation (MODE)-Time Domain (TD) is a tool developed at NCAR for such analyses, allowing users to set several parameters to evaluate storms with certain spatial scales and intensities.  Moreover, there are several parameters that users can set that impact how observed and modeled objects are matched for comparative evaluation metrics. In FY18-19, sensitivity tests were performed that varied the matching parameters to quantify the impact of these choices on the evaluation outcomes.  This effort has established methods for how to apply MODE-TD to quantify impacts of various physics choices, with specific emphasis on the new multi-moment graupel/hail category, and in order to determine baseline performance metrics.

FY20 plans

  • Evaluate the Thompson microphysics scheme updates to improve forecasted convective storm structure, evolution, and QPF.  This includes both the new y-intercept diagnostic in the single-moment graupel/hail scheme and the new multi-moment graupel/hail scheme.
  • Run a ~30-day simulation experiment to provide a longer-term evaluation of the schemes, as opposed to single case studies.  This experiment period will be selected to coincide with a complementary field experiment or numerical modeling exercise (i.e. RELAMPAGO and/or Hazardous Weather Testbed), to broaden its applicability and enhance the observations that are available for the study.
  • Use MODE-TD to evaluate the prototype microphysics scheme compared to the original Thompson scheme and other microphysics schemes and/or forecast ensemble members as available.
  • Use evaluation results to inform further microphysics parameterization improvements.

WRF-Hydro and the National Water Model

Background

Over the past four years, a team of RAL scientists and engineers have developed and transitioned the community WRF-Hydro modeling system into the National Water Model (NWM), the first operational, high-resolution, physics-based hydrologic prediction model ever implemented across the continental U.S.  Developed in close collaboration with the National Weather Service (NWS) Office of Water Prediction, the USGS, and a number of university partners, the RAL team delivered the first version of the model in only one year (two years ahead of schedule) on a modest budget. The model is now being used, to solve a complex problem—tracking water flow across the country to aid local communities and emergency managers in responding to water-related threats.  It is also providing consistent, reliable, high-resolution data to decision makers across the country, helping them address increasingly complex societal issues related to flooding, drought, water availability and water quality. In addition to providing significant benefit to the operational community, the NWM is proving to be a valuable tool in the conduct of research and as a mechanism for moving research to operations.  The NWM provides an important bridge between the numerical weather prediction community and the terrestrial hydrology community. As an open source, open platform model, the NWM and WRF-Hydro will continue to evolve to meet the nation’s complex needs for water resource planning and management information while also providing a seamless pathway for academic researchers to innovate new improvements in water prediction. 

Notable Recognition - National

In September 2017 the Department of Commerce recognized the development and implementation of the National Water Model with a Gold Medal for Scientific/Engineering Achievement; this is the highest award given by the agency. The award noted that “the NWM is the first high-resolution CONUS-wide water forecast model executed in the operational NOAA supercomputing environment.  It provides first-of-its-kind water resource guidance to NWS River Forecast Centers and other end users…” The NOAA/NCAR development team was specifically praised for demonstrating “exceptional dedication, hard work, expertise, and creativity.”  The WRF-Hydro modeling system, which was developed at NCAR, has been successfully transitioned into the nation’s new state-of-the-art operational water prediction system through this exemplary NOAA/NCAR partnership.

Local Recognition

In December of 2017, the NCAR WRF-Hydro/NWM development team within the Research Applications Laboratory was awarded an Outstanding Accomplishment for Scientific and Technical Advancement for the development and implementation the National Water Model, the first operational, high-resolution, hydrologic prediction model to be implemented across the continental U.S.

Accomplishments & Goals:

FY2019 National Water Model (NWM) Version 2.1 development lead to many scientific and technical advancements which include but are not limited to the following.

  • Improved representations of meteorological forcings and groundwater fluxes for the NWM implementation for Hawaii.
  • Development and implementation of the NWM for Puerto Rico and the U.S. Virgin Islands making this implementation the first 24/7 analysis and hydrologic forecast service for this region.
  • Expanded and improved hydrologic model parameter estimation, calibration, and regionalization methods increased the skill in NWM analyses and forecasts.
  • Improved treatment of snowpack ablation and melt water dynamics and their interactions with soil runoff generation mechanisms.
  • Implementation of new meteorological forcing data bias corrections to improve the quality of source meteorological data driving NWM forecasts.
  • An expanded capability for ensemble seasonal water supply forecasting in the western U.S. was established to demonstrate and provide quantitative evidence of ensemble seasonal water supply forecasts in the western U.S.
  • Significant expansion of reservoir and water management representation to improve analysis and prediction of mainstream river flows impact by human operations.
  • An updated representation of soil depth to bedrock structure using geomorphically derived soil depth estimates.

FY2020 Goals for NWM Version 3.0 include improvements to the whole WRF-Hydro/NWM Ecosystem and suite of tools as well as focusing on engaging and training our community partners. Listed below are some of the overarching themes.

  • Expand the oCONUS domain and implementing NWM for the Cook Inlet region of southeast Alaska
  • Improve the meteorological forcings in terms of downscaling and rain/snow partitioning and additional bias corrections
  • Improve the land use and land cover representation by using a dynamic land cover specification
  • Improve computational performance
  • Continue code modularization efforts
  • Update groundwater/baseflow representation to better represent low flow conditions and hydrograph recession characteristics.
  • Partner with entities that are working on coupling the NWM to coastal prediction models.
  • Expand options for representing infiltration and surface runoff processes in the NWM.
  • Engage NWS River Forecast Centers with individual hands-on training

Goals of FY20 are to continue to build the WRF-Hydro community by:

  • Expanding the number and type of video recorded webinars on WRF-Hydro and NWM development activities
  • Expanding WRF-Hydro and NWM related news outlets such as Twitter to broaden entrainment of the  global hydrologic community
  • Expanding the online training suite of education modules to reach those users who are unable to attend the hands-on training workshops due to limited seating
  • Expanding the translated model documentation and user guides into Spanish to reach our Latin American and Spanish speaking users around the globe

Water Resources Applications

BACKGROUND

Scientists and engineers in RAL’s Hydrometeorological Applications Program at the National Center for Atmospheric Research are collaborating with the U.S. Army Corps of Engineers, the Bureau of Reclamation, the National Atmospheric and Oceanic Administration, the National Aeronautics and Space Administration, the Department of Energy, the U.S. Geological Survey, the U.S. Forest Service and multiple universities to build new community hydrologic datasets, models and methods for water resources research and applications that will advance our nation’s capability to monitor, predict and project hydrology and to inform water management and planning.  The work strives to address scientific gaps and serve practical needs across time and space scales – from quantifying long-term trends and variability, to predicting real-time flood and drought risk and characterizing uncertainties arising from a multitude of sources. Through developing improved methods, models, and datasets, this research improves the fundamental building blocks on which hydrometeorological analyses and applications depend. It provides useful tools and data resources for both researchers and practitioners to better manage current climate and flood risk, reveal future climate change risks, and to more effectively evaluate future change and adaptation options.

ACCOMPLISHMENTS

Models, methods, and datasets

In the last 5 years, RAL/HAP scientists have made widely recognized advances in developing models, methods, and datasets. These science advances collectively provide a strong foundation for understanding and adapting to future environmental change, servicing multiple needs for multiple users. The key advances are as follows:

Meteorological forcing data

Moving from deterministic to probabilistic national-domain meteorological datasets. NCAR has further developed the Gridded Meteorological Ensemble Tool (GMET), which generates high-quality, probabilistic gridded meteorological fields that can be used to quantify uncertainty of meteorological forcings useful for climate model evaluation, hydrologic model parameter estimation, and hydrologic data assimilation. The initial application of GMET is a first-of-its-kind ensemble gridded dataset of precipitation and temperature for the period 1980-2012, was described by Newman et al. (2015) and is available at http://dx.doi.org/10.5065/D6TH8JR2. Subsequent applications of GMET include the probabilistic evaluation of WRF model simulations (Prein et al., 2016; Liu et al., 2016) and hydrologic data assimilation for initializing short-range streamflow forecasts (Clark E. et al., 2017).  In the past year, GMET has been extended to make use of climatologically aided interpolation methods in regions with sparse observational networks (e.g. Alaska) and extreme meteorological gradients (e.g. Hawaii) (Newman et al. 2018).

More details on GMET are available at

https://ncar.github.io/hydrology/projects/meteorological_datasets

Local Scale Weather and Climate Prediction

Advancing a new, powerful statistical weather and climate downscaling tool for high-resolution weather prediction and localizing climate projections.  NCAR’s Ensemble Generalized Analog Regression Downscaling (En-GARD) is a generalized ensemble downscaling utility that can apply most common downscaling methods, e.g. regression, analogs, and hybrid analog-regression method on any number of variables and spatial configuration. It is being used both for forecasting and climate downscaling applications. The En-GARD approach derives from previous papers on climate downscaling and probabilistic quantitative precipitation estimation (Clark and Hay, 2004; Clark and Slater, 2006; Gangopadhyay et al., 2005). The development of En-GARD and the assessment of forecasting and climate downscaling performance will be documented in a series of papers in the next year.  In the past year, En-GARD has been used to produce a large ensemble of downscaled climate projections, and this work is currently being documented in two papers (Gutmann et al in prep.; Hamman et al. in prep).  The En-GARD source code is available at https://github.com/NCAR/GARD, and is already being used by university and agency researchers around the world with support from NCAR.

Creating the first community quasi-dynamical weather and climate downscaling model.  NCAR has developed the Intermediate Complexity Atmospheric Research (ICAR) model, a quasi-dynamical downscaling approach that uses simplified wind dynamics to perform high-resolution meteorological simulations 100 to 1000 times faster than a traditional atmospheric model and can therefore be used to better characterize uncertainty across numerical weather prediction models and climate models, and in dynamical downscaling. Gutmann et al. (2016) describes the development of ICAR.  In the last year, this code has been significantly updated through the use of object-oriented programming and modern parallelization techniques.  The newer parallel features of ICAR overlap communication between parallel processes with computation within a process to permit the simulation to scale to 100,000 cores (Rouson et al. 2017).  As a result, a simulation that used to take 30 minutes can be performed in half a second. In addition, new supporting infrastructure and documentation has been developed to make the setup and configuration of ICAR simulations easier for new users. The ICAR source code is available at https://github.com/NCAR/icar.  ICAR is being used by university and agency researchers around the world with support from NCAR, see for example, Horak et al (2018) and Bernhardt et al (2018). The effort is supported by USACE and Reclamation to improve their understanding of future climate at scales relevant to water resource managers.

More details on ICAR are available at

https://ncar.github.io/hydrology/projects/intermediate-complexity_downscaling

Hydrologic Modeling

Advanced a comprehensive new community hydrologic modeling framework that for the first time provides the hydrology community with a structured approach for investigating and developing theories about hydrologic processes.  NCAR’s Structure for Unifying Multiple Modeling Alternatives (SUMMA)is a framework that provides multiple options to generate models that simulate a wide range of biophysical and hydrologic processes from the treetops to the stream. It will be particularly useful to characterize model and parameter uncertainty in hydrologic model simulations, and to identify strengths as weaknesses in our existing hydrologic understanding. Clark et al. (2015a; b; c) describes the development of SUMMA; the SUMMA source code is available at https://github.com/NCAR/summa. Many of the concepts developed with SUMMA are now being used to unify land modeling activities across NCAR as part of the developing Community Terrestrial Systems Model (CTSM).  In addition, RAL scientists are collaborating with NASA to implement SUMMA within the NASA Land Information System (LIS).

More details on SUMMA are available at

https://www.ral.ucar.edu/projects/summa.

Developed an advanced hydrologic model parameter estimation tool to address the long-standing challenge of model implementation over regional domains. NCAR’s Multi-scale Parameter Regionalization Flex (MPR-flex) is a model-independent, flexible parameter estimation application that enables continental-domain application of multiple hydrologic models in a spatially consistent way (Mizukami et al., 2017). In the past year, MPR-flex has been applied to multiple hydrological models, a step which significantly expands the potential use of MPR-flex by the broader community, and permits a consistent evaluation of calibration issues across different hydrologic modeling approaches. NCAR has also investigated the properties of MPR calibrated models, particularly for flood frequency analysis (e.g. Wobus et al 2017) and shown how model calibration can be modified to improve robustness of the parameter set across a broader range of metrics through the use of a weighted Kling-Gupta Efficiency objective function.

More details on MPR-Flex are available at

https://ncar.github.io/hydrology/projects/parameter_estimation.

Developed a flexible multi-method, continental-domain routing model,  mizuRoute, which efficiently routes streamflow from any distributed hydrologic model through river networks. It has been used to provide streamflow values at 54,000 river segments across the contiguous United States. In the past year, mizuroute has been extended to operate on the more detailed National Hydrography Dataset (NHDplus), which contains millions of river segments across the United States. To enable this effort, mizuroute has been parallelized to make better use of available High Performance Computing resources. Mizukami et al. (2016a) describes the development of mizuRoute; the mizuRoute source code is available at https://github.com/NCAR/mizuRoute.

More details on mizuRoute are available at

https://ncar.github.io/hydrology/projects/streamflow_routing.

Climate Scenario Applications

 Analyzed existing climate scenarios and developed a broader suite of projections. To aid water resource managers in assessing projections of future hydrologic scenarios, NCAR has performed detailed assessments of existing climate scenarios and developed a large-ensemble of downscaled hydrologic predictions. In performing these assessments, the first step has been to review hydrologic metrics of interest, in collaboration with researchers in the university community and MMM (Ekström et al 2017).  This work has been extended with broad discussion of the properties of ensembles of climate projections, with particular attention to the independence and representativeness of different climate models (Abramowitz et al 2018).  Next, NCAR has worked with the university community in the evaluation of hydrologic projections to understand the role of hydrologic models in controlling climate change signals (Melsen et al 2018). Most recently, a very large ensemble of climate projections have been developed, making use of ICAR and En-GARD as well as MPR and Mizuroute to improve the physical representation of both local climate and hydrology.  This work is being disseminated at the American Geophysical Union and will be published in the coming year.  Likewise, advanced projections are being developed over Alaska and Hawaii to provide these communities with a larger ensemble of likely hydro-climate projections than they have had available in the past.

Outreach to water resource managers. NCAR has performed extensive outreach to water resource managers, working closely at every stage in the process with collaborators at the US Bureau of Reclamation and the Army Corps of Engineers.  This has included developing guidance for water resource manager (Vano et al. 2018), as well as presenting webinars on the analysis of current datasets (Hamman et al 2018).

Hydrologic Forecasting Applications

Developed an integrative system for real-time assessment and demonstration of advanced streamflow forecasting approaches.  The System for Hydromet Analysis Research and Prediction (SHARP) provides an integrative platform for assessment and demonstration of many of the modeling and methodological advances outlined above to evaluate new opportunities for streamflow and water prediction applications, including operational forecasting for water systems support for development of climate adaptation though better anticipation of climate and water extremes. The effort is supported by USACE and Reclamation to provide science-based solutions to long-standing challenges in streamflow forecasting to support water management.

More details on SHARP are available at 

https://ncar.github.io/hydrology/projects/streamflow_forecasting.

Assessed CONUS-wide seasonal streamflow predictability.  To develop and benchmark new methods of climate and seasonal streamflow prediction, NCAR has conducted a comprehensive assessment and intercomparison of new and existing strategies for leveraging climate and hydrologic predictability to advance operational seasonal flow prediction, in collaboration with water management offices of the two largest US federal water agencies, USACE and Reclamation (Mendoza et al, 2017).  This research effort also included a comprehensive nationwide seasonal hydrologic predictability assessments, as described by Wood et al. (2016), and assessment of hybrid methods in climate prediction (Madadgar, et al, 2016). 

Advanced new methods in hydrologic data assimilation, which is a critical strategy for improving short to seasonal range streamflow predictions. With support from USACE and Reclamation, NCAR has comprehensively assessed capabilities for assimilating snow observations (Huang et al., 2016; Clark et al., 2006; Clark and Slater, 2006; Wood and Lettenmaier, 2006) to improve seasonal hydrologic prediction; and the particle filter for streamflow assimilation to enhance shorter range operational flow forecasting (E. Clark et al, 2017). In support of community modeling, NCAR has developed Hydro-DART, an open-source, ensemble based data assimilation architecture (DART) that has been configured to support hydrologic data assimilation in the community WRF-hydro modeling system.  The diversity of different data assimilation methodologies and filtering algorithms in DART provides users with significant flexibility in applying data assimilation to a host of environmental data assimilation problems.  Using this new HYDRO-DART system work is now proceeding on assimilation of remotely sensed snowpack estimates using NOAA JPSS satellite data.  HYDRO-DART is also being employed in WRF-Hydro parameter estimation activities.  (The DART system is developed and supported by the NCAR Computational and Information Systems Laboratory). 

Advanced new methods in streamflow forecast post-processing, another critical strategy for improving short to medium range streamflow forecasts, NCAR has leveraged support from USACE and Reclamation to develop a first-of-its-kind multi-method streamflow forecast post-processing application.  Working within SHARP, the application provides a retrospective and real-time assessment of a broad range of forecast post-processing approaches being explored by the streamflow forecasting community.  NCAR has also developed and implemented a major, CONUS-wide ‘nudging’ technique for operational deployment of the National Water Model, in which observed streamflows are used to adjust the simulation and forecast outputs of the NWM to improve NWM forecast skill. 

Leadership of HEPEX, an international initiative in ensemble hydrologic prediction.  NCAR’s Andy Wood is current chair of the Hydrologic Ensemble Prediction Experiment (HEPEX; http://www.hepex.org/), together with other leads from the European Center for Medium Range Forecasting, Irstea (France), and the Commonwealth Science, Industry and Research Organization (CSIRO, Australia).  With over 400 members, HEPEX promotes the development and operational application of ensemble hydrologic forecasting to support water, hazard and energy management.  In 2018, HEPEX organized international workshops on ensemble prediction (Melbourne, Australia).

Water Resource Applications of Computational Hydrology Research and Tools

Many of the models, methods, datasets and tools described above are motivated by the need for new applications to serve important societal needs.  Two key needs and application areas are Operational Streamflow Prediction and Assessing Climate Change Impacts on Hydrology and Water Resources. 

1.  In the Streamflow Prediction area, a major effort has been the development of hyper-resolution modeling and prediction capabilities for the National Weather Service, and in particular the National Water Model that was launched operationally in summer 2016 at the National Water Center.  This effort leveraged elements described above, including the WRF-Hydro model, the visualization tool RWRFhydro, and the Meteorological Forcing Engine.  A second major effort has been the deployment of real-time short-range and seasonal ensemble streamflow forecasts to support collaborations with the two major US water agencies (USACE and Reclamation).  The central objective is to demonstrate and understand the viability of new ‘Over-the-Loop’ forecasting methods for water management.  This effort employed the SHARP system, running operationally at NCAR and integrating tools such as GMET, SUMMA, mizuRoute, En-GARD, and several data assimilation and post-processing capabilities.  Streamflow prediction applications are described in more detail in the Streamflow Prediction LAR.

2.  In the Climate Change Impacts area, NCAR has used many of the tools described above to undertake a major CONUS-wide effort (extending to Alaska and Hawaii) to characterize and communicate uncertainties in the projection of future hydrology, given climate change and variability.  Recognizing that key scientific challenges persist in estimating future climate at the large scale, downscaling climate to the local scale, and representing hydrologic sensitivities to climate, NCAR has developed an assessment strategy that reveals uncertainties in each of these areas that have been previously under-estimated, and then reduces these uncertainties through application of tools and models described above, including ICAR, GMET, SUMMA, MizuRoute, MPR-flex, among others.  Outcomes from this effort are informing federal water agency guidance to support water management decisionmaking and risk assessment.   

Each of these opportunities is expected to be pursued in future work (next section).

Climate Scenario Applications

Analyzed existing climate scenarios and developed a broader suite of projections. To aid water resource managers in assessing projections of future hydrologic scenarios, NCAR has performed detailed assessments of existing climate scenarios and developed a large-ensemble of downscaled hydrologic predictions. In performing these assessments, the first step has been to review hydrologic metrics of interest, in collaboration with researchers in the university community and MMM (Ekström et al 2017).  This work has been extended with broad discussion of the properties of ensembles of climate projections, with particular attention to the independence and representativeness of different climate models (Abramowitz et al 2018).  Next, NCAR has worked with the university community in the evaluation of hydrologic projections to understand the role of hydrologic models in controlling climate change signals (Melsen et al 2018). Most recently, a very large ensemble of climate projections have been developed, making use of ICAR and En-GARD as well as MPR and Mizuroute to improve the physical representation of both local climate and hydrology.  This work is being disseminated at the American Geophysical Union and will be published in the coming year.  Likewise, advanced projections are being developed over Alaska and Hawaii to provide these communities with a larger ensemble of likely hydro-climate projections than they have had available in the past. 

Outreach to water resource managers. NCAR has performed extensive outreach to water resource managers, working closely at every stage in the process with collaborators at the US Bureau of Reclamation and the Army Corps of Engineers.  This has included developing guidance for water resource manager (Vano et al. 2018), as well as presenting webinars on the analysis of current datasets (Hamman et al 2018). 

Hydrologic Forecasting Applications

Developed an integrative system for real-time assessment and demonstration of advanced streamflow forecasting approaches.  The System for Hydromet Analysis Research and Prediction (SHARP) provides an integrative platform for assessment and demonstration of many of the modeling and methodological advances outlined above to evaluate new opportunities for streamflow and water prediction applications, including operational forecasting for water systems support for development of climate adaptation though better anticipation of climate and water extremes. The effort is supported by USACE and Reclamation to provide science-based solutions to long-standing challenges in streamflow forecasting to support water management.  

FUTURE PLANS

Looking ahead there is a vast array of possibilities in improving the fidelity and skill of our hydrologic modeling and prediction tools. RAL scientists will continue to work with their academic and government partners to advance this research and also to bring new water prediction technologies and capabilities into societal applications.  There are number of emerging capabilities that will provide significant advances in accuracy and usability of hydrologic modeling and prediction products. These include:

  • Improve probabilistic spatial meteorological fields, to both improve the quality and probabilistic information content of hydrologic model inputs;
  • Advance model-agnostic methods to generate spatial fields of model parameters, to improve the fidelity of hydrologic model simulations;
  • Increase the computational agility of process-based hydrologic models, to support computationally intensive tasks such as hydrologic data assimilation and parameter estimation;
  • Build more robust hydrologic data assimilation capabilities to reduce errors in model initialization states;
  • Developing a broader understanding of tradeoffs in hydrologic prediction approaches at scales from flash flooding to seasonal forecasting
  • Utilize a new generation of meter scale terrain data from airborne lidar and incorporating that information into flow routing and inundation algorithms;
  • Improve the physics of snowpack accumulation and ablation;
  • Explore the use of remotely sensed meter scale snowpack products for model parameter estimation and data-assimilation.
  • Advance the representation of water management and infrastructure influences on runoff generation and streamflow;
  • Integrate more real-time information on land cover and land cover disturbance characteristics into real-time prediction systems.

These efforts are actively being worked to support a number of practical initiatives, including the NOAA National Water Model development effort, the Reclamation Reservoir Pilot Operations Study and West Wide Risk Assessment, and Over-the-Loop ensemble streamflow forecasting demonstration project, sub-seasonal to seasonal hydrologic and water supply forecasting, and longer-term 50-state water security assessment projects by federal agencies under the federal Secure Water Act.  As these tools and datasets mature they become publicly available and will be accompanied with documentation for how to use them to support adaptation planning and decision-making.

REFERENCES

Abramowitz, G.; Nadja Herger, Ethan Gutmann, Dorit Hammerling, Reto Knutti, Martin Leduc, Ruth Lorenz, Gavin A. Schmidt. 2018: Model dependence in multi-model climate ensembles: weighting, sub-selection and out-of-sample testing. Earth System Dynamics Discussion paper in review doi:10.5194/esd-2018-51

Arnal, L, AW Wood, E Stephens, H Cloke, F Pappenberger, 2016, Decomposing the sources of seasonal streamflow predictability, Hydrol. Earth Syst. Sci.

Bernhardt, M., Härer, S., Feigl, M., and Schulz, K. (2018). Der Wert Alpiner Forschungseinzugsgebiete im Bereich der Fernerkundung, der Schneedeckenmodellierung und der lokalen Klimamodellierung. Österreichische Wasserund Abfallwirtschaft.

Clark, M. P., B. Nijssen, J. D. Lundquist, D. Kavetski, D. E. Rupp, R. A. Woods, J. E. Freer, E. D. Gutmann, A. W. Wood, L. D. Brekke, J. R. Arnold, D. J. Gochis, and R. M. Rasmussen, 2015a: A unified approach for process-based hydrologic modeling: 1. Modeling concept. Water Resources Research, 51, 4, 2498-2514, doi: 10.1002/2015wr017198.

Clark, M. P., B. Nijssen, J. D. Lundquist, D. Kavetski, D. E. Rupp, R. A. Woods, J. E. Freer, E. D. Gutmann, A. W. Wood, D. J. Gochis, R. M. Rasmussen, D. G. Tarboton, V. Mahat, G. N. Flerchinger, and D. G. Marks, 2015b: A unified approach for process-based hydrologic modeling: 2. Model implementation and case studies. Water Resources Research, 51, 4, 2515-2542, doi: 10.1002/2015wr017200.

Clark, M. P., B. Nijssen, J. D. Lundquist, D. Kavetski, D. E. Rupp, R. A. Woods, J. E. Freer, E. D. Gutmann, A. W. Wood, and L. D. Brekke, 2015c: The Structure for Unifying Multiple Modeling Alternatives (SUMMA), version 1: Technical description. NCAR Technical Note NCAR/TN-514+STR, 54 pp., National Center for Atmospheric Research, Boulder, Colo., doi:10.5065/D6WQ01TD.

Clark, M. P., Y. Fan, D. M. Lawrence, J. C. Adam, D. Bolster, D. J. Gochis, R. P. Hooper, M. Kumar, L. R. Leung, and D. S. Mackay, 2015d: Improving the representation of hydrologic processes in Earth System Models. Water Resources Research, 51, doi: 10.1002/2015WR017096.

Clark, M. P., B. Schaefli, S. Schymanski, L. Samaniego, C. Luce, B. Jackson, J. Freer, J. R. Arnold, D. Moore, E. Istanbulluoglu, and S. Ceola, 2016a: Improving the theoretical underpinnings of process-based hydrologic models. Water Resources Research, 52, doi: 10.1002/2015WR017910.

Clark, M. P., R. L. Wilby, E. D. Gutmann, J. A. Vano, S. Gangopadhyay, A. W. Wood, H. J. Fowler, C. Prudhomme, J. R. Arnold, and L. D. Brekke, 2016b: Characterizing uncertainty of the hydrologic impacts of climate change. Current Climate Change Reports, 2, 2, 55-64, doi: 10.1007/s40641-016-0034-x.

Ekström M, Gutmann ED, Wilby RL, Tye MR, Kirono DGC. 2017: Robustness of hydroclimate metrics for climate change impact research. WIREs Water. e1288. doi:10.1002/wat2.1288

Emerton, R, EM Stephens, F Pappenberger, TC Pagano, AH Weerts, AW Wood, P Salamon, JD Brown, N Hjerdt, C Donnelly and HL Cloke, 2016.  Continental and Global Scale Flood Forecasting Systems, WIREs Water 3:391–418. doi: 10.1002/wat2.1137.

Gutmann, E., I. Barstad, M. Clark, J. Arnold, and R. Rasmussen, 2016: The Intermediate Complexity Atmospheric Research Model (ICAR). Journal of Hydrometeorology, 17, 2016, 957-973, doi: 10.1175/JHM-D-15-0155.1.

Hamman, J; ED Gutmann, N Mizukami, M Clark, A Wood. 2018: LOCA Hydrology Analysis. US Bureau of Reclamation Science & Technology Water Operations and Planning Monthly Webinar Series.

Horak J; Hoffer, Maussian, Gutmann, Gohm, Rotach (2018) Assessing the Added Value of the Intermediate Complexity Atmospheric Research Model (ICAR). Hydr.Earth Sys.Sci. (submitted)

Huang, C, AJ Newman, MP Clark, AW Wood and X Zheng, 2016, Evaluation of snow data assimilation using the ensemble Kalman Filter for seasonal streamflow prediction in the Western United States, Hydrol. Earth Syst. Sci.

Liu, C., K. Ikeda, R. Rasmussen, M. Barlage, G. Thompson, A. J. Newman, A. F. Prein, F. Chen, L. Chen, M. Clark, A. Dai, J. Dudhia, T. Eidhammer, D. Gochis, E. Gutmann, S. Kurkute, Y. Li, and D. Yates, 2016: The Current and Future Water Cycle over the Contiguous United States from Decadal Convection Permitting Simulations. Climate Dynamics, doi: 10.1007/s00382-016-3327-9

Madadgar, S, A AghaKouchak, S Shukla, S Sorooshian, K-L Hsu, M Svoboda, and AW Wood, 2016, A Hybrid Statistical-Dynamical Drought Prediction Framework: Application to for the Southwestern United States, Wat. Res. Rsrch (online early view) DOI: 10.1002/2015WR018547 

Melsen, L., N. Addor, N. Mizukami, A. J. Newman, P. Torfs, M. Clark, R. Uijlenhoet, and A. J. Teuling, 2018: Mapping (dis)agreement in hydrologic projections. HESS, 22, 1775-1791, doi:10.5194/hess-22-1775-2018

Mendoza, PA, AW Wood, E Rothwell, EA Clark, MP Clark, B Nijssen, LD Brekke, and JR Arnold, 2016, An intercomparison of approaches for harnessing sources of predictability in operational seasonal streamflow forecasting, HESS Discussions (submitted)

Mizukami, N., M. Clark, K. Sampson, B. Nijssen, Y. Mao, H. McMillan, R. Viger, S. Markstrom, L. Hay, and R. Woods, 2016b: mizuRoute version 1: a river network routing tool for a continental domain water resources applications. Geoscientific Model Development, 9, 2223-2238, doi: doi:10.5194/gmd-9-2223-2016.

Mizukami, N., M. Clark, E. Gutmann, P. A. Mendoza, A. Newman, B. Nijssen, B. Livneh, J. R. Arnold, L. Brekke, and L. Hay, 2016b: Implications of the methodological choices for hydrologic portrayals over the Contiguous United States: statistically downscaled forcing data and hydrologic models. Journal of Hydrometeorology 17, 73-98, doi: 10.1175/JHM-D-14-0187.1

Mizukami, N., M. Clark, A. Newman, A. Wood, E. Gutmann, B. Nijssen, O. Rakovec and L. Samaniego, 2017: Towards seamless large-domain parameter estimation for hydrologic models. Water Resources Research, doi:10.1002/2017WR020401.

Pagano, TC, F Pappenberger, AW Wood, MH Ramos, A. Persson and B Anderson, 2016, Automation and human expertise in operational river forecasting. WIREs Water, 3: 692–705. doi:10.1002/wat2.1163

Prein, A. F., G. J. Holland, R. M. Rasmussen, M. P. Clark, and M. R. Tye, 2016: Running dry: The US Southwest's drift into a drier climate state. Geophysical Research Letters, 43, 3, 1272-1279, doi: 10.1002/2015GL066727.

Rouson, D; ED Gutmann; A Fanfarillo; B Friesen 2017: Performance portability of an intermediate-complexity atmospheric research model in coarray Fortran. Proceedings of the Second Annual PGAS Applications Workshop, 4 SC17. doi:10.1145/3144779.3169104

Vano, J.A., Jeffrey R. Arnold, Bart Nijssen, Martyn P. Clark, Andrew W. Wood, Ethan D. Gutmann, Nans Addor, Joseph Hamman, Flavio Lehner. 2018: DOs and DON'Ts for using climate change information for water resource planning and management: guidelines for study design, Climate Services, doi:10.1016/j.cliser.2018.07.002.

Wobus, C., and Coauthors, 2017: Climate change impacts on flood risk and asset damages within mapped 100-year floodplains of the contiguous United States. Natural Hazards and Earth System Sciences, 17, 2199-2211, doi:10.5194/nhess-17-2199-2017.

Wood, A., T. Hopson, A. Newman, J. R. Arnold, L. Brekke, and M. Clark, 2016: Quantifying streamflow forecast skill elasticity to initial condition and climate prediction skill. Journal of Hydrometeorology 17, 651-668, doi: 10.1175/JHM-D-14-0213.1.

Zhao, T, J Bennett, QJ Wang, A Schepen, AW Wood, D Robertson and MH Ramos, 2016, How suitable is quantile mapping for post-processing GCM precipitation forecasts?  J. Climate.

Winter Weather

IDAHO POWER PROJECT

BACKGROUND

Figure 1. Terrain map of the SNOWIE project domain north of Boise, Idaho, illustrating the sites of ground-based instrument locations (see legend) as well as an example flight track for the Seeding Aircraft and University of Wyoming King Air, assuming conditions with westerly winds.  The Payette River Basin is outlined in thick gray, and was the target region for the SNOWIE field campaign.  From Tessendorf et al. (2019).
Figure 1. Terrain map of the SNOWIE project domain north of Boise, Idaho, illustrating the sites of ground-based instrument locations (see legend) as well as an example flight track for the Seeding Aircraft and University of Wyoming King Air, assuming conditions with westerly winds.  The Payette River Basin is outlined in thick gray, and was the target region for the SNOWIE field campaign.  From Tessendorf et al. (2019).

Idaho Power Company (IPC) conducts a winter cloud seeding program to augment snowfall along the Snake River Basin and its tributaries for hydroelectric generation. The program has been focused in Payette River basin in western Idaho and the upper Snake River system in eastern Idaho, and has recently expanded into the Boise and Wood basins in western Idaho.  RAL has been working with IPC since 2011 to develop model-based forecasting and evaluation tools for their cloud-seeding program.  In 2017, IPC, RAL and several universities collaborated on a field project called SNOWIE—Seeded and Natural Orographic Wintertime clouds: the Idaho Experiment.

The SNOWIE project aims to study the impacts of cloud seeding on winter orographic clouds.  The field campaign took place in Idaho between 7 January–17 March 2017 and employed a comprehensive suite of instrumentation, including ground-based radars and airborne sensors, to collect in situ and remotely-sensed data in and around clouds containing supercooled liquid water before and after they were seeded with silver iodide aerosol particles (Figure 1). Seeding material was released primarily by a seeding aircraft, which produced zig-zag lines of silver iodide as it dispersed downwind.  In several cases, unambiguous zig-zag lines of radar reflectivity were detected by radar, and in situ measurements within these lines have been analyzed to examine the microphysical response of seeding the cloud (Figure 2).  The measurements from SNOWIE aim to address long-standing questions about the efficacy of cloud seeding, starting with documenting the physical chain of events following seeding.  The data is also being used to evaluate and improve computer modeling parameterizations, including the cloud-seeding parameterization developed in RAL that will be used to further evaluate and quantify the impacts of cloud seeding.  

29 UTC. The wind barbs indicate mean flight-level winds. From Tessendorf et al. (2019).
Figure 2. PPI scans (0.99-degree elevation angle) at 0109 UTC (left) and 0137 UTC (right) from the Packer John DOW radar. The red line denotes the track of the seeding aircraft. The track was repeated 8 times between 00:03 and 01:29 UTC. The wind barbs indicate mean flight-level winds. From Tessendorf et al. (2019).

In FY19, RAL worked on a “Phase Eight” study for IPC to conduct data analysis and modeling research from SNOWIE. This has included specific investigation on quantifying the amount of precipitation produced by cloud seeding in three SNOWIE cases with unambiguous seeding lines using SNOWIE observations, simulating those cases to compare the model with the SNOWIE observations, studying ice generating cells in SNOWIE data, and the evaluation of the ability of model re-analysis and high-resolution simulations to replicate the layered clouds observed in SNOWIE.

FY2019 ACCOMPLISHMENTS

In 2019, RAL conducted SNOWIE modeling and data analysis on the effectiveness of cloud seeding and precipitation formation processes in the clouds observed over Idaho.  Components of this effort included:

  • analyzing snow gauge data and radar data in cases from SNOWIE where unambiguous seeding lines were observed;
  • running model simulations of cases from SNOWIE where unambiguious seeding lines were observed;
  • comparing model simulation results with observations, such as radiometer, sounding, snow gauge, and aircraft data from SNOWIE;
  • analyzing the in situ aircraft data and airborne W-band cloud radar data for the presence and characteristics of ice generating cells and layered cloud structures,
  • evaluating the ability of the WRF model and ERA-5 reanalysis to resolve the observed layered cloud structures, and
  • collaborating with the RAL In Flight Icing team to use SNOWIE data for evaluation of the HRRR forecast model.

 

These efforts have yielded several publications to date, with several more in preparation. In FY19, an article summarizing the SNOWIE field campaign, led by Dr. Sarah Tessendorf, was published in the Bulletin of the American Meteorological Society (BAMS).  Another collaborative manuscript, led by Dr. Bob Rauber, was published in FY19 in the Journal of Applied Meteorology and Climatology entitled “Wintertime Orographic Cloud Seeding--A Review”.  It provides a modern review of the science regarding cloud seeding, including the latest advances from SNOWIE. A third paper, led by Dr. Katja Friedrich, was submitted to Proceedings of the National Academy of Science (PNAS) shows results that quantify the impact of cloud seeding using snow gauge measurements and radar derivations of precipitation rates.  A fourth paper was also written and will be ready for submission in early FY20 on the detailed microphysics of the three cases with unambiguous seeding lines, which builds upon French et al. (2018), which was published in PNAS last year.

 Climatology uses the climatology background aerosol, 01CCN uses 0.1 times the climatology for CCN, and CTRL is the control simulation that uses 0.15 times the climatology for CCN.
Figure 3. The observed cloud droplet concentration (top) and liquid water content (bottom) for SNOWIE IOP9 on 31 January 2017 compared to 3 model simulations: Climatology uses the climatology background aerosol, 01CCN uses 0.1 times the climatology for CCN, and CTRL is the control simulation that uses 0.15 times the climatology for CCN.

The SNOWIE modeling simulations conducted in FY19 continue to reveal a strong sensitivity of the simulated cloud microphysical characteristics to the amount of background ice produced by the model as well as to the background aerosol in the model (Figure 3). Analyses to improve the model in this regard is underway and will be a major focus for FY20 and going forward.  A simulation of one seeding case with unambiguous seeding lines was conducted with temporal model output being generated every 5 minutes to better resolve the temporal evolution of the observed seeding lines.  Analysis is underway to evaluate this simulation compared to the observations and run it at even higher horizontal grid spacing using Large Eddy Simulation (LES).

FY2020 PLANS

  • Conduct additional data analysis and model simulations from the SNOWIE field project and collaborate with SNOWIE university PIs to perform analysis on high priority cases
  • Simulate SNOWIE cases at even finer resolution with Large Eddy Simulations (LES) to evaluate the impacts of grid resolution on the resulting cloud characteristics
  • Perform detailed case study data analyses and model simulations to improve the production of supercooled liquid water and ice in the model, as well as improve the cloud seeding model, especially with regard to ice production and dispersion of seeding material in the seeding model compared to the observations from SNOWIE
  • Work with IPC to develop new and/or augmented observational networks for continued monitoring, forecasting assistance, and evaluation of their cloud-seeding program
  • Publish journal papers on the major findings from these studies.

 

Education and Outreach on the State of Cloud Seeding Science

In FY19, the U.S. Bureau of Reclamation (USBR) sponsored RAL scientists to develop training material on the latest state of science regarding cloud-seeding research, evaluation capabilities, and understanding of relevant processes.  The training material consists of presentation slides and a 1-page fact sheet handout.  The trainings will be held at each of the five USBR regional offices over the course of the next year.

The training material consists of a historical background of cloud seeding research, as well as background material on the conceptual model of cloud seeding, with recent results from the research coming out of SNOWIE and other advances in the field that NCAR is leading. 

FY2020 PLANS

  • Finalize training material
  • Conduct training sessions at five USBR regional offices

 

Measurements of snow at the Marshall Field site

A long term snow measurement site has been maintained at the NCAR Marshall field site since the early 1990’s.  The site was used to support research related to ground deicing in the 1990’s and 2000’s, and supported the Solid Precipitation Inter-Comparison Experiment (SPICE) in the 2010’s.   NOAA supported the SPICE effort as well as testing and developing new instruments for its Climate Reference Network (CRN).  This year NOAA supported testing efforts regarding a new wind shield design for CRN.  The NCAR Water System program is supporting testing and evaluation of a new hotplate sensor built and designed by Pond Engineering.

This site is able to serve as a reference site due to the deployment of a WMO standard snow reference system called the Double Fence Intercomparision Reference (DFIR) system.  The main features of this system are the use of double fences of outer diameter 40 feet and inner diameter 25 feet, with an automated gauge in the center.  The automated gauge used is an Alter shielded GEONOR.   A key aspect of this site is the archival of the data on an efficient database and the ability to examine the data in real-time based on a web based graphical interface. 

Land Atmosphere Interactions

BACKGROUND

The main objectives of the land-atmosphere interaction and modeling group are to understand, through theoretical and observational studies, the complex interactions (including biophysical, hydrological, and biogeochemical) between the land surface and the atmosphere across a wide range of temporal and spatial scales, and landscapes. The ultimate goal is to improve the community Terrestrial System Model (CTSM), Noah, and Noah-MP land-surface models and to integrate such knowledge into numerical mesoscale weather prediction and regional climate models to improve prediction of the impacts of land-surface processes on regional weather, climate, and water systems. These R&D efforts are results of collaborations with domestic and international partners, and sponsored by the NCAR Water System, and research grants from NSF, USDA, NOAA, and Institute of Urban Meteorology.

UNDERSTANDING AND MITIGATING UNCERTAINTIES IN LAND MODELS, AND IMPROVING THE COMMUNITY NOAH-MP LAND-SURFACE MODEL

Modeling land-surface processes over the complex-terrain Tibetan Plateau (TP) is a challenging problem. We contributed to the scientific design of the Third Tibetan Plateau Atmospheric Scientific Experiment (TIPEX-III) field campaign (Zhao et al. 2018). We used its surface and boundary layer observations to investigate the effects of surface heterogeneity on the surface energy budgets (Xin et al. 2018) and to assess the uncertainties in the Noah-MP land surface model simulations over the Central Tibetan regions (Li et al. 2018). We quantified the effects of grain shape and multiple black carbon (BC)‐snow internal mixing on snow albedo by explicitly resolving shape and mixing structures (He et al. 2018a) and applied the updated SNICAR snow model with observed BC concentrations in the Tibetan Plateau snowpack to quantify the present-day (2000–2015) BC-induced snow albedo effects from a regional and seasonal perspective (He et al. 2018b).

 Spatial distribution of seasonal mean FSC over the QTP region from the 0.04° FY-3B (2012–17), MODIS (2002–17), and 4-km IMS (2006–17) snow-cover data. The FSC of IMS is the ratio between the snow days and the total days in a specific season. Jiang et al. (2019).
Figure 1. Spatial distribution of seasonal mean FSC over the QTP region from the 0.04° FY-3B (2012–17), MODIS (2002–17), and 4-km IMS (2006–17) snow-cover data. The FSC of IMS is the ratio between the snow days and the total days in a specific season. Jiang et al. (2019).

We conducted a number of studies using the physics ensemble simulations with Noah-MP and developed a method of assessing and mitigating the uncertainty range of Noah-MP simulations using data obtained from TIPEX-III and global FluxNet, and to improve the Noah-MP LSM (Li et al. 2018, 2019; Gan et al. 2019; Yimam et al. 2019; Zhang et al. 2018, 2019; Chen et al. 2019; Brunsell et al. 2019). The results show that three subprocesses—surface exchange coefficient, runoff and groundwater, and surface resistance to evaporation—have the most significant impacts on the performance of the simulated sensible heat flux, latent heat flux, and net absorbed radiation in the Noah‐MP LSM. The interaction between two subprocesses could contribute up to 50% of the variation in model performance for some sites, which highlights the need for considering he interactions of subprocesses to improve LSMs.

Snow cover in the Qinghai–Tibet Plateau (QTP) is a critical component in the water cycle and regional climate of East Asia. We evaluated fractional snow cover (FSC) derived from five satellite sources (the three satellites comprising the multisensor synergy of FengYun-3 (FY-3A/B/C), the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Interactive Multisensor Snow and Ice Mapping System (IMS)) over the QTP to examine uncertainties in mean snow cover and interannual variability over the last decade. A four-step cloud removal procedure was developed for MODIS and FY-3 data, which effectively reduced the cloud percentage from about 40% to 2%–3% with an error of about 2% estimated by a random sampling method. The cloud-removed FY-3B data have an annual classification accuracy of about 94% for both 0.04° and 0.01° resolutions, which is higher than other datasets and is recommended for use in QTP studies. Among the five datasets analyzed, IMS has the largest snow extent (22% higher than MODIS) and the highest FSC (4.7% higher than MODIS), while the morning-overpass MODIS and FY-3A/C FSC are similar and are around 5% higher than the afternoon-overpass FY-3B FSC (see Figure 1, Jiang et al. 2019).

Publications

Zhao, P., X. Xu, F. Chen, X. Guo, X. Zheng, L. Liu, Y. Hong, Y. Li, Z. La, H. Peng, L. Zhong, Y. Ma, S. Tang, Y. Liu, H. Liu, Y. Li, Q. Zhang, Z. Hu, J. Sun, S. Zhang, L. Dong, H. Zhang, X. Yan, A. Xiao, X. Zhou, 2018: The Third Tibetan Plateau Atmospheric Scientific Experiment (TIPEX-III): An Integrated Land-Troposphere-Stratosphere Observation Network. Bull. Amer. Meteor. Soc., https://doi.org/10.1175/BAMS-D-16-0050.1

Gao, Y., F. Chen, D. Lettenmaier, L. Xiao, X. Li, 2018: Does the elevation-dependent warming still hold true above 5000m altitude? npj Atmospheric Science and Climate, DOI: 10.1038/s41612-018-0030-z.  

Xin, Y., F. Chen; P. Zhao; M. Barlage; Y-L Chen; B. Chen; Y-J Wang, 2018: Surface Energy Balance Closure at ten Sites over the Tibetan Plateau and Implication to Land Modeling. Agricultural and Forest Meteorology, 259, 317-328. 

He, C., K. N. Liou, Y. Takano, P. Yang, L. Qi, and F. Chen, 2018a: Impact of grain shape and multiple black carbon internal mixing on snow albedo: parameterization and radiative effect analysis, J. Geophys. Res.-Atmos, 123. https://doi.org/10.1002/2017JD027752

He, C., M. Flanner, F. Chen, M. Barlage, K.-N. Liou, S. Kang, J. Ming, and Y. Qian, 2018b: Black carbon-induced snow albedo reduction over the Tibetan Plateau: Uncertainties from snow grain shape and aerosol-snow mixing state based on an updated SNICAR model. Atmospheric Chemistry and Physics,18, 11507-11527, https://doi.org/10.5194/acp-18-11507-2018

Li, J., F. Chen, G. Zhang, M. Marlage, Y. Gan, Y. Xin, W. Chen, 2018: Impacts of land cover and soil texture uncertainty on land model simulations over the central Tibetan Plateau. J. Adv. Model. Earth Syst., https://doi.org/10.1029/2018MS001377. 

Nayak, H., K. K. Osuri, P. Sinha, U. Mohanty, F. Chen, M. Rajeevan, and D. Niyogi, 2018: High resolution gridded soil moisture and soil temperature datasets for the Indian monsoon region. Scientific Data, 5, 180264. 10.1038/sdata.2018.264

Li, J., G. Zhang, F. Chen, X. Peng, and Y. Gan, 2019: Evaluation of land surface sub-processes and their impacts on model performance with global flux data. J. Adv. Model. Earth Syst., https://doi.org/10.1029/2018MS001606.

Gan, Y., X. Liang, Q. Duan, F. Chen, J. Li, 2019: Assessment and Reduction of the Physical Parameterization Uncertainty for Noah-MP Land Surface Model. Water Resources Research, https://doi.org/10.1029/2019WR024814.

Jiang, Y., F. Chen, Y. Gao, M. Barlage; J. Li, 2019: Using multi-source satellite data to assess recent snow-cover change in the Qinghai-Tibet Plateau and its uncertainty. J. Hydromet, https://doi-org.cuucar.idm.oclc.org/10.1175/JHM-D-18-0220.1

Brunsell, N., G. de Oliveira, M. Barlage, Y. Shimabukuro, E. Moraes, and L. Aragao, 2019: Examination of seasonal water and carbon dynamics in eastern Amazonia. Submitted to Theor. Appl. Clim.

Yimam, Y., C. Morgan, M. Barlage, B. Dornblaser, J. Gross, D. Gochis, H. Neely and A. Kishne, 2019: Evaluation of Noah-MP performance with improved soil information. Submitted to J. Hydrometeor.

Zhang, Z., Y. Li, M. Barlage, F. Chen, G. Miguez-Macho, A. Ireson and Z. Li, 2019: Simulating groundwater responses to climate change in North America’s Prarie Pothole Region, Clim. Dyn., in review.

Chen, L., Y. Li, F. Chen, M. Barlage, Z. Zhang, and Z. Li, 2019: Using 4-km WRF CONUS Simulations to diagnose surface coupling strength, Clim. Dyn., https://doi.org/10.1007/s00382-019-04932-9.

Zhang, Z., Y. Li, F. Chen, M. Barlage, and Z. Li, 2018: Evaluation of convection-permitting WRF CONUS simulation on the relationship between soil moisture and heatwaves. Climate Dynamics, pp.1-18, Clim. Dyn., http://doi.org/10.1007/s00382-018-4508-5.

DEVELOPING THE WRF-URBAN MODELING SYSTEM AND APPLYING IT TO ADDRESS URBAN ENVIRONMENTAL ISSUES

The global population has become increasingly urbanized; to date 52% of the world’s population live in cities, and this proportion is projected to increase to 67% by 2050. Urbanization modifies surface energy and water budgets, and has significant impacts on local and regional hydroclimates. The primary goal of developing WRF-Urban model is to provide the research community a useful modeling tool to address urban environmental issues. We have augmented the existing WRF-Urban capabilities by coupling its three urban canopy models (UCMs) with the new community Noah with the Noah‐MP LSM. The WRF‐Urban modeling system's performance were evaluated for two major cities of Arizona: Phoenix and Tucson metropolitan area in a semiarid urban environment. The results show that Noah‐MP reproduces somewhat better than Noah the daily evolution of surface skin temperature and near‐surface air temperature (especially nighttime temperature) and wind speed. Regarding near‐surface wind speed, only the multilayer UCM was able to reproduce realistically the daily evolution of wind speed, although maximum winds were slightly overestimated, while both the single‐layer and bulk urban parameterizations overestimated wind speed considerably. This paper demonstrates that the new community Noah‐MP LSM coupled to an UCM is a promising physics‐based predictive modeling tool for urban applications (Salamanca et al. 2018).

We collaborated with scientists at the Institute of Urban Meteorology to design the Study of Urban Impacts on Rainfall and Fog/Haze (SURF) field campaign in Beijing to improve understanding of urban, terrain, convection, and aerosol interactions for improved forecast accuracy (Liang et al. 2018). The data collected from SURF were used to evaluate WRF-Urban using three urban canopy models (the single-layer UCM, and the multi-layer BEP and BEM models) and four planetary boundary layer (PBL) schemes (the non-local first-order YSU, SH and ACM2 schemes, as well as the local TKE-based BouLac scheme for selected cloudy and clear-sky cases days. Results show that the WRF-Urban simulated 2-m temperature and 10-m wind speed are more sensitive to UCMs than to PBL schemes. The convective boundary layer (CBL) from the single-layer UCM experiment develops at the slowest pace when compared with other two multi-layer UCMs (Huang et al. 2018).

The WRF-Urban was applied to investigate the influence of sea breeze (SB) propagation on the development of the urban boundary layer (UBL) in the Metropolitan Region of Sao Paulo (MRSP), Brazil (Ribeiro et al. 2018).  Results show that the propagation of the SB front disrupts the convective growth of the UBL and establishes a thermal internal boundary layer, thereby reducing the UBL height. A capability was developed to run the urban model decoupled from a full atmosphere model to efficiently perform climate change mitigations strategies (Gao et al. 2019). Xu et al. (2019) examined the impact of air conditioning (AC) electric loads on local weather over Beijing during a 5-day heat wave event in 2010 by using WRF-Urban with the multilayer Building Effect Parameterization and Building Energy Model (BEP+BEM). The simulated AC electric loads in suburban and rural districts are significantly improved by introducing the urban class‐dependent building cooled fraction (Figure 2). Analysis reveals that the observed AC electric loads in each district are characterized by a common double peak at 3 p.m. and at 9 p.m. local standard time, and the incorporation of more realistic AC working schedules helps reproduce the evening peak. Influences of AC systems can only reach up to ~400 m above the ground for the evening air temperature and humidity due to a shallower urban boundary layer than daytime. Spatially varying maps of AC working schedules and the ratio of sensible to latent waste heat release are critical for correctly simulating the cooling electric loads and capturing the thermal stratification of urban boundary layer.

 10 MW) for (a) Chaoyang, and (b) Huairou.
Figure 2. Time series of observed and modeled with the introduction of the building cooled fraction (MP-COOLED-FRC) and with a new AC-usage schedule (MP-AC-SCHEDULE) air-conditioning electric loads (unit: 10 MW) for (a) Chaoyang, and (b) Huairou.

Wu et al. (2019) examined, for the first time, the extreme hourly precipitation changes in the coastal South China using rain-gauge observations during 1971–2016, because sub-daily extreme rainfall is important for engineering practices and urban infrastructure design for mitigating urban flood. This study found a statistically significant increase of hourly precipitation intensity (leading to higher annual amounts of both total and extreme precipitation over the Pearl River Delta (PRD) urban cluster in the rapid urbanization period (~1994–2016) than during the pre-urbanization era (1971 to 1993). More importantly, the 120 hourly-extreme-rainfall events in the last 15 years are clearly related to strong urban heat islands (UHI) effect for a wide range of synoptic backgrounds and seasons, as shown in Figure 3. These new findings provide further evidence that UHI-induced dynamical and thermal perturbations play an important role in the convection initiation and intensification of the locally developed extreme-rain-producing storms. 

 (a) local/SW wind type, (b) local/shear line type, (c) migratory-NW type, (d) migratory-SW type, (e) migratory-NE type, and (f) migratory-S type. Pink and blue bars, respectively, represent the strong- and weak-UHI events. The number of events within each subtype is shown in parentheses. From Wu et al. (2019).
Figure 3. Seasonality of the 2011-16 extreme rainfall events, classified by six weather types: (a) local/SW wind type, (b) local/shear line type, (c) migratory-NW type, (d) migratory-SW type, (e) migratory-NE type, and (f) migratory-S type. Pink and blue bars, respectively, represent the strong- and weak-UHI events. The number of events within each subtype is shown in parentheses. From Wu et al. (2019).

Publications

Salamanca, F., Zhang Y., M. Barlage, F. Chen, A.Mahalov, and S. Miao, 2018: Evaluation of the Noah-MP land surface model coupled to WRF in a semiarid urban environment. J. Geophys. Res., DOI: 10.1002/2018JD028377

Liang, X., S. Miao, et al., 2018: SURF: understanding and predicting urban convection and haze. Bull. Amer. Meteor. Soc., https://doi.org/10.1175/BAMS-D-16-0178.1

Xu, X., F. Chen, S. Shen, S. Miao, M. Barlage, W. Guo, and A. Mahalov, 2018: Using WRF-Urban to assess summertime air conditioning electric loads and their impacts on urban weather in Beijing. J. Geophys. Res., 123, https://doi.org/10.1002/2017JD028168

Ribeiro, F., A. de Oliveira, J. Soares, R. de Miranda, M. Barlage and F. Chen, 2018: Characterization of sea breeze circulation effects on the urban boundary layer of the metropolitan region of Sao Paulo, Brazil, Atmospheric Research, 214, 174-188.

Sharma, A., S. Woodruff, M. Budhathoki, H.J.S. Fernando, A. Hamlet, and F. Chen, 2018: Role of green roofs in reducing heat stress in vulnerable urban communities - A multidisciplinary approach. Environ. Res. Lett., 13(9), 094011.

Huang, M., Z. Gao, S. Miao, F. Chen, 2018:Sensitivity of Urban Boundary Layer Simulation to Urban Canopy Models and PBL Schemes over Beijing. Meteorology and Atmospheric Physics, https://doi.org/10.1007/s00703-018-0634-1.

Ching, J., G. Mills, et al., 2018: World Urban Database and Access Portal Tools (WUDAPT), an urban weather, climate and environmental modeling infrastructure for the Anthropocene. Bull. Amer. Meteor. Soc., 99(9), 1907-1924

Ching, J., et al., 2019: Pathway using WUDAPT's Digital Synthetic City tool towards generating urban canopy parameters for multi-scale urban atmospheric modeling. Urban Climate, https://doi.org/10.1016/j.uclim.2019.100459 

Wu, M., Y. Luo, F. Chen, W.K. Wong, 2019: Observed Link of Extreme Hourly Precipitation Changes to Urbanization over Coastal South China. J. Appl. Meteorol. Climatol., 58(8), 1799-1819.

Xu, X. F. Chen, S. Shen, S. Miao, M. Barlage, W. Guo and A. Mahalov, 2018: Using WRF-Urban to assess summertime air conditioning electric loads and their impacts on urban weather in Beijing, J. Geophys. Res., 123, 2475-2490, doi:10.1002/2017JD028168.

Gao, M., F. Chen, H. Shen, M. Barlage, H. Li, Z. Tan, and L. Zhang, 2019: Trade-offs of possible strategies to mitigate the urban heat island based on u-HRLDAS, J. Meteor. Soc. Jap., 97,  https://doi.org/10.2151/jmsj.2019-060. 

DEVELOPING THE WRF-CROP MODELING SYSTEM  

The overarching goal for developing the WRF-Crop model is to capture fine-scale hydrology, agriculture, weather, and climate interactions, based on the coupling of crop-growth models (Figure 4) with the Noah-MP LSM (Noah-MP-Crop, Liu et al. 2016, JGR-Atmosphere), because of the significant coverage of croplands (12.6% of the global land and 19.5% of the continental United States) and its role in influencing regional weather.

Fig. 4. Flowchart of the Noah-MP-Crop model. (“LAI” and “leaf mass” marked red for emphasizing the calculation process and the role of LAI in the model simulations.
Figure 4. Flowchart of the Noah-MP-Crop model. (“LAI” and “leaf mass” marked red for emphasizing the calculation process and the role of LAI in the model simulations. 

Agriculture irrigation modifies land-surface water and energy budgets. Chen et al. (2018) used long-term data collected from two contrasting (irrigated and rainfed) nearby maize-soybean rotation fields, to study the effects of irrigation memory on local hydroclimate. For a 12-year average, irrigation decreases summer surface-air temperature by less than 1 °C and increases surface humidity by 0.52 g kg−1. The irrigation cooling effect is more pronounced and longer lasting for maize than for soybean. Irrigation reduces maximum, minimum, and averaged temperature over maize by more than 0.5 °C for the first six days after irrigation, but its temperature effect over soybean is mixed and negligible two or three days after irrigation (Figure 5). Irrigation increases near-surface humidity over maize by about 1 g kg−1 up to ten days and increases surface humidity over soybean (~ 0.8 g kg−1) with a similar memory. These differing effects of irrigation memory on temperature and humidity are associated with respective changes in the surface sensible and latent heat fluxes for maize and soybean. These findings highlight great need and challenges for earth-system models to realistically simulate how irrigation effects vary with crop species and with crop growth stages, and to capture complex interactions between agricultural management and water-system components (crop transpiration, precipitation, river, reservoirs, lakes, groundwater, etc.) at various spatial and temporal scales.

Fig 5.  The x-axis represents days from an irrigation application with amount > 7.5 mm day−1. The y-axis represents the differences in daily Tmin (°C, top), Tmax (°C, middle) and Tave (°C, bottom) between USNe2 and USNe3. Samples were taken from all irrigation events from 2001−2012 and the red stars represent their averaged values for a given day after irrigation. From Chen et al. (2018).
Figure 5.  The x-axis represents days from an irrigation application with amount > 7.5 mm day−1. The y-axis represents the differences in daily Tmin (°C, top), Tmax (°C, middle) and Tave (°C, bottom) between USNe2 and USNe3. Samples were taken from all irrigation events from 2001−2012 and the red stars represent their averaged values for a given day after irrigation. From Chen et al. (2018).

Xu et al. (2019) incorporated a dynamic irrigation scheme into Noah-MP and investigated three methods of determining crop growing season length by agriculture management data. The irrigation scheme was assessed at field scales. Results show that crop‐specific growing‐season length helped capture the first application timing and total irrigation amount, especially for soybeans. When transitioning from field to regional scales, the county‐level calibrated IRR_CRI helped mitigate overestimated (underestimated) total irrigation amount in southeastern Nebraska (lower Mississippi River Basin). In these two heavily irrigated regions, irrigation produced a cooling effect of 0.8–1.4 K, a moistening effect of 1.2–2.4 g/kg, a reduction in sensible heat flux by 60–105 W/m2, and an increase in latent heat flux by 75–120 W/m2. Most of irrigation water was used to increase soil moisture and evaporation, rather than runoff. Lacking regional‐scale irrigation timing and crop‐specific parameters makes transferring the evaluation and parameter‐constraint methods from field to regional scales difficult.

Publications

Chen, F., X. Xu, M. Barlage, R. Rasmussen, S. Shen, S. Miao, G. Zhou, 2018:  Memory of irrigation effects on hydroclimate and its modeling challenge. Environ. Res. Lett., https://doi.org/10.1088/1748-9326/aab9df

Xu, X., F. Chen, M. Barlage, D. Gochis, S. Miao, and S. Shen, 2019: Lessons learned from modeling irrigation from field to regional scales. J. Adv. Model. Earth Syst., DOI:10.1029/2018MS001595 

2019 PLANS

Improve the agriculture modeling in the CONUS convection-permitting regional climate model for the GEWEX Water for Foodbaskets.

Continue to improve the Noah-MP land model and agriculture management modeling in the National Water Model.

Climate and Managed Water Systems

FY2019 ACCOMPLISHMENTS

The Moffatt tunnel, which brings trans basin water from the west side of the continental divide into the South Platte River basin where it serves agriculture and municipal water uses for the population of the Colorado Front Range.
The Moffatt tunnel, which brings trans basin water from the west side of the continental divide into the South Platte River basin where it serves agriculture and municipal water uses for the population of the Colorado Front Range.
 

Municipal Water Systems

Calibrated and Validated the WEAP Headwaters Model through the extension of the domain to include the South Platte River Basin, its tributaries, and the water supply and demand elements in this domain. Developed detailed water demand elements along the Colorado Front Range, and refined analysis for Colorado Springs Utilities and Denver Water.

With this model, we analyzed two water systems of particular interest for these utilities, including the Upper Blue River and the Fraser River, where infrastructure planning in the face of future climate uncertainty was explored. The figure on the right shows the Moffatt tunnel, where the utility is making investments and asking if current capacities of pipelines are adequate. (Moffatt Figure)

We trained of Denver Water and Colorado Springs Utilities staff on the use and application of the WEAP-HW model. Training was conducted throughout the year; and included how to build water systems models and incorporate current and future climate projections. The figure below shows the Fraser River basins and the Denver Water collection system. (Fraser Figure)

Climate Change and Lake Tanganyika

As part of a World Bank Project,we deployed a Regional Climate Modeling (RCM) over the broader domain of the East Africa Great Lakes and Ethiopia (EAGLE) to understand how rainfall, winds, temperature, moisture, etc. may change across the region in response to global climate change. In addition to running the regional climate simulation, an integrated hydrological model of the river systems that feed Lake Tanganyika and a water balance model of the lake itself has been developed based on the Water Evaluation and Planning (WEAP) system. The WEAP model is used to simulate streamflow and other hydrologic variables on a monthly basis for a 65 year historic period (1950 to 2015) and to the end of the 21st century with forcing from a collection of 12 Global Climate Model (GCM) datasets and the WRF dataset.  The figure below shows the graphical user interface for the WEAP-Lake Tanganyika model, with the time series of the historic lake levels included.

Climate Smart Agriculture

For a World Bank Project on climate smart agriculture, we developed an agriculture sector model for the South African state of Lesotho, where we explored drivers of change that affect agricultures metrics that include productivity, resilience, and mitigative capacity.

 

Schematic of the Water Evaluation and Planning (WEAP) decision support tool used to explore the vulnerability of the Denver Water collection system of the Fraser River to climate variability and change.
Schematic of the Water Evaluation and Planning (WEAP) decision support tool used to explore the vulnerability of the Denver Water collection system of the Fraser River to climate variability and change. 

PLANS FOR FY2020

US Water Systems

Through a new DOE project, we will continue to advance the SW WEAP model for water-energy nexus analysis. We are working collaboratively with the Lawrence Berkley National LAB on this effort, where we will explore in depth, how hydropower and energy demand influences electric energy investment strategies into the future across the southwestern US.

Continued training session for water utility staff on the use of the use of WEAP-HW

Finish a peer review paper with utilities on seasonal forecasting work; and the use of the WEAP-HW model in their integrated water resource planning process (IWRP).

Continue to work with Denver Water and Colorado Springs Utilities to advance WEAP-HW model to extend the South Platte portion of the model to the Colorado-Nebraska border.

Improvements and new applications will include:

  • We will continue to work with Denver Water and Colorado Springs Utilities to develop and apply weather typing to explore current and future extreme events in Denver Water’s supply and demand regions, and then demonstrate how changes in extremes might impact Denver Water through simulation with the WEAP-HW water systems model.
  • We are analyzing the how the regional climate modeling projections done as part of the Water Cycle project manifest themselves in the form of hydrology changes in the Upper Colorado River basin. This includes the regional climate simulations of the “historic” climate and those derived from the Pseudo Global Warming (PGW) experiments.
  • Addition of the water infrastructure of the new elements of the South Platte Basin and the Upper Colorado, such as local reservoirs and diversions. There is particular interest in looking at conditional water rights within the context of climate variability and change.

 

The WEAP schematic of the Lake Tanganyika region, showing the historic time series of lake levels from 1950 to 2016.
The WEAP schematic of the Lake Tanganyika region, showing the historic time series of lake levels from 1950 to 2016.

Water Resources and Mining

As part of the environment stewardship program Newmont-Goldcorp is working with NCAR to develop water systems models for select mine sites in order to advance their watershed-based targets in the face of future climate change. The project will explore the risks and opportunities that exist within the local and regional watersheds, where we will model the physical, regulatory and reputation risks using WEAP. To provide additional context on how water risks translate to operations, NCAR will work with Newmont to frame the challenges and opportunities for water management adaptation for select sites.

Lake Tanganyika

We will be conducting a regional climate modeling workshop with the four nation state of Lake Tanganyika that are part of the Lake Tanganyika Authority (Burundi, Tanzania, Zambia and the Democratic Republic of the Congo). The workshop will include presentations and training on the climate change science explored in the project, the climate change scenarios developed, and hands-on training on the use and application of the WEAP model developed as part of the project. (Lake Tang WEAP figure)

Hydrometeorological Observations

Background

Scientists in RAL’s Hydrometeorological Applications Program (HAP) are actively engaged in numerous observational studies aimed at improving the understanding of critical processes that control various linkages in the water cycle.  In the last few years, staff have engaged in several field observation efforts focused on winter precipitation, snowpack, snowmelt and runoff.  In addition to requiring a comprehensive scientific research strategy, these projects demand significant integration of instrument engineering and field work skills to collect research-quality data in Colorado’s extreme mountain environments.

High-elevation monitoring for snowpack and water supply predictions

 HydroInspector web mapping service display of observation stations (white circles) and observation and model time series (right hand side time series plots) from the Upper Rio Grande observation and modeling project.
Figure 1. HydroInspector web mapping service display of observation stations (white circles) and observation and model time series (right hand side time series plots) from the Upper Rio Grande observation and modeling project.

In 2017 a network of snowpack, soil moisture, near surface meteorological and stream water level measurement stations was maintained in the Conejos River basin in southern Colorado (Fig 1 ). White circles indicate location of NCAR measurement stations, blue squares are NRCS SNOTEL stations, green triangles are Colorado Division of Water Resources streamflow stations).  These stations were deployed in 2014 as part of the inter-agency Rio-SNO-FLO project which is performing observational and modeling based research aimed at improving the characterization and prediction of snowpack and seasonal water supplies in the headwaters of the Upper Rio Grande.  This work is being done in collaboration with the Conejos Water Conservancy District, the State of Colorado, the NOAA Severe Storms Laboratory and NASA’s Jet Propulsion Laboratory.  Research conducted during 2017 documented the performance of observed vs. modeled snowpack depth and near-surface temperature, humidity, and incoming solar radiation.  These results are summarized in a report to the State of Colorado (Gochis et al., 2016; Karsten et al. 2017).  The principal outcomes of this work were that research radars possessed significant skill in estimating mountain snowfall as validated by surface precipitation gauges in the southern Colorado region and that when used to drive a physics-based hydrologic model, resulting snowpack and streamflow simulations were significantly improved over simulations using background national analyses of precipitation. Additionally, it was also found that direct insertion of airborne lidar-derived estimates of snowpack across the basin into a high-resolution hydrologic model had a direct and positive impact on snowmelt-driven seasonal water supply forecasts.  As a result, the State of Colorado is currently exploring financial alternatives to purchasing and deploying a gap-filling radar in the Upper Rio Grande basin.

Gochis, D.J, K. Howard, J. Busto, J. Deems, N. Coombs, L. Tang, I. Maycumber, K. Bormann, L. Karsten, A. Dugger, N. Langley, J. Mickey, T. Painter, M. Rchardson, and S.M. Skiles, 2016: Upper Rio Grande Basin Snowfall Measurement and Streamflow (RIO-SNO-FLOW) Forecasting Improvement Project. Project report submitted to the Colorado Water Conservation Board. Available online at: http://cwcb.state.co.us/public-information/publications/Pages/StudiesRep....

Karsten, L., D.J. Gochis, A. Dugger, K. Howard, L. Tang, J. Deems, T. Painter, G. Fall, C. Olheiser, 2017: Assessing the impact of operational meteorological forcings and experimental radar snowfall estimates on simulated snowpack conditions in the headwaters of the Upper Rio Grande River basin, In preparation.

Recent Accomplishments

In collaboration with the Upper Gunnison River Water Conservancy District (UGRWCD) and the Natural Resources Conservation Service (NRCS) NCAR installed 4 new SNOTEL-Lite stations in the Upper Taylor River basin in southern Colorado.

Real-time measurements from these stations are being fed into the GOES satellite communication system and are being downloaded and integrated into the operational NRCS station data stream.  NCAR is currently in the process of displaying this information for the UGRWCD using the NCAR/RAL HydroInspector tool along with other model-based snowpack and streamflow prediction products.

In collaboration with the Conejos Water Conservancy District, the State of Colorado, the NOAA Severe Storms Laboratory and NASA’s Jet Propulsion Laboratory  5 new SNOLITE stations were installed in the headwaters of the Conejos River basin in Upper Rio Grande basin in southern Colorado. The data from these stations is in the process of being telemetered via GOES satellite back to NCAR for quality control, archival, analysis and model evaluation.

These stations will be used to validate model-estimated and remotely-sensed observations of snowpack, soil moisture, precipitation, and other near-surface meteorological conditions.

 GoogleEarth map plot of the proposed installation sites (yellow balloons) of 4 new SNOTEL lite stations to be deployed in the Upper Taylor River Basin near Crested Butte, Colorado.  Inset photo shows the system hardware.
Figure 2. GoogleEarth map plot of the proposed installation sites (yellow balloons) of 4 new SNOTEL lite stations to be deployed in the Upper Taylor River Basin near Crested Butte, Colorado.  Inset photo shows the system hardware.

Plans for 2020

  1. Continue operation of the 4 new SNOTEL-Lite stations in the Upper Taylor River basin in southern Colorado. (see Figure 2 near Crested Butte, Colorado; station locations are shown with yellow bubbles. Inset shows photo of the station hardware.)  These stations are being built and installed in collaboration with the Upper Gunnison River Water Conservancy District (UGRWCD) and the Natural Resources Conservation Service (NRCS). Real-time measurements from these stations are being fed into the GOES satellite communication system and are being downloaded and integrated into the operational NRCS station data stream.  NCAR will also prepare and display this information for the UGRWCD using the NCAR/RAL HydroInspector tool along with other model-based snowpack and streamflow prediction products.
  2. Complete installation and operation of 5 additional new SNOLITE stations in the headwaters of the Conejos River basin in Upper Rio Grande basin in southern Colorado. This work is being done in collaboration with the Conejos Water Conservancy District, the State of Colorado, the NOAA Severe Storms Laboratory and NASA’s Jet Propulsion Laboratory. These stations will be used to validate model-estimated and remotely-sensed observations of snowpack, soil moisture, precipitation and other near surface meteorological conditions. These data are now beginning to provided telemetered data via GOES satellite back to NCAR for quality control, archival, analysis and model evaluation.
  3. Continue work with the NSF-supported Rocky Mountain Biological Laboratory (RMBL) near Gothic, Colorado to upgrade and reconfigure the network of research meteorological instrumentation in the region.  This data is critical to supporting a host of ecological, biological and hydrometeorological research studies in the RMBL region is one of the only networks in N. America explicitly designed to sample ecologically important parameters across a high elevation gradient extending above local treeline.

Measurements and modeling of land surface hydrologic conditions in seasonal rainfall dominated regions in South and Central America

FY19 Accomplishments

During FY19, networks of hydrometeorological instrumentation were deployed across regions of central Argentina and across Costa Rica in support of larger NSF-funded field research campaigns studying land-atmosphere coupling behavior.  Each of these networks provided region wide characterization of local meteorological and hydrological conditions providing important model initialization, evaluation and assimilation datasets.  In the case of Costa Rica, atmospheric sounding operations were also conducted in order to study the diurnal and seasonal evolution of lower atmospheric temperature and humidity structure leading to convective rainfall formation.  Links to these field campaigns can be found at:

South American deep convection campaign:  RELAMPAGO (https://www.eol.ucar.edu/field_projects/relampago)

Central American moisture transport campaign: OTREC (https://www.eol.ucar.edu/field_projects/otrec)

Plans for 2020

During FY20 work will continue on the evaluation of these new field datasets.  The aim will be to configure and execute coupled WRF/WRF-Hydro model instantiations and explore the nature of land-atmosphere coupling in these important seasonally-dominated rainfall regimes.

Geographic Information Systems (GIS) Program

The Geographic Information Systems (GIS) program is an interdisciplinary effort to foster interdisciplinary collaborative science, spatial data interoperability, and knowledge sharing using GIS. Current research and development activities in the GIS program occur in three thematic areas:

  • Integrating atmospheric and social sciences with GIS;
  • Improving usability of weather and climate models;
  • Conducting GIS-focused educational activities and building capacity at the science-society interface.

    Figure 1. Communicating storm surge forecast through integrative geovisualizations. This example represents a hypothetical storm forecast for coastal Georgia and South Carolina.
    Figure 1. Communicating storm surge forecast through integrative geovisualizations. This example represents a hypothetical storm forecast for coastal Georgia and South Carolina.

Climate and society are coevolving in a manner that may place vulnerable populations at greater risk to weather and climate stresses. Understanding societal risks and vulnerabilities to weather hazards and climate change requires integration of georeferenced information from physical and social sciences, including weather and climate data, information about natural and built environments, demographic characteristics, as well as social and behavioral processes. NCAR’s GIS program is working towards developing research frameworks and spatial methods for integration of diverse, multidisciplinary datasets, which are both quantitative and qualitative and exist at different spatial and temporal scales. 

 “Envisioning Risk of Hurricane Storm Surge and Sea Level Rise”
Figure 2. 2019 BRIGHTE workshop participants: “Envisioning Risk of Hurricane Storm Surge and Sea Level Rise”

In FY19, our research and development efforts aimed to better understand the effects of urban extreme heat and air pollution on human health, explore novel approaches to study and visualize governance of the food-water-energy nexus, and investigate the use and effectiveness of geovisualizations in risk communication of coastal flooding due to hurricane storm surge (Figure 1). The GIS program’s technical capabilities contributed to RAL’s work on wildfire and flood prediction, research on urban meteorology, and data interoperability.  

GIS education at the science-society interface is an important component of the GIS program.  In 2019, we conducted a BRIGHTE (Broadening participation in Interdisciplinary Geosciences: Hands-on Training and Education) workshop, “Envisioning Risk of Hurricane Storm Surge and Sea Level Rise”. The workshop provided an opportunity for participants to learn about an interdisciplinary approach to science of coastal flooding, explored innovative ways to visualize storm surge forecast and sea level rise projections in 2D and 3D GIS environments, and learn about science communication using Story Maps.

We will continue this ongoing work in FY2020 with the goal to contribute to NCAR’s convergence research and actionable science that meets the needs of research communities and decision-makers.