Integrating near-infrared, thermal infrared, and microwave satellite observations to retrieve high-resolution precipitable water vapor

Various techniques have been developed to monitor water vapor because of its important role in weather forecasting and climate change studies. However, high-resolution, spatially continuous water vapor data remain scarce due to the sparsity of ground stations, coarse observational resolution, unavailability of remote sensing data during cloudy conditions, and systematic biases among different techniques. In this study we developed the Global Navigation Satellite System (GNSS) aided algorithms to retrieve Precipitable Water Vapor (PWV) from near-infrared (NIR), thermal infrared (TIR), and microwave (MW) observations from the Medium Resolution Spectral Imager II (MERSI-II) and the Microwave Radiation Imager (MWRI) onboard the Fengyun-3D satellite. We also proposed an improved iterative tropospheric decomposition algorithm to fuse the multiband PWV data, yielding the NIR + TIR PWV (0.01°), the MW PWV (0.25°), and the fused PWV (0.001°) for Australia. Validation against the GNSS PWV shows that the NIR + TIR PWV has a Root Mean Square Error (RMSE) of 1.45 mm and a bias of 0.07 mm, implying a 34 % improvement over the operational NIR products in terms of RMSE. The MW PWV shows RMSE and bias of 1.86 mm and 0.05 mm. The fused PWV integrates the advantages of different datasets, further enhancing the accuracy by 15 % for the NIR + TIR PWV and 21 % for the MW PWV. This study made the first attempt to retrieve PWV from three-band observations and delivers high-quality PWV products, which fills the data gap for high-resolution, spatially continuous PWV information.