{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,6]],"date-time":"2026-03-06T04:23:00Z","timestamp":1772770980945,"version":"3.50.1"},"reference-count":50,"publisher":"MDPI AG","issue":"8","license":[{"start":{"date-parts":[[2022,4,15]],"date-time":"2022-04-15T00:00:00Z","timestamp":1649980800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Precipitable water vapor can be estimated from the Global Navigation Satellite System (GNSS) signal\u2019s zenith wet delay (ZWD) by multiplying a conversion factor, which is a function of weighted mean temperature (Tm) over the GNSS station. Obtaining Tm is an important step in GNSS precipitable water vapor (PWV) conversion. In this study, aiming at the problem that Tm is affected by space and time, observations from seven radiosonde stations in the Yangtze River Delta region of China during 2015\u22122016 were used to establish both linear and nonlinear multifactor regional Tm model (RTM). Compared with the Bevis model, the results showed that the bias of yearly one-factor RTM, two-factor RTM and three-factor RTM was reduced by 0.55 K, 0.68 K and 0.69 K, respectively. Meanwhile, the RMSE of yearly one-factor, two-factor and three-factor RTM was reduced by 0.56 K, 0.80 K and 0.83 K, respectively. Compared with the yearly three-factor linear RTM, the mean bias and RMSE of the linear seasonal three-factor RTMs decreased by 0.06 K and 0.10 K, respectively. The precision of nonlinear seasonal three-factor RTMs is comparable to linear seasonal three-factor RTMs, but the expressions of the linear RTMs are easier to use. Therefore, linear seasonal three-factor RTMs are more suitable for calculating Tm and are recommended to use for PWV conversion in the Yangtze River Delta region.<\/jats:p>","DOI":"10.3390\/rs14081909","type":"journal-article","created":{"date-parts":[[2022,4,19]],"date-time":"2022-04-19T02:39:31Z","timestamp":1650335971000},"page":"1909","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":11,"title":["Weighted Mean Temperature Modelling Using Regional Radiosonde Observations for the Yangtze River Delta Region in China"],"prefix":"10.3390","volume":"14","author":[{"ORCID":"https:\/\/orcid.org\/0000-0003-3952-4621","authenticated-orcid":false,"given":"Li","family":"Li","sequence":"first","affiliation":[{"name":"Research Center of Beidou Navigation and Environmental Remote Sensing, Suzhou University of Science and Technology, No. 99 Xuefu Road, Huqiu District, Suzhou 215009, China"}]},{"given":"Yuan","family":"Li","sequence":"additional","affiliation":[{"name":"School of Earth Sciences and Engineering, Hohai University, No. 8 Focheng West Road, Jiangning District, Nanjing 211100, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-8262-1035","authenticated-orcid":false,"given":"Qimin","family":"He","sequence":"additional","affiliation":[{"name":"Research Center of Beidou Navigation and Environmental Remote Sensing, Suzhou University of Science and Technology, No. 99 Xuefu Road, Huqiu District, Suzhou 215009, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-1720-6630","authenticated-orcid":false,"given":"Xiaoming","family":"Wang","sequence":"additional","affiliation":[{"name":"Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China"}]}],"member":"1968","published-online":{"date-parts":[[2022,4,15]]},"reference":[{"key":"ref_1","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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