NCAR-CEPRI Joint Development of WRF-FDDA NWP Systems for Power-grid Weather Safety in China

Weather affects all cycles of electric-power industries, including power generation, grid integration, transmission, dispatch, and consumption (load). Given the dramatic growth of renewable energy and the deployment of ultra-high-voltage large-capacity electric power transmission systems recently, weather information has become a critical factor affecting electric power operation reliability, economics, and safety. Power industries desire high-resolution real-time weather analyses and forecasts, as well as historical weather and climate reanalysis. Real-time weather analysis and forecasting tools are needed to produce weather information for real-time renewable power and load prediction, electric-grid operation and maintenance, load balances, and risk management of severe weather events; whereas background weather/climate information is essential for electric-power production planning, power-transmission system design and maintenance, renewable-energy resource assessment, and power-plant siting. In collaboration with Chinese Electric Power Research Institute (CEPRI), NCAR is developing a WRF based weather modeling suite that contains real-time four-dimensional data and forecasting (RTFDDA), ensemble-RTFDDA, RTFDDA-LES and climate-FDDA. The modeling tools facilitate the electric-power meteorological services in China with precision numerical weather prediction, high-resolution downscaling of global reanalysis datasets, mesoscale ensemble prediction, and ultra-high resolution LES modeling of weather-sensitive transmission facilities over complex terrain. The data capability of the modeling system includes 4D relaxation ensemble Kalman filter (4D-REKF) and Hydrometeor and Latent Heat Nudging (HLHN) scheme for assimilating conventional and power-special data. Diagnostics and coupling modeling of high-impact weather, including lightning, icing and hydropower predictions are developed to customize the WRF products for the applications. In this paper, we’ll highlight the research and development regarding to the WRF data assimilation and ensemble configuration in these systems.