{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,10,14]],"date-time":"2025-10-14T00:42:52Z","timestamp":1760402572880,"version":"build-2065373602"},"reference-count":22,"publisher":"MDPI AG","issue":"1","license":[{"start":{"date-parts":[[2020,1,1]],"date-time":"2020-01-01T00:00:00Z","timestamp":1577836800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"National Key Research and Development Program of China &quot;Development of Meteorological Satellite Remote Sensing Technology and Platform for Global Monitoring, Assessments and Applications","award":["2018YFC1506500"],"award-info":[{"award-number":["2018YFC1506500"]}]},{"name":"Jiangsu basic research program youth fund project of China","award":["BK20150911"],"award-info":[{"award-number":["BK20150911"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Remote Sensing"],"abstract":"<jats:p>A new database, the tropical cyclones passive microwave brightness temperature (TCsBT) database including 6273 overpasses of 503 tropical cyclones (TC) was established from 6-year (2011\u20132016) Fengyun-3B (FY-3B) Microwave Radiation Imager (MWRI) Level-1 brightness temperature (TB) data and TC best-track data. An algorithm to estimate the TC intensity is developed using MWRI TB\u2019s from the database. The relationship between microwave TB and the maximum sustained surface wind (Vmax) of TCs is derived from the TCsBT database. A high correlation coefficient between MWRI channel TB and Vmax is found at the radial distance 50\u2013100 km near the TC inner core. Brightness temperatures at 10.65, 18.70, 23.8, and 36.5 GHz increase but 89 GHz TB\u2019s and polarization corrected TB at 36.5 GHz (PCT36.50) and PCT89 decrease with increasing TC intensity. The TCsBT database is further separated into the 5063 dependent samples (2010\u20132015) for the development of the TC intensity estimation algorithm and 1210 independent samples (2016) for algorithm verification. The stepwise regression method is used to select the optimal combination of storm intensity estimation variables from 12 candidate variables and four parameters (10.65h, 23.80v, 89.00v and PCT36.50) were selected for multiple regression models development. Among the four predictors, PCT36.50 contributes the most in estimating TC intensity. In addition, the errors are lower for estimating 6-h and 12-h future Vmax than estimating the current Vmax.<\/jats:p>","DOI":"10.3390\/rs12010147","type":"journal-article","created":{"date-parts":[[2020,1,3]],"date-time":"2020-01-03T04:43:03Z","timestamp":1578026583000},"page":"147","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Climatology of Passive Microwave Brightness Temperatures in Tropical Cyclones and their Relations to Storm Intensities as Seen by FY-3B\/MWRI"],"prefix":"10.3390","volume":"12","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-5480-1001","authenticated-orcid":false,"given":"Bo","family":"Qian","sequence":"first","affiliation":[{"name":"Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)\/ Joint International Research Laboratory of Climate and Environment Change (ILCEC)\/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)\/Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science &amp; Technology (NUIST), Nanjing 210044, China"},{"name":"Department of Earth &amp; Environment, Florida International University, Miami, FL 33199, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haiyan","family":"Jiang","sequence":"additional","affiliation":[{"name":"Department of Earth &amp; Environment, Florida International University, Miami, FL 33199, USA"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Fuzhong","family":"Weng","sequence":"additional","affiliation":[{"name":"Laboratory of Severe Weather, Beijing 100081, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ying","family":"Wu","sequence":"additional","affiliation":[{"name":"Key Laboratory of Meteorological Disaster, Ministry of Education (KLME)\/ Joint International Research Laboratory of Climate and Environment Change (ILCEC)\/Collaborative Innovation Center on Forecast and Evaluation of Meteorological Disasters (CIC-FEMD)\/Key Laboratory for Aerosol-Cloud-Precipitation of China Meteorological Administration, Nanjing University of Information Science &amp; Technology (NUIST), Nanjing 210044, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2020,1,1]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"420","DOI":"10.1175\/1520-0493(1975)103<0420:TCIAAF>2.0.CO;2","article-title":"Tropical Cyclone Intensity Analysis and Forecasting from Satellite Imagery","volume":"103","author":"Dvorak","year":"1975","journal-title":"Mon. 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