Research on Monitoring Oceanic Precipitable Water Vapor and Short-Term Rainfall Forecasting Using Low-Cost Global Navigation Satellite System Buoy

This study utilizes a low-cost Global Navigation Satellite System (GNSS) buoy platform, combined with multi-system GNSS data, to investigate the impact of GNSS signal quality and multipath effects on the accuracy of atmospheric precipitable water vapor (PWV) retrievals. It also explores the methods for oceanic rainfall event forecasting and precipitation prediction based on GNSS-PWV. By analyzing the data quality from various GNSS systems and using the European Centre for Medium-Range Weather Forecasts (ECMWF) ERA5 dataset as a reference, the study assesses the accuracy of PWV retrievals in dynamic marine environments. The results show that the GNSS-derived PWV from the buoy platform is highly consistent with ERA5 data in both trend and characteristics, with an RMSE of 3.8 mm for the difference between GNSS-derived PWV and ERA5 PWV. To enhance rainfall forecasting accuracy, a balanced threshold selection (BTS) method is proposed, significantly improving the balance between the probability of detection (POD) and false alarm rate (FAR). Furthermore, a Random Forest model based on multiple meteorological parameters optimizes precipitation forecasting, especially in reducing false alarms. Additionally, a particle swarm optimization (PSO)-based BP Neural Network model for rainfall prediction achieves high precision, with an R2 of 97.8%, an average absolute error of 0.08 mm, and an RMSE of 0.1 mm. The findings demonstrate the potential of low-cost GNSS buoy for monitoring atmospheric water vapor and short-term rainfall forecasting in dynamic marine environments.