High-resolution integrated water vapor estimation using the Gaussian mixed long short-term memory network : a satellite-based intercomparison and data fusion

Abstract

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. The 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 studies, climate surveillance, and operational weather forecasting within the remote sensing community.