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

BACKGROUND

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

TECHNOLOGIES 

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

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

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

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

FY 2019 ACCOMPLISHMENTS 

Radar Data Assimilation  

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

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

Lightning Data Assimilation

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

Sand and dust forecasting with WRF-Chem

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

US Army Test and Evaluation Commands (ATEC)

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

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

 

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

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

RTFDDA High-resolution Reanalysis and Nowcasting for Shenzhen, China

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

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

PLANS FOR FY 2020

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

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

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