Water vapor retrieval from MERSI NIR channels of Fengyun-3B satellite using ground-based GPS data

Abstract

Abstract An ensemble-based empirical regression algorithm is for the first time developed to retrieve total column water vapor from the Medium Resolution Spectral Imager (MERSI) near-infrared (NIR) channels onboard the Fengyun-3B (FY-3B) satellite. This retrieval method uses precipitable water vapor (PWV) data estimated from ground-based Global Positioning System (GPS) data to build a regression model in which the reflectance ratio observed from MERSI NIR absorption channels and the corresponding GPS PWV data are the parameters. The MERSI Level 1b data, specifically the three water vapor absorption channels centered at 905\xa0nm, 940\xa0nm, and 980\xa0nm are used to retrieve water vapor. PWV data observed from 256 ground-based GPS stations located in the western North America in 2016 are used as reference data for model development. Then, validation is performed with data obtained during 2017–2019 from both the western North America and Australia to assess the performance of the proposed algorithm. The results indicate that the new PWV results agree very well with ground-based PWV reference data. The mean absolute percentage error (MAPE) for ensemble median PWV is 16.72%\xa0~\xa036.74% in western North America and is 15.47%\xa0~\xa032.31% in Australia. The RMSE is 4.635\xa0mm\xa0~\xa08.156\xa0mm in western North America and is 5.383\xa0mm\xa0~\xa08.900\xa0mm in Australia. The weighted mean value using three-channel ratio transmittance has the best retrieval accuracy, with RMSE of 4.635\xa0mm in western North America and 5.383\xa0mm in Australia. This new PWV algorithm can retrieve PWV from FY-3B data with a higher accuracy for different regions. Different from conventional algorithms, no pre-observed information of atmospheric parameters is required in this model.