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

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  • 1 North Carolina Agricultural and Technical State University, Greensboro, North Carolina
  • 2 NOAA/National Climatic Data Center, Asheville, North Carolina
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Abstract

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°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.

Corresponding author address: Abdollah Homaifar, North Carolina A&T State University, 1601 East Market St., Greensboro, NC 27411. E-mail: homaifar@ncat.edu

Abstract

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°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.

Corresponding author address: Abdollah Homaifar, North Carolina A&T State University, 1601 East Market St., Greensboro, NC 27411. E-mail: homaifar@ncat.edu
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