Operational RTFDDA

BACKGROUND

NCAR WRF (Weather Research and Forecasting) based Real-Time Four-Dimensional Data Assimilation (RTFDDA) and forecasting technology is developed to meet the need for rapidly updated high-resolution precision weather information for weather-critical applications directed at national defense and security, energy, emergency response and health. By far 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. This section reviews the following operational RTFDDA NWP projects:  

  1. US Army Test and Evaluation Commands (ATEC) test ranges
  2. Advanced NWP for State Grid Corporation of China (SGCC)  
  3. MAGEN for the Government of Israel
  4. WRF-RTFDDA for wind power prediction of State Power Investment of China (SPIC)
  5. RTFDDA high-resolution reanalysis and nowcasting for Shenzhen, China

FY2018 ACCOMPLISHMENTS 

US Army Test and Evaluation Commands (ATEC)

RTFDDA system serves eight Army test ranges located in the US and also supports on-demand test missions of ATEC in other regions over the globe. One ensemble-RTFDDA system and a deterministic RTFDDA-LES system have been set up for operational forecasts at the Army Dugway Proving Ground, Utah. More detail on specific advances made in ATEC modeling systems can be found at the 4DWX section of this report.

Advanced NWP for the State Grid Corporation of China (SGCC)

Figure 1. Summary of China-national-domain RTFDDA, Climate-FDDA and Ensemble-RTFDDA systems developed for SGCC.
Figure 1. Summary of China-national-domain RTFDDA, Climate-FDDA and Ensemble-RTFDDA systems developed for SGCC. 

Electric power generation, grid integration, transmission, dispatch, and consumption (load) are heavily affected by weather. The recent tremendous growth of renewable energy and the deployment of ultra-high-voltage large-capacity electric power transmission systems critically depend on weather.  Collaborated with Chinese Electric Power Research Institute (CEPRI) of the State Grid Corporation of China (SGCC), NCAR RAL applied the NCAR WRF (Weather Research and Forecasting) based RTFDDA (Real-time four-dimensional data assimilation and forecasting) technology to improve the weather modeling capabilities at the Numerical Weather Prediction Center at CEPRI. The weather modeling systems provide broad weather information for the SGCC electric power production. Fig. 1 summarizes major NWP systems developed for real-time operation as well as testbed research. The real-time operational 3-km grid RTFDDA system outperformed other available model forecasts for the heavy precipitation events happened during the 2017 flooding season in China.         

MAGEN for Israeli Government

MAGEN (Model for Advanced GENeration of 4D Weather) employs RTFDDA and WRFDA-3DVAR hybrid data assimilation technologies to provide high-resolution weather guidance over the eastern Mediterranean region. The MAGEN system is enhanced with sand and dust prediction capabilities for real-time operational forecasts of dust and visibility. The RTFDDA data assimilation technology is coupled with WRF-Chem, supporting online dust spin-up and cycling modeling for accurate real time forecasting. A large single-domain RTFDDA-Dust model at a grid size of 9km was set up to cover the main dust emission and transport areas over the Mid-East and northern Africa that affect Israel. In addition, a new 3.3km grid high-resolution MAGEN forecasting is also developed to optimally take the advantage of the high-resolution ECMWF global model forecasts working as the initial and lateral boundary conditions. The MAGEN-dust and the new ECMWF data driven 3km grid MAGEN modeling systems have been installed at Israel for real-time operation. 

WRF-RTFDDA for SPIC Wind-Power Prediction

Figure 2. 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 2. 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 China. The wind farms are located in two regions featured by complex terrain, including steep mountains and river corridors. Up to three days forecasts of general wind evolution and rapid-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 is 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 met-tower weather observations for wind prediction; b) developing machine-learning post-processing tools based on the NCAR analog ensemble (ANEN) technology and quantile-regression (QR) ensemble calibration technology to improve the accuracy of the model forecast of the turbine hub-height wind speed; and c) studying the WRF model dynamics and physics to improve its boundary layer flow simulation over complex terrain. Fig.2 shows a sample comparison of the model surface wind forecasts with the observations of the automatic weather stations.

RTFDDA High-resolution Reanalysis and Nowcasting for Shenzhen, China

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 met-towers, Doppler radars, the Global Positioning System (GPS), lightning, and other platforms, into the RTFDDA system to provide continuous weather analysis and forecasts, and generate a ten-year microclimatology for the Shenzhen area. The modeling system was configured with four nested domains with horizontal grid sizes at 27km, 9km, 3km and 1 km, respectively. The 1-km domain covers Shenzhen municipality, Hong Kong, and the neighboring area. The main accomplishments in FY2018 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) capabilities. The system runs hourly analysis and forecast cycles, producing 24 h forecasts at 1km grid spacing every hour; 2) evaluating the newly installed dual-pol Doppler radar data and developing new scheme that more accurately retrieve the hydrometeor classification and water contents for data assimilation; and 3) conducting numerical experiments for future observation system design; and 4) working with the SZMB scientists and engineers to produce the last ten year climate-FDDA reanalysis and generate public climate service products.

PLANS FOR FY2019

RTFDDA is continuously improved for producing more accurate analysis and forecasts of the current operational systems and deploying for new applications as well. The research work will continue to advance the core model sciences and technologies. Plans for several other on-going RTFDDA projects are as follows.

US Army Test and Evaluation Commands (ATEC)

As a main user of the RTFDDA technologies, ATEC will continue to collaborate with NCAR to improve the forecast accuracy of the operational RTFDDA systems over the eight Army test ranges located in the US and those on-demand systems in other regions over the globe. More detail on research goals for the ATEC modeling systems can be found at the 4DWX section of this report.

RTFDDA for SPIC Wind-Power Prediction

The research of this project will be focused on RTFDDA and ensemble-RTFDDA system refinements to achieve accurate boundary layer wind forecasts for the three wind farms. Algorithms for more effective assimilation and machine-learning based model forecast prost-processing using the complete wind farm data, including met-tower observations and wind-turbine nacelle wind measurements, will be developed. Effective data quality-control schemes are critical to warrant successful use of these data and should be studied.  The NCAR analog ensemble (ANEN) bias correction technology and quantile-regression (QR) ensemble forecast calibration algorithm will be employed for the model forecast post-processing. Wind-power conversion scheme, objective verification and graphic display of the wind and power forecasts will be accomplished to finalize the real-time operational forecasting system. As an extension of the current project, a new SPIC-NCAR collaboration project develop a wind power forecast system for a large wind farm cluster in the northwestern China will begin in 2019.

Israel MAGEN Systems

New research proposals will be initiated to significantly augment the capabilities of the existing MAGEN hybrid system to assimilate data from dust models to improve the dust forecasting capability. Plans for further enhancements to the MAGEN models also include increasing model resolution, cloud analysis and assimilation, ensemble data assimilation and probabilistic prediction, adaptive observation, and improvement of integration with the European Centre for Middle-range Weather Forecasts (ECMWF) model output

Joint Research Program with the China Electric Power Research Institute

RAL is expanding the collaboration with CEPRI of the State Grid Corporation of China to establish a CEPRI-NCAR joint program for electrical power meteorological research and ensure a long-term, stable collaborative research program with multi-discipline electric power and meteorological sciences. CEPRI and NCAR will jointly develop advanced numerical weather prediction and data assimilation systems with the technologies including the Weather Research and Forecasting (WRF) model, Four-Dimensional Data Assimilation (FDDA), Ensemble-Kalman-Filter FDDA, Gridpoint Statistical Interpolation (GSI), Climate-FDDA, data quality control, advanced model output statistical bias correction, and ensemble calibration technologies.

Because of the diverse weather regimes across the country, the joint research program will conduct weather forecasting experiments to advance electric power weather simulation and forecasting capabilities. High-resolution weather reanalyses for generating a multi-year electric power weather/climate reanalysis historical database will be performed, and fine-scale weather modeling and forecasting systems toward renewable energy resource assessment and power forecasting applications and effectively support large-scale integration of renewable energy on to the power grids will be carried out. Electric-power meteorological disasters are the key research foci.  NCAR and CEPRI will jointly research and develop analysis and forecasting technologies and develop electrical power grids weather safety forecasting and early warning systems.  

Meteorological Bureau of Shenzhen Municipality

NCAR-SZMB collaboration on optimizing the RTFDDA weather forecasting technologies and urban applications will be continued for the next a few years. SZMB recently upgraded its high-performance computing (HPC) capabilities, with more than an order of computing power increase for supporting major upgrade of the operational RTFDDA systems at the center. The project goals for 2019 include developing very-large eddy simulation (VLES) RTFDDA modeling system for the city, with grid sizes of ~300m and a 1-km grid ensemble RTFDDA modeling system. The core scientific research is to explore the potential for improving urban-scale weather analysis and forecasting with VLES model data assimilation (of all available data) and convection modeling. In the next two years, the model applications will be extended for fog and severe wind warning in the offshore and the Pearl Delta regions within a distance of 300km from the Shenzhen city.  In addition, NCAR will continue to work with SZMB to make use of the RTFDDA QPE and QPF products for the city flooding warning and the risk assessment of the reservoirs in Shenzhen during heavy rainfall.