Long-Term Calibration of Satellite-based All-Weather Precipitable Water Vapor Product from FengYun-3A MERSI Near-Infrared Bands from 2010 through 2017 in China

• Published in: IEEE transactions on geoscience and remote sensing p. 1
• Main Authors: Xu, Jiafei, Liu, Zhizhao
• Format: Journal Article
• Language: English
• Published: IEEE 07.08.2023
• Subjects: Atmospheric measurements; Cloud computing; FengYun-3A; Global Positioning System; GPS; Instruments; machine learning; MERSI; Meteorology; precipitable water vapor; Satellite broadcasting; Satellites
• ISSN: 0196-2892; 1558-0644
• DOI: 10.1109/TGRS.2023.3300880

Precipitable water vapor (PWV) product, obtained from near-infrared (NIR) measurements of the Medium Resolution Spectral Imager (MERSI) from the FengYun-3A (FY-3A) spacecraft, has not been used in weather forecasting and climate monitoring so far because of its degraded accuracy. In this research, four machine learning based correction approaches are for the first time developed to adjust the long-term observation accuracy of the official MERSI/FY-3A NIR all-weather water vapor product from 2010 through 2017 in China considering multiple influence factors - MERSI/FY-3A NIR PWV, latitude, longitude, month, and cloud. In addition to four machine learning models, the conventional Multiple Parameter Quadratic (MPQ) regression method is also utilized for inter-comparison. The in-situ PWV estimates, acquired from 100 Global Positioning System (GPS) stations in 2010-2017 across China, are utilized as reference PWV in model training. The validation results obtained from comparison with PWV from the other 114 GPS stations during 2010-2017 in China indicate that the methods notably enhance the long-term performance of FY-3A MERSI NIR water vapor observations under all weather conditions, reducing root-mean-squares error (RMSE) by 57.62-71.61%. The calibrated MERSI NIR PWV, calculated using machine learning models, performs better than the conventional MPQ-estimated PWV. After the calibration, the new MERSI NIR water vapor estimates show a performance comparable to other satellite-observed NIR PWV products. The enhanced MERSI/FY-3A NIR all-weather PWV data can complement other water vapor data in the FY-3 series to ensure a continuous long-term data record and benefit the FY-3 user community.