Preliminary Application of a Multi-Physical Ensemble Transform Kalman Filter in Cloud and Precipitation Forecasts

In this study, based on the retrieval data from Fengyun geostationary meteorological satellite and Tropical Rainfall Measuring Mission satellite, a large-scale precipitation case in eastern China is selected to address systematic deviations of deterministic forecasts for clouds precipitation. A multi-physical ensemble transform Kalman filter (ETKF) constructed research Weather Research Forecast model version 3.6, its forecasting ability terms cloud-top height temperature, hydrometeors, evaluated by quantitatively comparing three microphysical parameterization schemes (Lin, Morrison, CAM5.1 schemes) their corresponding mean. The results show that Lin, all underestimate range cloud systems have different advantages disadvantages elements, while improvement mean limited. However, ETKF can effectively improve forecast accuracy system range. addition, has physical schemes, which dramatically reduce errors, threat scores.