On the Potential of Using GNSS-Derived Precipitable Water Vapor From 5G Base Stations to Improve the Model for Typhoon Precipitation Forecasting

Global navigation satellite system (GNSS) timing modules from 5G base stations enable precipitable water vapor (PWV) retrieval, offering a promising approach to overcome the limitations of conventional meteorological stations in atmospheric monitoring. In this study, we retrieved PWV values (referred to as 5G-PWV) based on GNSS observations from 5G base stations in Zhoushan City, Zhejiang Province, China, using data from September to November 2024. The results were compared with ERA5 reanalysis data (ERA5-PWV) and observations from a dedicated GNSS station in Zhoushan (ZJZS-PWV). The 5G PWV data showed consistent temporal variability with both ERA5-PWV and ZJZS-PWV. After applying a bi-weight quality control algorithm, comparisons against ERA5-PWV revealed a mean bias of −1.07 mm, a root mean square error of 8.03 mm, and a correlation coefficient of 0.828 for 5G-PWV. We further conducted assimilation and forecast experiments using the CMA MESO (China Meteorological Administration Mesoscale Model) coupled with its three-dimensional variational data assimilation system for two typhoon cases: Bebinca and Krathon. Validation of precipitation forecasts—with and without assimilation of 5G PWV—showed that the assimilation of 5G-PWV improved the threat score (TS). After assimilating 5G-PWV, both false alarms and missed alarms across different precipitation intensity thresholds were slightly reduced. The improved forecast performance is attributed to effective adjustments in the initial humidity field through the assimilation of 5G-PWV, as well as subsequent enhancements in the model's dynamic conditions via precipitation feedback. Our study demonstrates that GNSS derived PWV from 5G base stations has clear potential to improve typhoon-related precipitation forecasting.