Research On The Effect Of Assimilation Of Geostationary Satellite Atmospheric Motion Vector Data On Typhoon Numerical Prediction

This paper mainly studies the effect of assimilation of Atmospheric Motion Vectors(AMVs)data of geostationary satellites on typhoon numerical prediction in the model.Firstly,this paper evaluates the quality of the AMVs of the geostationary satellites FY-2G and Himawari-8 in July and August 2017.The evaluation results show that the data of QI(Quality Indicator)?80 is better in the AMVs data of the two satellites.On the whole,the quality of the AMVs of Himawari-8 is relatively better.Compared with FY-2G,the deviation and the root-mean-square-error between the U-wind component of the AMVs data of Himawari-8 and the reference data are smaller,and the probability density distribution of the deviation is closer to the normal distribution.Then based on the above evaluation results,we design and construct a series of algorithm models for geostationary satellite AMVs data,such as height assignmnet,Quality control and background check,observation error approach,channel merging and data thinning method.Analysis of the processing results of each module found that the AMVs data processing algorithm works well and the quality of the AMVs data has been improved.Finally,the typhoon “HATO” of 2017,NO.1713,was selected as a research case.Using the WRF model(Weather Research and Forecasting model,version 3.9.1)and the WRFDA assimilation module(Weather Research and Forecasting model Data Assimilation system,version 3.9.1)to respectively assimilate the different AMVs data and perform multiple sets of cyclic assimilation experiments.Compared with the control test simulation results,the typhoon track and intensity of the assimilation Group are closer to the actual observation data.In addition,the difference in forecasting effect of each assimilation group is small,and the forecasting effect of the assimilation of the AMVs data of the Himawari-8 Water-Vapor channel was the best.Assimilation the processed AMVs data can improve the forecasting effect of the model wind fields and pressure fields.Assimilation the processed AMVs data plays a positive role in typhoon numerical forecasting and it can also extends the duration of model forecasts.