{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,18]],"date-time":"2026-04-18T03:54:34Z","timestamp":1776484474769,"version":"3.51.2"},"reference-count":39,"publisher":"MDPI AG","issue":"23","license":[{"start":{"date-parts":[[2024,12,2]],"date-time":"2024-12-02T00:00:00Z","timestamp":1733097600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Nature Science Foundation of China","award":["12303071"],"award-info":[{"award-number":["12303071"]}]},{"name":"National Nature Science Foundation of China","award":["23YF1455500"],"award-info":[{"award-number":["23YF1455500"]}]},{"name":"National Nature Science Foundation of China","award":["2023M743653"],"award-info":[{"award-number":["2023M743653"]}]},{"name":"National Nature Science Foundation of China","award":["TC220A04A-80"],"award-info":[{"award-number":["TC220A04A-80"]}]},{"name":"Shanghai Science and Technology Plan Project","award":["12303071"],"award-info":[{"award-number":["12303071"]}]},{"name":"Shanghai Science and Technology Plan Project","award":["23YF1455500"],"award-info":[{"award-number":["23YF1455500"]}]},{"name":"Shanghai Science and Technology Plan Project","award":["2023M743653"],"award-info":[{"award-number":["2023M743653"]}]},{"name":"Shanghai Science and Technology Plan Project","award":["TC220A04A-80"],"award-info":[{"award-number":["TC220A04A-80"]}]},{"name":"China Postdoctoral Science Foundation","award":["12303071"],"award-info":[{"award-number":["12303071"]}]},{"name":"China Postdoctoral Science Foundation","award":["23YF1455500"],"award-info":[{"award-number":["23YF1455500"]}]},{"name":"China Postdoctoral Science Foundation","award":["2023M743653"],"award-info":[{"award-number":["2023M743653"]}]},{"name":"China Postdoctoral Science Foundation","award":["TC220A04A-80"],"award-info":[{"award-number":["TC220A04A-80"]}]},{"name":"Ministry of Industry and Information Technology of China through the High Precision Timing Service Project","award":["12303071"],"award-info":[{"award-number":["12303071"]}]},{"name":"Ministry of Industry and Information Technology of China through the High Precision Timing Service Project","award":["23YF1455500"],"award-info":[{"award-number":["23YF1455500"]}]},{"name":"Ministry of Industry and Information Technology of China through the High Precision Timing Service Project","award":["2023M743653"],"award-info":[{"award-number":["2023M743653"]}]},{"name":"Ministry of Industry and Information Technology of China through the High Precision Timing Service Project","award":["TC220A04A-80"],"award-info":[{"award-number":["TC220A04A-80"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Empirical tropospheric models can improve the performance of GNSS precise point positioning (PPP) by providing a priori zenith tropospheric delay (ZTD) information. However, existing models experience insufficient ZTD profile refinement, inadequate correction for systematic bias between the ZTD used in empirical modelling and the GNSS ZTD, and low time efficiency in model updating as more data become available. Therefore, a refined spatiotemporal empirical ZTD model was developed in this study on the basis of the fifth generation of European Centre for Medium-Range Weather Forecasts Reanalysis (ERA5) data and GNSS data. First, an ENM-R profile model was established by refining the modelling height of the negative exponential function model (ENM). Second, a regression kriging interpolation method was designed to model the systematic bias correction between the ERA5 ZTD and the GNSS ZTD. Last, the final refined ZTD model, ENM-RS, was established by introducing systematic bias correction into ENM-R. Experiments suggest that, compared with the ENM-R and GPT3 models, ENM-RS can effectively suppress systematic bias and improve ZTD modelling accuracy by 10~17%. To improve model update efficiency, the idea of updating an empirical model with sequential least square (SLSQ) adjustment is proposed for the first time. When ENM-RS is modelled via 12 years of ERA data, our method can reduce the time consumption to one-fifth of that of the traditional method. The benefits of our ENM-RS model are evaluated with PPP. The results show that relative to PPP solutions with ENM-R- and GPT3-derived ZTD constraints as well as no constraint, the ENM-RS ZTD constraint can decrease PPP convergence time by approximately 10~30%.<\/jats:p>","DOI":"10.3390\/rs16234515","type":"journal-article","created":{"date-parts":[[2024,12,3]],"date-time":"2024-12-03T04:04:04Z","timestamp":1733198644000},"page":"4515","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["A Refined Spatiotemporal ZTD Model of the Chinese Region Based on ERA and GNSS Data"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0007-5476-120X","authenticated-orcid":false,"given":"Yongzhao","family":"Fan","sequence":"first","affiliation":[{"name":"GNSS Research Center, Wuhan University, Wuhan 430079, China"}]},{"given":"Fengyu","family":"Xia","sequence":"additional","affiliation":[{"name":"Shanghai Astronomical Observatory, Chinese Academy of Sciences, Shanghai 200030, China"}]},{"given":"Zhimin","family":"Sha","sequence":"additional","affiliation":[{"name":"GNSS Research Center, Wuhan University, Wuhan 430079, China"}]},{"given":"Nana","family":"Jiang","sequence":"additional","affiliation":[{"name":"School of Geodesy and Geomatics, Wuhan University, Luoyu Road 129, Wuhan 430079, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,12,2]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"5005","DOI":"10.1029\/96JB03860","article-title":"Precise point positioning for the efficient and robust analysis of GPS data from large networks","volume":"102","author":"Zumberge","year":"1997","journal-title":"J. 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