The Characteristics Of FY-4A Satellite Atmospheric Motion Vectors And Their Impacts On Data Assimilation

High observation efficiency,scanning speed and observation frequency of Feng-Yun-4A(FY-4A)satellite indicates the update and progress of Chinese geostationary meteorological satellites.To promote the application of Y-4A products in Numerical Weather Prediction(NWP),atmospheric motion vectors(AMVs)derived from FY-4A satellite are taken as the research object.Firstly,the characteristics of FY-4A AMVs derived from the high-level water vapor(WV-High)channel,the low-level water vapor(WV-Low)channel and the infrared(IR)channel are analyzed and their corresponding observation errors are estimated respectively.Then,the impacts of single-channel and multi-channel FY-4A AMVs on Rapid-refresh Multi-scale Analysis and Prediction System-Short Term(RMAPS-ST)are discussed based on one-month cycling data assimilation and forecasting experiments.Main results are as follows:(1)AMVs from water vapor channels(both WV-High and WV-Low)concentrate above 700 h Pa,and the difference between height,where the most AMVs of each channel concentrate,is related to different sensitivities of the channel wavelength to the water vapor at different heights.In addition,because the IR channel is in the atmospheric window band,and there is still considerable data volume for the height below 700 h Pa,which can well make up for the information of wind field on the lower layer.Compared with the Himawari-8 AMVs of the same period,FY-4A AMVs have a larger bias range and obviously large outliers,so the selection of AMVs with quality indicator(QI)value greater than 80 could ensure the data quality and improve the utilization rate.(2)Compared with default observation errors in WRFDA,new observation errors have more obvious structure,especially for layer above 700 h Pa.Besides,new observation errors are bigger than default values on most layers.(3)In the single-channel assimilation experiments,data derived from water vapor channels mainly improves the wind field on the upper layer,while data from the infrared channel can improve the wind field prediction on the lower layer to some extent.Moreover,AMVs derived from infrared channel produce the largest improvement on precipitation forecast,which is related to that AMVs on the lower layer in the infrared channel could adjust the wind field and water vapor transportation.(4)In the multi-channel assimilation experiment,simultaneous assimilation of AMVs from the three channels could make good use of the advantages of each channel on most layers.Besides,the multi-channel assimilation further optimizes the wind field,so as to obtain a better precipitation forecast than experiment assimilating AMVs from IR channel only.