High-Resolution Water Vapor Field Generation Using the Gaussian Mixed Long Short-Term Memory Network: A Satellite-Based Inter-Comparison in Germany

Water vapor, the most significant greenhouse gas, plays a critical role in the Earth's climate system, influencing the hydrological cycle, energy distribution, and atmospheric dynamics. Integrated Water Vapor (IWV) serves as a vital parameter for understanding these processes. While traditional methods, including ground-based instruments and satellite observations, provide IWV measurements, they are often limited by spatial resolution, coverage, and accuracy. Advances in numerical weather models (NWM) and remote sensing have improved large-scale IWV estimations but still face challenges in capturing high accuracy. To address these limitations, this study introduces a novel approach using a Gaussian Mixed Long Short-Term Memory (GM-LSTM) deep learning model to generate high-resolution water vapor fields (WVF) with enhanced spatial and temporal resolution. The GM-LSTM integrates numerical weather models (NWM) and GNSS data to create an adaptive mapping for zenith wet delay (ZWD) estimation, which is then converted to IWV. By utilizing a bidirectional Long Short-Term Memory (Bi-LSTM) architecture and probabilistic density distribution sequences, the model not only improves ZWD estimation accuracy but also quantifies inherent uncertainties due to spatial heterogeneity. Compared with ERA5 and VMF3, the proposed GM-LSTM achieves an average RMSE reduction of 67.68% and 48.74%. The proposed WVF generation method was validated through an inter-comparison with MODIS and Fengyun satellite products, highlighting its superior accuracy and reliability. This study demonstrates the potential of deep learning models like GM-LSTM to overcome the limitations of traditional techniques, providing a transformative tool for high-resolution IWV estimation and supporting advancements in climate monitoring and weather prediction.

Keywords:

WVF, Inter-comparison, IWV, GM-LSTM, GNSS, MODIS, Fengyun satellite