A Fusion Model Enabling Encryption of Ground-Based GNSS ZTD Datasets for Enhanced Precipitation Forecasting

Recent decades have witnessed the emergence of the Global Navigation Satellite System (GNSS) Zenith Tropospheric Delay (ZTD) as a pivotal element within the domain of GNSS meteorology. The spatial density of ZTD data is contingent on the number of regional GNSS stations, thereby influencing its application in regional meteorology. This research presents a novel model, GWR-CNN (Geographically Weighted RegressionConvolutional Neural Networks), which utilizes convolutional neural networks to address geographically weighted regression residuals. Utilizing the French region as an illustrative case, this model necessitates solely the acquisition of positional information (comprising precise longitude, latitude, and height) and the ZTD values of the GNSS stations to execute high-precision interpolation calculations for regional ZTD. It is noteworthy that the outcomes of high-precision interpolation for ZTD can closely approximate actual observations, thereby augmenting the predictive capability of the Weather Research and Forecasting (WRF) model for regional rainfall through the process of data assimilation.