摘要
Sea surface temperature(SST)is a crucial physical parameter in meteorology and oceanography.This study demonstrates that the influence of earth incidence angle(EIA)on the SST retrieved from the microwave radiation imager(MWRI)onboard FengYun-3(FY-3)meteorological satellites should not be ignored.Compared with algorithms that do not consider the influence of EIA in the regression,those that integrate the EIA into the regression can enhance the accuracy of SST retrievals.Subsequently,based on the recalibrated Level 1B data from the FY-3/MWRI,a long-term SST dataset was reprocessed by employing the algorithm that integrates the EIA into the regression.The reprocessed SST data,including FY-3B/MWRI SST during 2010-2019,FY-3C/MWRI SST during 2013-2019,and FY-3D/MWRI SST during 2018-2020,were compared with the in-situ SST and the SST dataset from the Operational Sea Surface Temperature and Ice Analysis(OSTIA).The results show that the FY-3/MWRI SST data were consistent with both the in-situ SST and the OSTIA SST dataset.Compared with the Copernicus Climate Change Service V2.0 SST,the absolute deviation of the reprocessed SST,with a quality flag of 50,was less than 1.5℃.The root mean square errors of the FY-3/MWRI orbital,daily,and monthly SSTs,with a quality flag of 50,were approximately 0.82℃,0.69℃,and 0.37℃,respectively.The primary discrepancies between the FY-3/MWRI SST and the OSTIA SST were found mainly in the regions of the western boundary current and the Antarctic Circumpolar Current.Overall,this reprocessed SST product is recommended for El Niño and La Niña events monitoring.
作者
ZHANG Miao
CHEN Lin
XU Na
CAO Guang-zhen
张淼;陈林;徐娜;曹广真(Key Laboratory of Radiometric Calibration and Validation for Environmental Satellites,National Satellite Meteorological Center,China Meteorological Administration,Beijing 100081 China;Innovation Center for FengYun Meteorological Satellite(FYSIC),Beijing 100081 China)
基金
National Natural Science Foundation of China(42330602)
Youth Innovation Team for“FengYun Satellite Remote Sensing Product Verification”(CMA2023QN12)。