A Novel Model Integrating the Spherical Cap Harmonic Analysis With the XGBoost Algorithm to Improve the MODIS NIR PWV

Water vapor is an essential element in the hydrologic and energy cycles, as well as in the climate and atmospheric circulation on Earth. Though various techniques have been developed, water vapor is still difficult to retrieve with both high accuracy and resolution. To calibrate the biases and restrain large errors in the moderate resolution imaging spectroradiometer (MODIS) near-infrared (NIR) precipitable water vapor (PWV) in Western Europe, we propose a hybrid model that combines the spherical cap harmonic analysis (SCHA) model and the Extreme Gradient Boosting (XGBoost) model. This model includes two main steps 1) initial calibration of MODIS PWV using an SCHA model and 2) advanced calibration of the interim PWV using an XGBoost model. The results show that the hybrid model achieves an average bias of 0.0 mm, STD of 2.0 mm, and rms of 2.0 mm, increasing the MODIS PWV accuracy by 55.6% in terms of rms in Western Europe in 2020. We further demonstrate that the hybrid model outperforms the SCHA model and the XGBoost model in calibrating biases and restraining large errors. We find that the MODIS PWV typically exceeds the GNSS PWV by 0.7 mm on annual average and their difference fluctuates seasonally, varying from −4.0 mm in winter to 4.0 mm in summer. This study provides a powerful method to optimize the MODIS PWV and obtain high-quality PWV products for meteorological research and Earth observation systems.