Study On AMSU Radiance Data Direct Assimilation In Heavy Rain Forecasting

Satellite data has the characteristics of being more consistent, covering a wider area and having high spatial and temporal resolution, which making up for the conventional observations over oceans and plateau. How to make use of data assimilation to extract the effective observation information of satellite data and to form a better initial field, to improve the accuracy of numerical prediction, that is a meaningful topic.The direct assimilation of microwave radiance data AMSU(AMSU-A and AMSU-B) and conventional observation are researched with the mesoscale numerical model WRFV3.1 and its 3DVAR system. Besides, the regional background error covariance matrix (B matrix) using NMC method is adopted in the assimilation system. The characteristics of the B matrix structure are analyzed in detail. Four comparison experiments, i.e. CONTROL, Conventional Observation, Conventional Observation&AMSU-A and Conventional Observation&AMSU-B, are conducted with a heavy rainfall process occurred at Jianghuai region during 8-9 july 2007. The results show that:(1) It can simulate the regional background error covariance of Yangtze-Huaihe Valley by using American NMC method. And the B matrix makes the part of balance outputted, while the part of unbalance is became control variables, which ensures the quality of analyses.(2) The experiments of choosing the different characteristic length have the different impact on the simulation of heavy rainfall. By many experiments, It indicates that the factor of length scale and variance scale is 0.05, the result of analysis is optimal.(3) After AMSU data being added into the WRFDA model, the forecast of precipitation is more exact, especially the intensity of precipitation. In addition, the result of Exp. AMSU-B seems superior to the Exp.AMSU-A.(4) By continuous assimilation, the large scale of environmental field is obviously improved. Whether intensity or position, the northeast cold eddy, southwest trough and subtropical high of Exp.AMSU-B are more closely the real.(5) The assimilation of AMSU radiance data has a certain impact on the temperature, humidity and wind fields. The direct assimilation AMSU-A data adjusts the temperature increment of the medium troposphere layers more distinctly. The direct assimilation AMSU-B data adjusts the humidity increment of the lower troposphere layers more significantly. In addition, the direct assimilation AMSU data has influence on the wind increment, which is convergence in the lower and divergence in the upper.