{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T09:16:39Z","timestamp":1783070199240,"version":"3.54.6"},"reference-count":40,"publisher":"MDPI AG","issue":"13","license":[{"start":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T00:00:00Z","timestamp":1783036800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>The atmospheric weighted mean temperature (Tm) is critical for converting a zenith wet delay (ZWD) to precipitable water vapor (PWV). However, the existing Tm models still have certain limitations: Those driven by surface-measured parameters achieve high accuracy but depend heavily on in situ instruments, incurring high costs and lacking forecasting capability. Empirical models avoid measured data but fail to capture short-term Tm variations, leading to lower accuracy. Daily weather forecast data\u2014which are low-cost, readily available, and reflective of short-term changes\u2014offer a promising alternative. This study develops a gridded Tm model named CGTV-Tm, which couples temperature and water vapor pressure, using ERA5 reanalysis data over China (2019\u20132023). The model can be driven by daily weather forecast data. A dual vertical correction method is also proposed to improve performance. Validation against 2024 ERA5 and radiosonde data shows that CGTV-Tm achieves RMSEs of 2.38 K (vs. ERA5) and 2.64 K (vs. radiosonde), significantly outperforming the Bevis (3.61 K, 3.67 K), PTm (3.19 K, 2.94 K), and CGT-Tm (2.71 K, 3.08 K) models. When driven by daily weather forecast data, CGTV-Tm achieves an RMSE of 2.90 K, improving accuracy by 29.6% and 21.2% over the state-of-the-art empirical models GPT3 and HGPT2, respectively. These results demonstrate that CGTV-Tm not only surpasses traditional linear Tm models that rely solely on surface temperature but also, by using weather forecast data, it removes dependence on in situ instruments, offering a superior low-cost solution for real-time GNSS (Global Navigation Satellite System) PWV retrieval.<\/jats:p>","DOI":"10.3390\/s26134218","type":"journal-article","created":{"date-parts":[[2026,7,3]],"date-time":"2026-07-03T07:59:47Z","timestamp":1783065587000},"page":"4218","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["CGTV-Tm: A High-Accuracy Gridded Atmospheric Weighted Mean Temperature Model Coupling Surface Temperature and Water Vapor Pressure over China"],"prefix":"10.3390","volume":"26","author":[{"given":"Yaoshuang","family":"Zhang","sequence":"first","affiliation":[{"name":"Faculty of Geography, Tianjin Normal University, Tianjin 300387, China"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jian","family":"Mao","sequence":"additional","affiliation":[{"name":"Faculty of Geography, Tianjin Normal University, Tianjin 300387, China"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"1968","published-online":{"date-parts":[[2026,7,3]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"379","DOI":"10.1029\/RS022i003p00379","article-title":"Estimation of Tropospheric Delay for Microwaves from Surface Weather Data","volume":"22","author":"Askne","year":"1987","journal-title":"Radio Sci."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"15787","DOI":"10.1029\/92JD01517","article-title":"GPS Meteorology: Remote Sensing of Atmospheric Water Vapor Using the Global Positioning System","volume":"97","author":"Bevis","year":"1992","journal-title":"J. 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