{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,12]],"date-time":"2025-10-12T02:09:55Z","timestamp":1760234995381,"version":"build-2065373602"},"reference-count":26,"publisher":"MDPI AG","issue":"14","license":[{"start":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T00:00:00Z","timestamp":1625529600000},"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":["No.  41775065"],"award-info":[{"award-number":["No.  41775065"]}],"id":[{"id":"10.13039\/501100001809","id-type":"DOI","asserted-by":"publisher"}]},{"name":"Key Program for International S&amp;T Cooperation Projects of China","award":["No. 2017YFE0107700"],"award-info":[{"award-number":["No. 2017YFE0107700"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>Estimation of maximum wind speed associated with tropical cyclones (TCs) is crucial to evaluate potential wind destruction. The Holland B parameter is the key parameter of TC parametric wind field models. It plays an essential role in describing the radial distribution characteristics of a TC wind field and has been widely used in TC disaster risk evaluation. In this study, a backpropagation neural network (BPNN) is developed to estimate the Holland B parameter (Bs) in TC surface wind field model. The inputs of the BPNN include different combinations of TC minimum center pressure difference (\u0394p), latitude, radius of maximum wind speed, translation speed and intensity change rate from the best-track data of the Joint Typhoon Warning Center (JTWC). We find that the BPNN exhibits the best performance when only inputting TC central pressure difference. The Bs estimated from BPNN are compared with those calculated from previous statistical models. Results indicate that the proposed BPNN can describe well the nonlinear relation between Bs and \u0394p. It is also found that the combination of BPNN and Holland\u2019s wind pressure model can significantly improve the maximum wind speed underestimation and overestimation of the two existing wind pressure models (AH77 and KZ07) for super typhoons.<\/jats:p>","DOI":"10.3390\/rs13142653","type":"journal-article","created":{"date-parts":[[2021,7,6]],"date-time":"2021-07-06T11:36:44Z","timestamp":1625571404000},"page":"2653","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":6,"title":["Estimating the Key Parameter of a Tropical Cyclone Wind Field Model over the Northwest Pacific Ocean: A Comparison between Neural Networks and Statistical Models"],"prefix":"10.3390","volume":"13","author":[{"given":"Ziyao","family":"Sun","sequence":"first","affiliation":[{"name":"Shanghai Typhoon Institute and Laboratory of Typhoon Forecast Technique, China Meteorological Administration, Shanghai 200030, China"}]},{"ORCID":"https:\/\/orcid.org\/0000-0001-6569-1998","authenticated-orcid":false,"given":"Biao","family":"Zhang","sequence":"additional","affiliation":[{"name":"School of Marine Sciences, Nanjing University of Information Science and Technology, Nanjing 210044, China"},{"name":"Southern Marine Science and Engineering Guangdong Laboratory (Zhuhai), Zhuhai 510275, China"},{"name":"Fisheries and Oceans Canada, Bedford Institute of Oceanography, Dartmouth, NS B2Y 4A2, Canada"}]},{"given":"Jie","family":"Tang","sequence":"additional","affiliation":[{"name":"Shanghai Typhoon Institute and Laboratory of Typhoon Forecast Technique, China Meteorological Administration, Shanghai 200030, China"}]}],"member":"1968","published-online":{"date-parts":[[2021,7,6]]},"reference":[{"key":"ref_1","unstructured":"Harper, B.A. (2002). 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