Hourly Regional Precipitation Forecasting Using GNSS PWV and the ConvLSTM-ED Algorithm

Accurate hourly regional precipitation forecasting is essential for flood warning and weather-risk management, but station-scale global navigation satellite system (GNSS) precipitation models cannot fully describe the spatial evolution of rainfall between stations. This study proposes an hourly regional precipitation forecasting (HRPF) model that combines gridded GNSS-derived precipitable water vapor (PWV) and gridded precipitation with a convolutional long short-term memory (ConvLSTM) encoder–decoder network. Missing values in the hourly PWV and precipitation records from 69 GNSS-collocated meteorological stations in Taiwan Province during 2008–2012 were reconstructed using singular spectrum analysis and then resampled onto a 0.06° latitude–longitude grid using nearest-neighbor interpolation. The model uses PWV and precipitation fields from the preceding 1–6 h as two input channels and predicts the precipitation field one hour ahead. Lagged autocorrelation analysis showed strong spatiotemporal dependence in both precipitation and PWV fields, with coefficients above 0.86 and 0.85, respectively. In annual-scale external validation, the HRPF model maintained probability of detection (POD) values above 90%, false alarm rate (FAR) values below 55%, and threat score (TS) values of 46.22–46.58%. Seasonal external validation showed POD values of 88.8–97.8%, with the highest TS in summer (52.8–53.3%) owing to lower FAR values (45.8–46.4%). These results indicate that GNSS-derived PWV can provide useful spatial water-vapor information for one-hour-ahead regional precipitation forecasting, while model skill remains season-dependent over Taiwan’s complex terrain.