{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,4,11]],"date-time":"2026-04-11T12:19:34Z","timestamp":1775909974423,"version":"3.50.1"},"reference-count":45,"publisher":"MDPI AG","issue":"22","license":[{"start":{"date-parts":[[2024,11,18]],"date-time":"2024-11-18T00:00:00Z","timestamp":1731888000000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Natural Science Foundation of China","award":["62076137"],"award-info":[{"award-number":["62076137"]}]},{"name":"National Natural Science Foundation of China","award":["61931004"],"award-info":[{"award-number":["61931004"]}]},{"name":"National Natural Science Foundation of China","award":["BK 20211539"],"award-info":[{"award-number":["BK 20211539"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["62076137"],"award-info":[{"award-number":["62076137"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["61931004"],"award-info":[{"award-number":["61931004"]}]},{"name":"Natural Science Foundation of Jiangsu Province","award":["BK 20211539"],"award-info":[{"award-number":["BK 20211539"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this research, we introduce a novel method leveraging the Transformer architecture to generate high-fidelity precipitation model outputs. This technique emulates the statistical characteristics of high-resolution datasets while substantially lowering computational expenses. The core concept involves utilizing a blend of coarse and fine-grained simulated precipitation data, encompassing diverse spatial resolutions and geospatial distributions, to instruct Transformer in the transformation process. We have crafted an innovative ST-Transformer encoder component that dynamically concentrates on various regions, allocating heightened focus to critical spatial zones or sectors. The module is capable of studying dependencies between different locations in the input sequence and modeling at different scales, which allows it to fully capture spatiotemporal correlations in meteorological element data, which is also not available in other downscaling methods. This tailored module is instrumental in enhancing the model\u2019s ability to generate outcomes that are not only more realistic but also more consistent with physical laws. It adeptly mirrors the temporal and spatial distribution in precipitation data and adeptly represents extreme weather events, such as heavy and enduring storms. The efficacy and superiority of our proposed approach are substantiated through a comparative analysis with several cutting-edge forecasting techniques. This evaluation is conducted on two distinct datasets, each derived from simulations run by regional climate models over a period of 4 months. The datasets vary in their spatial resolutions, with one featuring a 50 km resolution and the other a 12 km resolution, both sourced from the Weather Research and Forecasting (WRF) Model.<\/jats:p>","DOI":"10.3390\/rs16224292","type":"journal-article","created":{"date-parts":[[2024,11,19]],"date-time":"2024-11-19T06:06:54Z","timestamp":1731996414000},"page":"4292","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":8,"title":["Successful Precipitation Downscaling Through an Innovative Transformer-Based Model"],"prefix":"10.3390","volume":"16","author":[{"given":"Fan","family":"Yang","sequence":"first","affiliation":[{"name":"School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China"}]},{"given":"Qiaolin","family":"Ye","sequence":"additional","affiliation":[{"name":"College of Information Science and Technology, Nanjing Forestry University, Nanjing 210037, China"}]},{"given":"Kai","family":"Wang","sequence":"additional","affiliation":[{"name":"Nanjing NARl Information & Communication Technology Co., Ltd., Nanjing 211815, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6465-8678","authenticated-orcid":false,"given":"Le","family":"Sun","sequence":"additional","affiliation":[{"name":"School of Computer Science, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Jiangsu Collaborative Innovation Center of Atmospheric Environment and Equipment Technology (CICAEET), Nanjing University of Information Science and Technology, Nanjing 210044, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,11,18]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"50","DOI":"10.1016\/j.atmosres.2015.02.010","article-title":"Comparison and evaluation of high resolution precipitation estimation products in Urmia Basin-Iran","volume":"158","author":"Ghajarnia","year":"2015","journal-title":"Atmos. 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