Research on Applicability of GNSS PWV Data in Northeast China and Its Application in Intelligent Precipitation Prediction

Abstract

Atmospheric water vapor is an important factor in the formation and evolution of extreme weather events, such as heavy rainfall, typhoons, and major droughts and floods. We analyzed the applicability of precipitable water vapor (PWV) values from the Global Navigation Satellite System Meteorology (GNSS/MET) stations in northeastern China. We examined the potential of PWV for precipitation forecasting using an enhanced bidirectional long short-term memory (BiLSTM) network. Using radiosonde data, the accuracy of GNSS/MET PWV was evaluated, and the diurnal and seasonal differences in retrieval errors were analyzed. Results show that diurnal differences in retrieval errors are insignificant, while seasonal differences are pronounced, which can be attributed to the seasonal distribution of precipitation. Through case analyses of rainstorms and severe convective events, this study concludes that GNSS PWV varies several hours ahead of precipitation. Building on the earlier analyses of applicability assessment and precipitation warning signal identification, the improved BiLSTM framework is employed to investigate the application of GNSS PWV in hourly precipitation forecasting. Feature extraction and data resampling were utilized to enhance the physical interpretability of the binary nature of precipitation prediction and improve the model's generalization capability. Validation with 873 randomly split testing samples revealed a classification accuracy of 86.3% for precipitation prediction, with a regression RMSE of 2.73 mm. The intelligent precipitation forecasting methodology developed in this research can be applied to public-sector precipitation monitoring and early warning services.