Bias Correction And Assimilation Study On FY-3A Microwave Temperature Data

Compared with the conventional data, the satellite data is more consistent, covering wider area and having finer spatio-temporal resolution, so over such areas as oceans and plateau, it makes up for the shortage of conventional observations. It is a meaningful topic that using satellite data effectively in mesoscale numerical model.In this paper, in order to promote the use of Fengyun3A Microwave Temperature Sounding (MWTS) sensor data in regional numerical weather prediction, on the basis of WRF-3DVar system, an assimilation system suitable for MWTS data has been constructed by modifying or adding some programs or files. For making this system more perfect, one bias correction system has been established with the observation data in2010January17-31as the basic data, then using this bias correction system for MWTS data assimilation. Based on the successful foundation of the assimilation system, one Assimilation Experiment of MWTS data at12:00on January20,2010has been designed with a3hours time window, then analysis the assimilation results and prediction results.The main research results and conclusions of this paper are shown as follows:(1) A bias correction system for FY-3A MWTS data has been developed:using the classic satellite bias correction method and combining with the characteristics of FY-3AMWTS data, the bias correction system has been built. The distribution of observation residuals after bias correction was very close to the standard normal distribution. The result also shows MWTS data possess good quality, and potential for data assimilation.(2) A assimilation system for FY-3A MWTS has been constructed:combining with the result of bias correction experiment, a satellite data direct assimilation system for MWTS data has been created. The application of bias correction scheme in assimilation system made the assimilation result more reasonable and effective.(3) assimilation experiments of FY-3A MWTS data:Compared with the control experiment, the assimilation of MWTS data made a dramatical change in first guess field. Forecast field statistical results show that the the MWTS data assimilation process can play a positive effect, it can be applied to the numerical prediction.