{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,3]],"date-time":"2026-04-03T15:32:12Z","timestamp":1775230332822,"version":"3.50.1"},"reference-count":43,"publisher":"MDPI AG","issue":"16","license":[{"start":{"date-parts":[[2020,8,7]],"date-time":"2020-08-07T00:00:00Z","timestamp":1596758400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001809","name":"National Natural Science Foundation of China","doi-asserted-by":"publisher","award":["41501584"],"award-info":[{"award-number":["41501584"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>High-resolution precipitation field has been widely used in hydrological and meteorological modeling. This paper establishes the spatial and temporal distribution model of precipitation in Hubei Province from 2006 through 2014, based on the data of 75 meteorological stations. This paper applies a geographically and temporally weighted regression kriging (GTWRK) model to precipitation and assesses the effects of timescales and a time-weighted function on precipitation interpolation. This work\u2019s results indicate that: (1) the optimal timescale of the geographically and temporally weighted regression (GTWR) precipitation model is daily. The fitting accuracy is improved when the timescale is converted from months and years to days. The average mean absolute error (MAE), mean relative error (MRE), and the root mean square error (RMSE) decrease with scaling from monthly to daily time steps by 36%, 56%, and 35%, respectively, and the same statistical indexes decrease by 13%, 15%, and 14%, respectively, when scaling from annual to daily steps; (2) the time weight function based on an exponential function improves the predictive skill of the GTWR model by 3% when compared to geographically weighted regression (GWR) using a monthly time step; and (3) the GTWRK has the highest accuracy, and improves the MAE, MRE and RMSE by 3%, 10% and 1% with respect to monthly precipitation predictions, respectively, and by 3%, 10% and 5% concerning annual precipitation predictions, respectively, compared with the GWR results.<\/jats:p>","DOI":"10.3390\/rs12162547","type":"journal-article","created":{"date-parts":[[2020,8,7]],"date-time":"2020-08-07T10:16:23Z","timestamp":1596795383000},"page":"2547","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":17,"title":["Regional Precipitation Model Based on Geographically and Temporally Weighted Regression Kriging"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9131-6742","authenticated-orcid":false,"given":"Wei","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6239-3899","authenticated-orcid":false,"given":"Dan","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0972-7516","authenticated-orcid":false,"given":"Shengjie","family":"Zheng","sequence":"additional","affiliation":[{"name":"China Petroleum Pipeline Engineering CO., LTD., Langfang 065000, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-0277-7764","authenticated-orcid":false,"given":"Shuya","family":"Liu","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-5372-0659","authenticated-orcid":false,"given":"Hugo A.","family":"Lo\u00e1iciga","sequence":"additional","affiliation":[{"name":"Department of Geography, University of California, Santa Barbara, CA 93106, USA"}]},{"ORCID":"https:\/\/orcid.org\/0000-0003-2266-8477","authenticated-orcid":false,"given":"Wenkai","family":"Li","sequence":"additional","affiliation":[{"name":"School of Geography and Information Engineering, China University of Geosciences (Wuhan), Wuhan 430074, China"}]}],"member":"1968","published-online":{"date-parts":[[2020,8,7]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"1965","DOI":"10.1080\/01431161.2012.730157","article-title":"Multi-technique observations on precipitation and other related phenomena during cyclone Aila at a tropical location","volume":"34","author":"Bhattacharya","year":"2013","journal-title":"Int. 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