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

Abstract

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