{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,2,28]],"date-time":"2026-02-28T18:17:46Z","timestamp":1772302666115,"version":"3.50.1"},"reference-count":31,"publisher":"MDPI AG","issue":"6","license":[{"start":{"date-parts":[[2021,3,11]],"date-time":"2021-03-11T00:00:00Z","timestamp":1615420800000},"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":["41975066"],"award-info":[{"award-number":["41975066"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Sensors"],"abstract":"<jats:p>Precipitation has an important impact on people\u2019s daily life and disaster prevention and mitigation. However, it is difficult to provide more accurate results for rainfall nowcasting due to spin-up problems in numerical weather prediction models. Furthermore, existing rainfall nowcasting methods based on machine learning and deep learning cannot provide large-area rainfall nowcasting with high spatiotemporal resolution. This paper proposes a dual-input dual-encoder recurrent neural network, namely Rainfall Nowcasting Network (RN-Net), to solve this problem. It takes the past grid rainfall data interpolated by automatic weather stations and doppler radar mosaic data as input data, and then forecasts the grid rainfall data for the next 2 h. We conduct experiments on the Southeastern China dataset. With a threshold of 0.25 mm, the RN-Net\u2019s rainfall nowcasting threat scores have reached 0.523, 0.503, and 0.435 within 0.5 h, 1 h, and 2 h. Compared with the Weather Research and Forecasting model rainfall nowcasting, the threat scores have been increased by nearly four times, three times, and three times, respectively.<\/jats:p>","DOI":"10.3390\/s21061981","type":"journal-article","created":{"date-parts":[[2021,3,11]],"date-time":"2021-03-11T20:17:40Z","timestamp":1615493860000},"page":"1981","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":33,"title":["RN-Net: A Deep Learning Approach to 0\u20132 Hour Rainfall Nowcasting Based on Radar and Automatic Weather Station Data"],"prefix":"10.3390","volume":"21","author":[{"given":"Fuhan","family":"Zhang","sequence":"first","affiliation":[{"name":"School of Computer, National University of Defense Technology, Changsha 410000, China"}]},{"given":"Xiaodong","family":"Wang","sequence":"additional","affiliation":[{"name":"School of Computer, National University of Defense Technology, Changsha 410000, China"}]},{"given":"Jiping","family":"Guan","sequence":"additional","affiliation":[{"name":"School of Meteorology and Oceanography, National University of Defense Technology, Changsha 410000, China"}]},{"given":"Meihan","family":"Wu","sequence":"additional","affiliation":[{"name":"School of Computer, National University of Defense Technology, Changsha 410000, China"}]},{"given":"Lina","family":"Guo","sequence":"additional","affiliation":[{"name":"School of Computer, National University of Defense Technology, Changsha 410000, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,3,11]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"2995","DOI":"10.1175\/MWR2828.1","article-title":"Using temporal modes of rainfall to evaluate the performance of a numerical weather prediction model","volume":"132","author":"Knievel","year":"2004","journal-title":"Mon. 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