{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,20]],"date-time":"2025-11-20T12:07:00Z","timestamp":1763640420229,"version":"build-2065373602"},"reference-count":51,"publisher":"MDPI AG","issue":"12","license":[{"start":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T00:00:00Z","timestamp":1718582400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"Natural Science Foundation of China","award":["42222503"],"award-info":[{"award-number":["42222503"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>In this study, a tropical cyclone or typhoon rainfall forecast model based on Random Forest is developed to forecast the daily rainfall at 133 weather stations in China. The input factors to the model training process include rainfall observations during 1960\u20132018, typhoon information (position and intensity), station information (position and altitude), and properties of the western Pacific subtropical high. Model evaluation shows that besides the distance between a station and cyclone, the subtropical high properties are ranked very high in the model\u2019s feature importance, especially the subtropical ridgeline, and intensity. These aspects of the subtropical high influence the location and timing of typhoon landfall. The forecast model has a correlation coefficient of about 0.73, an Index of Agreement of nearly 0.8, and a mean bias of 1.28 mm based on the training dataset. Biases are consistently low, with both positive and negative signs, for target stations in the outer rainband (up to 1000 km, beyond which the model does not forecast) of typhoons. The range of biases is much larger for target stations in the inner-core (0\u2013200 km) region. In this region, the model mostly overestimates (underestimates) the small (large) rain rates. Cases study of Typhoon Doksuri and Talim in 2023, as independent cases, shows the high performance of the model in forecasting the peak rain rates and timing of their occurrence of the two impactful typhoons.<\/jats:p>","DOI":"10.3390\/rs16122207","type":"journal-article","created":{"date-parts":[[2024,6,17]],"date-time":"2024-06-17T12:48:40Z","timestamp":1718628520000},"page":"2207","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":3,"title":["Contribution from the Western Pacific Subtropical High Index to a Deep Learning Typhoon Rainfall Forecast Model"],"prefix":"10.3390","volume":"16","author":[{"ORCID":"https:\/\/orcid.org\/0009-0004-9079-5333","authenticated-orcid":false,"given":"Zhou","family":"Fang","sequence":"first","affiliation":[{"name":"School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 211800, China"}]},{"given":"Kevin K. W.","family":"Cheung","sequence":"additional","affiliation":[{"name":"School of Emergency Management, Nanjing University of Information Science and Technology, Nanjing 211800, China"}]},{"given":"Yuanjian","family":"Yang","sequence":"additional","affiliation":[{"name":"School of Atmospheric Physics, Nanjing University of Information Science and Technology, Nanjing 211800, China"}]}],"member":"1968","published-online":{"date-parts":[[2024,6,17]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e2023JD038813","DOI":"10.1029\/2023JD038813","article-title":"Urbanization Impacts on Tropical Cyclone Rainfall Extremes-Inferences from Observations and Convection-Permitting Model Experiments Over South China","volume":"128","author":"Hu","year":"2023","journal-title":"JGR Atmos."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"e2120335119","DOI":"10.1073\/pnas.2120335119","article-title":"Increased variability of the western Pacific subtropical high under greenhouse warming","volume":"119","author":"Yang","year":"2022","journal-title":"Proc. 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