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

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 95^th 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.

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 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

Improving 48-h predictions of fine particulate matter (PM_2.5) 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 (PM_2.5) 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).

Figure 1: Top left panel shows EPA PM_2.5 monitoring sites used for evaluation of CMAQ-simulated PM_2.5 mass concentrations. The comparisons of the observed and CMAQ-simulated diurnal and daily variability of PM_2.5 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 PM_2.5 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 PM_2.5 and O_3. 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.

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.

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 PM_2.5.

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 (PM_2.5) 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 PM_2.5 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 PM_2.5 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/

Figure 4:Verification of averaged 72-h WRF-Chem forecasted PM_2.5 with (ASM) and without (BKG) assimilation of MODIS AOD against PM_2.5 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

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 PM_2.5 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.

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 PM_2.5 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

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

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

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

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.

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.

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.

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.

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.

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.

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

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.

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.

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.

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

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.

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.

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.

Figure 6. Example plot of the trajectory of each forecast model colored by forecast lead time.

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.

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.

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.

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.

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/

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.

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.

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.

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.

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.

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. 

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.

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.

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.

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 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.

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.

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.

• 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.

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

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.

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).

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.  

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.

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. 

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).

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.

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. 

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.

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.

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.

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.

 

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. 

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.

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)

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

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.

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.