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

    Source: Weather and Forecasting:;2013:;volume( 028 ):;issue: 006::page 1446
    Author:
    Fetanat, Gholamreza
    ,
    Homaifar, Abdollah
    ,
    Knapp, Kenneth R.
    DOI: 10.1175/WAF-D-13-00006.1
    Publisher: American Meteorological Society
    Abstract: n 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°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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      Objective Tropical Cyclone Intensity Estimation Using Analogs of Spatial Features in Satellite Data

    URI
    http://yetl.yabesh.ir/yetl1/handle/yetl/4231651
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    contributor authorFetanat, Gholamreza
    contributor authorHomaifar, Abdollah
    contributor authorKnapp, Kenneth R.
    date accessioned2017-06-09T17:36:15Z
    date available2017-06-09T17:36:15Z
    date copyright2013/12/01
    date issued2013
    identifier issn0882-8156
    identifier otherams-87928.pdf
    identifier urihttp://onlinelibrary.yabesh.ir/handle/yetl/4231651
    description abstractn 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°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.
    publisherAmerican Meteorological Society
    titleObjective Tropical Cyclone Intensity Estimation Using Analogs of Spatial Features in Satellite Data
    typeJournal Paper
    journal volume28
    journal issue6
    journal titleWeather and Forecasting
    identifier doi10.1175/WAF-D-13-00006.1
    journal fristpage1446
    journal lastpage1459
    treeWeather and Forecasting:;2013:;volume( 028 ):;issue: 006
    contenttypeFulltext
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    DSpace software copyright © 2002-2015  DuraSpace
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