Editorial Type:
Article
Article Type:
Research Article

Improving Tropical Cyclogenesis Statistical Model Forecasts through the Application of a Neural Network Classifier

Christopher C. Hennon Department of Atmospheric Sciences, University of North Carolina—Asheville, Asheville, North Carolina

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Caren Marzban Center for Analysis and Prediction of Storms, University of Oklahoma, Norman, Oklahoma, and Department of Statistics and Applied Physics Laboratory, University of Washington, Seattle, Washington

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Jay S. Hobgood Department of Geography, The Ohio State University, Columbus, Ohio

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Print Publication:
01 Dec 2005
DOI:
https://doi.org/10.1175/WAF890.1
Page(s):
1073–1083
Restricted access

Abstract

A binary neural network classifier is evaluated against linear discriminant analysis within the framework of a statistical model for forecasting tropical cyclogenesis (TCG). A dataset consisting of potential developing cloud clusters that formed during the 1998–2001 Atlantic hurricane seasons is used in conjunction with eight large-scale predictors of TCG. Each predictor value is calculated at analysis time. The model yields 6–48-h probability forecasts for genesis at 6-h intervals. Results consistently show that the neural network classifier performs comparably to or better than linear discriminant analysis on all performance measures examined, including probability of detection, Heidke skill score, and forecast reliability. Two case studies are presented to investigate model performance and the feasibility of adapting the model to operational forecast use.

Corresponding author address: Christopher C. Hennon, Dept. of Atmospheric Sciences, CPO 2450, University of North Carolina—Asheville, 1 University Heights, Asheville, NC 28804. Email: chennon@unca.edu

Abstract

A binary neural network classifier is evaluated against linear discriminant analysis within the framework of a statistical model for forecasting tropical cyclogenesis (TCG). A dataset consisting of potential developing cloud clusters that formed during the 1998–2001 Atlantic hurricane seasons is used in conjunction with eight large-scale predictors of TCG. Each predictor value is calculated at analysis time. The model yields 6–48-h probability forecasts for genesis at 6-h intervals. Results consistently show that the neural network classifier performs comparably to or better than linear discriminant analysis on all performance measures examined, including probability of detection, Heidke skill score, and forecast reliability. Two case studies are presented to investigate model performance and the feasibility of adapting the model to operational forecast use.

Corresponding author address: Christopher C. Hennon, Dept. of Atmospheric Sciences, CPO 2450, University of North Carolina—Asheville, 1 University Heights, Asheville, NC 28804. Email: chennon@unca.edu

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