Forecasting Rainfall Event and Rainfall Duration Directly Using GPS‐Derived Zenith Wet Delay Estimates

Abstract Rainfall forecasting based on the Global Navigation Satellite System (GNSS) plays an important role in GNSS‐meteorology applications. However, traditional methods that rely on GPS‐ZTD (Zenith Total Delay) are limited by the influence of ZHD (Zenith Hydrostatic Delay), while those based on GPS‐PWV (Precipitable Water Vapor) depend heavily on external meteorological parameters. Moreover, most existing studies have not addressed the forecasting of rainfall duration. In this study, the feasibility of rainfall forecasting using GPS‐ZWD (zenith wet delay) is demonstrated through theoretical analysis and correlation assessment. A new method is proposed for forecasting light, medium, and heavy rainfall events, as well as rainfall duration, directly based on GPS‐ZWD. Data sets collected from 2010 to 2020 in Hong Kong are used to evaluate the accuracy of GPS‐ZWD and establish a forecasting threshold model, while data from 2021 to 2022 are employed to validate the proposed method. Experimental results show that (a) the correlation coefficients between GPS‐ZWD and ERA5‐ZWD, and between GPS‐ZWD and NCEP‐ZWD, are 0.99 and 0.93, respectively; (b) the true rates for light, medium, and heavy rainfall event forecasting are 83.10%, 93.25%, and 100% based on GPS‐ZWD. The average true rate is 88.28%, which is comparable to the methods based on GPS‐PWV and better than GPS‐ZTD methods; (c) for rainfall duration forecasting, the average true rates for light, medium, and heavy rainfall are 74.55%, 85.35%, and 96.29%, respectively. These results indicate that the proposed methods for rainfall event and rainfall duration forecasting based on GPS‐ZWD demonstrate strong performance and hold significant potential for advancing GNSS‐meteorology applications.