Optimized Approach for Near-Real-Time 3D Water Vapor Estimation Technique Using the Informer Model in GNSS

3D water vapor data are now being used for numerical weather prediction, which is effective for monitoring extreme weather events and improving forecast quality. This study focuses on reconstructing the 3D water vapor field using Global Navigation Satellite System (GNSS) water vapor tomography techniques, addressing two main aspects: (1) Achieving high-precision real-time 3D water vapor predictions as initial values. In this study, a novel high-precision water vapor prediction model, the Informer-WV model, is introduced, and its predictions can be served as the initial values for tomography. We trained the Informer-WV model using 5 years of historical ERA5 reanalysis data in Hong Kong region to obtain the real-time values from sliding-window predictions. The model demonstrated a remarkable prediction accuracy, with an annual root mean square error (RMSE) better than 0.80 g/m³ compared to the actual ERA5 values. (2) The upper boundary height of the 3D tomography grid is determined by the vertical precision of initial values, which is adjusted to 5.2 km in this study, and the reconstructed SWVs are calculated with the predictions. By benchmarking against radiosonde data, we analyzed the near-real-time tomography inversion results for the two weakest prediction periods of the model. The RMSE of the water vapor inversion values derived from the optimized method was reduced from 1.55 to 1.26 g/m³, and the most significant improvement is at about 2-5 km. This approach not only improved the accuracy by 19% relative to the initial predictions but also significantly outperformed the traditional tomography method.