Impact of a New Bias Correction Predictor for FY‐4A AGRI All‐Sky Data Assimilation on Typhoon Forecast

Abstract

Abstract The all‐sky assimilation module for Advanced Geostationary Radiation Imager (AGRI) onboard the Chinese new generation of geostationary meteorological satellites Fengyun‐4A (FY‐4A) is constructed for the first time in Weather Research and Forecasting Model Data Assimilation (WRFDA) model with Radiative Transfer for the TIROS Operational Vertical Sounder (RTTOV). Based on the characteristics of bias distribution, the cloud effect average is selected as a new bias correction (BC) predictor to remove cloud‐related biases in all‐sky conditions. The impact of the modified BC scheme and AGRI all‐sky assimilation on typhoon Lekima (2019) forecast is evaluated through a set of cycle assimilation experiments with WRFDA 3DVAR component. The result shows that, compared with other BC schemes, the modified BC scheme can effectively reduce the mean standard deviation, root mean square error, and absolute mean of observation departure in all‐sky conditions. Meanwhile, the AGRI all‐sky assimilation with the modified BC scheme obtains significant improvements in track prediction. Substantial error reductions in model variables, such as wind, temperature, and humidity, are also produced.