Assimilating GNSS Tropospheric Products and Quantitative Evaluation of Their Contributions to Numerical Weather Prediction

作者：Yongjie Ma, Qingzhi Zhao, Wanqiang Yao, Hongwu Guo, Jinfang Yin, Ying Xu, Yuan Zhai, Yibin Yao, Yuting Gao · 发表于：IEEE Transactions on Geoscience and Remote Sensing · 年份：2025 · DOI：10.1109/tgrs.2025.3559789 · 被引用次数：8 · 研究领域：Geophysics and Gravity Measurements、GNSS positioning and interference、Soil Moisture and Remote Sensing

Apart from the applications of navigation, positioning, and timing, the Global Navigation Satellite System (GNSS) plays an important role in improving the quality and reliability of numerical weather prediction (NWP) models. However, the difference and contribution of assimilating GNSS-derived Zenith Total Delay (ZTD) and Precipitable Water Vapor (PWV) to forecast result are less investigated, which becomes the focus of this study. A unified method of assimilating GNSS-derived ZTD/PWV is first proposed, and their difference and contribution to the forecasting performance of Weather Research and Forecasting (WRF) model are quantitatively evaluated by focusing on the multiple meteorological parameters, such as precipitation, relative humidity, temperature, and pressure. In addition, the effects of magnitude and seasonal characteristics of GNSS-derived ZTD/PWV on the WRF model are further analyzed during a case of severe convective weather. Central and eastern China is selected as the study area, and 287 meteorological stations, 452 GNSS/Met stations, and 11 radiosonde stations are selected over the whole year of 2018. Results indicate that the assimilation of GNSS-derived ZTD/PWV, particularly ZTD, enhances the forecast accuracy of different meteorological parameters, and the positive contribution degree increases as the magnitude of GNSS-derived ZTD/PWV increases. Compared with the traditional method, the root mean square error reductions of precipitation, relative humidity, t...