资源环境科技发展态势分析平台

An ensemble three-dimensional variational assimilation method based on Proper Orthogonal Decomposition (referred to as POD-3DEnVar) can not only provide flow-dependent covariances through the evolving ensemble of short-range forecasts, but also obtain directly the analysis field without an iterative process. Using the POD-3DEnVar method, a regional hybrid variational ensemble data assimilation system (referred to as POD-HVEDAS) is constructed with the Community Radiative Transfer Model (CRTM) as the observation operator. Observations from Microwave Humidity Sounder (MWHS) and Microwave Temperature Sounder (MWTS) onboard Fengyun3A are simultaneously assimilated into the Weather Research and Forecasting (WRF) model by the POD-HVEDAS. Bias correction and quality control schemes for MWHS and MWTS microwave radiance are applied in the system. Three experiments (Con, Hybrid-DA, and POD-DA) are designed to investigate the assimilation ability of the POD-HVEDAS for heavy rainfall over the Yangtze River. The assimilation effects of POD-3DEnVar and WRFDA-Hybrid assimilation method are compared. The results show that the POD-HVEDAS can assimilate MWHS and MWTS microwave radiance effectively, and give a better precipitation forecast than that of the WRFDA-Hybrid method. And it is found that the assimilation increments of both POD-3DEnVar and WRFDA-Hybrid methods all show flow dependent characteristic. However, the improvements in the background wind, temperature and humidity fields by the POD-DA experiment are more obvious than those of the Hybrid-DA experiment, which play key roles in improving the accuracy of precipitation forecast. These promising results suggest initiulizing limited-area models with POD-3DEnVar method that assimilate microwave radiances is beneficial. In the future, POD-3DEnVar method will be used to study multiple cases to fully assess the robustness of POD-HVEDAS.