摘要
Data assimilation algorithm depends on the basic assumption of unbiased observation error,so bias correction is one of the important steps in satellite data processing.In this paper,using the geostationary interferometric infrared sounder(GIIRS)of FengYun-4 A(FY-4 A)observation and simulated brightness temperature based on background field,the brightness temperature bias correction of GIIRS channel is carried out based on random forest(RF)and extreme gradient boosting(XGBoost)machine learning.Based on the case data of Typhoon"Haishen",the correction effect of machine learning method is compared with Harris and Kelly’s"off-line"method,and the importance of different predictors to the bias correction is further discussed.The experimental results show that the systematic bias is effectively corrected,and the following conclusions are obtained:the correction effect is improved by adding geographic information(longitude and latitude)into the predictors;under the given combination of predictors,the correction effect of XGBoost is the best,followed by random forest,and finally offline method,but the three methods can correct the bias effectively;compared with long wave data of FY-4 A/GIIRS,machine learning may be more feasible for medium wave data bias correction.
基金
Supported by the National Natural Science Foundation of China(41805080)
Special Project for Innovation and Development of Anhui Meteorological Bureau(CXB202101)
Central Asian Fund for Atmospheric Science Research(CAAS202003)。
