High-Resolution Integrated Water Vapor Estimation Using the Gaussian Mixed Long Short-Term Memory Network: A Satellite-Based Inter-Comparison and Data-Fusion

Water vapor, the most influential greenhouse gas, is central to Earth’s climate system, affecting the hydrological cycle, energy balance, and atmospheric dynamics. Integrated water vapor (IWV) is a key variable for understanding these processes. However, conventional IWV retrieval methods—such as ground-based sensors, satellite observations, and numerical weather models (NWMs)—are often limited by spatial resolution, temporal continuity, and retrieval accuracy. To address these challenges, this study introduces a novel deep learning method, Gaussian mixture long short-term memory (GMLSTM)-high-resolution IWV estimation model (HIM), an HIM based on a GMLSTM framework. By integrating global navigation satellite system (GNSS) and NWM inputs, including weighted mean temperature, GMLSTM-HIM utilizes a bidirectional LSTM (Bi-LSTM) structure and probabilistic output sequences to improve IWV estimation accuracy while quantifying uncertainty arising from spatial heterogeneity. Compared to ERA5 and Vienna Mapping Functions 3 (VMF3), the model achieves average root mean square error (RMSE) reductions of 68.44% and 36.15%, respectively. The model’s performance is further evaluated through intercomparisons with moderate-resolution imaging spectroradiometer (MODIS) and Fengyun satellite-derived IWV products, highlighting both the accuracy of GMLSTM-HIM and the complementary strengths of satellite observations. ... mehrThe results suggest that, of the satellite datasets examined in this case study, the MODIS 5-km product exhibits the highest consistency with the GMLSTM-HIM model estimates, outperforming the high-resolution MODIS 1-km and FY-3D 1-km products in terms of product reliability (measured by RMSE and correlation). A data fusion strategy is also proposed, combining model and satellite estimates to preserve fine-scale details and enhance robustness. Overall, GMLSTM-HIM provides a robust framework for high-resolution IWV retrieval, with significant potential to advance atmospheric ...