Expedient Mid-Wave Infrared Band Generation for AGRI during Stray Light Contamination Periods Using a Deep Learning Model  

作　　者：Haixia XIAO Xiaoyong ZHUGE Fei TANG Jimin GUO 

机构地区：[1]Nanjing Innovation Institute for Atmospheric Sciences,Chinese Academy of Meteorological Sciences-Jiangsu Meteorological Service,Nanjing,210041,China [2]State Key Laboratory of Severe Weather,Chinese Academy of Meteorological Sciences,Beijing,100081,China [3]Jiangsu Key Laboratory of Severe Storm Disaster Risk/Key Laboratory of Transportation Meteorology of Chinese Academy of Meteorological Sciences,Nanjing,210041,China [4]Unit 93307,People’s Liberation Army,Shenyang,110000,China

出　　处：《Journal of Meteorological Research》2025年第1期211-222,共12页气象学报(英文版)

基　　金：Supported by the National Natural Science Foundation of China(42305177 and 42175006);Beijige Foundation(BJG202210);Open Research Program of the State Key Laboratory of Severe Weather(2023LASW-B16).

摘　　要：The Advanced Geosynchronous Radiation Imager(AGRI)onboard China’s Fengyun(FY)-4 satellites,which provides observational data across various wavelengths from visible to infrared(IR),holds great potential for diverse applications.However,the FY-4A AGRI mid-wave IR(MWIR)band(3.75µm)is often contaminated by stray light in the midnight hours during the 1-2 months before and after the vernal or autumnal equinoxes.In this study,a U-Net-based deep learning model was employed to generate an expedient MWIR band from the FY-4A AGRI long-wave IR band.Validation using normal radiance measurements revealed that MWIR brightness temperatures generated by the deep learning model are very close to those observed by the FY-4A AGRI,with mean absolute error of 1.48 K,root mean square error of 2.39 K,and a correlation coefficient of 0.99.When applying the model to periods of stray light contamination,the brightness temperature anomalies found in the FY-4A AGRI MWIR band are effectively eliminated.The findings of this study could support various scientific applications that necessitate use of the MWIR band during midnight hours,such as identification of fog/low stratus cloud.

关 键 词：Advanced Geosynchronous Radiation Imager(AGRI) mid-wave infrared band deep learning stray light contamination 

分 类 号：P237[天文地球—摄影测量与遥感] TP18[天文地球—测绘科学与技术]