P9 Verification of high-resolution WRF-RTFDDA based operational deterministic and ensemble weather forecasting systems over China with observations

Pan, Linlin, Yubao Liu, Gregory Roux, Will Cheng, Yuewei Liu, National Center for Atmospheric Research, Ju Hu, Shuanglong Jin, ShuangLei Feng, China Electric Power Research Institute, China

In collaboration with the Chinese Electric Power Research Institute (CEPRI) of State Grid Corporation of China (SGCC) to meet the needs for power generation, grid integration and transmission through dispatch and load, NCAR RAL has applied and improved the WRF-RTFDDA technology to support weather modeling capabilities and to develop new advanced weather/climate tools for wind energy forecasts and electric transport safety. This study investigated the performances of new developed high-resolution WRF-RTFDDA based deterministic numerical weather prediction (NWP) system and 30 member ensemble NWP system over China for CEPRI through objective evaluation of surface and upper level variables such as winds, temperature and humidity.

The forecasting skill of the deterministic system and ensemble system were tested on the 3 km and 9 km domain outputs, respectively. The verification investigates operational forecasting results from two separate months, with one focused on the dry period/season (January), and the other focused on the wet period/season (July). The analysis and forecasts run every 3 hours for deterministic system and every 6 hours for ensemble system. Using the observations from stations over China, the model outputs are validated objectively. The statistics of the system performance is calculated on a station-by-station basis as well as for the grid average in terms of traditional metrics such as domain/station average bias, root mean square error (RMSE), mean absolute error (MAE), and the correlation between observation and model outputs. Comparisons show that the high-resolution deterministic system has advantage in forecasting the detailed structures of weather features and can update forecasts rapidly, and the ensemble system has advantage in predicting some mesoscale weather processes.