A China-Specific Near-Real-Time GNSS Water Vapor Retrieval Model Based on LightGBM

Atmospheric water vapor is a key atmospheric variable that regulates weather variability, the hydrological cycle, and climate processes. GNSS-based water vapor retrieval provides an effective approach for continuous and near-real-time monitoring of atmospheric water vapor. However, conventional meteorology-independent models still have limitations in regional adaptability, vertical accuracy, and the representation of nonlinear atmospheric variability. To address these limitations, this study proposes a China-specific near-real-time GNSS water vapor retrieval model, termed China LightGBM-based Zenith Hydrostatic Delay and Precipitable Water Vapor Model (CLZP), by integrating LightGBM with near-real-time GNSS observations. First, a high-accuracy gridded ZHD model, CLZP-ZHD, was developed using LightGBM to estimate ZHD from the surface to near the tropopause. Subsequently, a PWV residual compensation model, CLZP-PWV, was developed by incorporating multi-source features, including near-real-time GNSS ZTD, to mitigate error propagation in PWV retrieval. Validation results from both ERA5 and independent radiosonde datasets indicate that CLZP-ZHD reduces RMSE by approximately 25.4% and 28.2% relative to GPT3-ZHD and CTrop-ZHD in the ERA5-based validation, and by 21.7% and 23.0% in the radiosonde-based validation, respectively, while maintaining stable accuracy across different heights. For PWV retrieval, the ERA5-based validation of CLZP-PWV shows that it reduces RMSE by approximately 32.7% and 38.2% relative to GPT3-PWV and CTrop-PWV, respectively, and demonstrates improved performance in humid and climatically complex regions. These results suggest that, once trained, CLZP improves the accuracy and stability of near-real-time GNSS water vapor retrieval over China without requiring in situ meteorological observations during operational application, providing a practical approach for regional GNSS meteorological applications.