Long-Term Calibration of Satellite-Based All-Weather Precipitable Water Vapor Product From FengYun-3A MERSI Near-Infrared Bands From 2010 to 2017 in China

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 to 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 intercomparison. 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-square 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 FengYun-3 (FY-3) series to ensure a continuous long-term data record and benefit the FY-3 user community.