Bias Characteristics Analysis And Assimilation Application Of FY-4A AGRI Radiance Data

As the latest generation of geostationary meteorological satellites in our country,a significant development has been made for Fengyun-4A(FY-4A).Compared with the previous generation(Fengyun-2),FY-4A has better observation accuracy and a shorter scanning time.The interface for the FY-4A AGRI data assimilation was complemented in Weather Research and Forecasting Data Assimilation(WRFDA)v3.9.1 model before investigating the bias characteristics based on RTTOV v11.3 model and GFS analysis.Bias-correction experiments of FY-4A AGRI data in infrared channels 8–14 were further conducted.Then using GFS analysis stimulated and AHI brightness temperature assessed AGRI data quality,and taking AGRI data assimilation experiments of typhoon.The results show that: 1)Channel 8-10 and 14 have warm biases.There are cold biases in channel 11-13.Channel 8 has larger bias in the day because of sun glint.The biases and standard deviation of water vapor channels 9 and 10 are small.The characteristics of the biases show obvious differences between land and ocean in channels 11–14.Land's biases are more complex than the ocean's.2)The bias in channels 8 and 11–14 show more obvious dependence on the scene temperature than those in channels 9 and 10.3)The standard deviation of bias between GFS stimulated brightness temperature and AGRI are smaller,and AGRI brightness temperature is closer to AHI.The data quality improve obviously.4)Assimilating AGRI data can improve wind forecast effectively.The experiments assimilating AGRI water vapor channels can decrease errors about specific humidity.Meanwhile,choosing 75 km thinning mesh had better result about typhoon Maria's(2018)path,intensity,velocity and vertical structure.5)There is no obvious dependence of biases on the satellite zenith angle.The systematic bias and standard deviation have been corrected effectively and closer to normal distribution after variational bias correction experiments.