Downscaling study of microwave sea surface temperature products based on FY-3C satellite

Sea surface temperature (SST) serves as a vital meteorological parameter, exerting considerable influence on climate change and the Earth's oceanic system. To address the limitation of microwave sensors in providing all-weather and high spatial resolution SST products, we focus on utilizing FY-3C/ Microwave Radiation Imager (MWRI) SST, sea surface wind speed, and chlorophyll concentration data from 12 periods in the South China Sea and Yellow-Bohai Sea. It further develops a spatial downscaling algorithm for MWRI SST products based on the Multiscale Geographically Weighted Regression (MGWR) method. The results show that the MGWR-based downscaling conversion function adeptly unveils distinct interaction relationships between different SST state parameters and SST, and sea surface wind speed exhibits a global impact on SST distribution, while longitude, latitude, and chlorophyll mass concentration have a localized effect on SST. Moreover, the downscaled 5 km resolution SST data derived from the MGWR algorithm boasts enhanced spatial texture, compensating for the limitations of infrared data. Finally, the downscaled MWRI SST is evaluated by FY-3C/VIRR SST data. We find that the coefficient of determination (R2) of both is greater than 0.86, and the root mean square error (RMSE) and mean absolute error (MAE) are less than 1.29 K and 1 K, respectively. (c) 2024 COSPAR. Published by Elsevier B.V. All rights are reserved, including those for text and data mining, AI training, and similar technologies.