4.2 Article

Objective Tropical Cyclone Intensity Estimation Using Analogs of Spatial Features in Satellite Data

期刊

WEATHER AND FORECASTING
卷 28, 期 6, 页码 1446-1459

出版社

AMER METEOROLOGICAL SOC
DOI: 10.1175/WAF-D-13-00006.1

关键词

Tropical cyclones; Algorithms; Data mining; Data processing; Remote sensing; Satellite observations

资金

  1. Expeditions in Computing Program of the National Science Foundation [CCF-1029731]
  2. Direct For Computer & Info Scie & Enginr
  3. Div Of Information & Intelligent Systems [1029731] Funding Source: National Science Foundation
  4. Direct For Computer & Info Scie & Enginr
  5. Div Of Information & Intelligent Systems [1029711] Funding Source: National Science Foundation

向作者/读者索取更多资源

An objective method for estimating tropical cyclone (TC) intensity using historical hurricane satellite data (HURSAT) is developed and tested. This new method, referred to as feature analogs in satellite imagery (FASI), requires a TC's center location to extract azimuthal brightness temperature (BT) profiles from current imagery as well as BT profiles from imagery 6, 12, and 24 h prior. Instead of using regression techniques, the estimated TC intensity is determined from the 10 closest analogs to this TC based on the BT profiles using a k-nearest-neighbor algorithm. The FASI technique was trained and validated using intensity data from aircraft reconnaissance in the North Atlantic Ocean, where the data were restricted to include storms that are over water and south of 45 degrees N. This subset comprised 2016 observations from 165 storms during 1988-2006. Several tests were implemented to statistically justify the FASI algorithm using n-fold cross validation. The resulting average mean absolute intensity error was 10.9 kt (50% of estimates are within 10 kt, 1 kt = 0.51 m s(-1)) or 8.4 mb (50% of estimates are within 8 mb); its accuracy is on par with other objective techniques. This approach has the potential to provide global TC intensity estimates that could augment intensity estimates made by other objective techniques.

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