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Large-Scale Diagnostics of Tropical Cyclogenesis Potential Using Environment Variability Metrics and Logistic Regression Models

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  • 1 The Pennsylvania State University, University Park, Pennsylvania
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Abstract

The authors propose that inclusion of medium- to high-frequency variability information will provide improved metrics of tropical cyclogenesis (TCG) applicable to climate GCM diagnostics. Capabilities of the Community Atmosphere Model version 3.1 (CAM3.1) GCM are assessed for detecting both large-scale and localized conditions for TCG in the tropical North Atlantic by comparison with the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) and observed TCG occurrences. CAM3.1 seasonality of large-scale environmental factors conducive to TCG compares favorably with the ERA-40. It is determined that most of the TCG-related high-frequency temporal variability in the ERA-40 is explained by dynamic variability; each of the CAM3.1 ensemble members has lower variability in these dynamic fields. Seventeen environmental variables are evaluated as potential indicators of TCG activity based on daily anomalous variability with respect to a 15-day base period. Principal component analysis (PCA) is employed to synthesize these into a set of uncorrelated parameters. ERA-40 PCA composite variables are used to develop logistic and Poisson regression models for TCG detection and frequency in the North Atlantic main development region. Some skill metrics for the logistic model are promising, but the threat score and hit rate signify that further development of the logistic regression model is warranted; results from the Poisson regression model based on the same inputs are weaker, implying that weighting by TCG counts does not improve the results. These findings indicate merit in incorporating medium- to high-frequency variability in TCG metrics for diagnostics of seasonal activity and for application to climate models.

Corresponding author address: Jenni L. Evans, Department of Meteorology, The Pennsylvania State University, 503 Walker Building, University Park, PA 16802. E-mail: evans@meteo.psu.edu

Abstract

The authors propose that inclusion of medium- to high-frequency variability information will provide improved metrics of tropical cyclogenesis (TCG) applicable to climate GCM diagnostics. Capabilities of the Community Atmosphere Model version 3.1 (CAM3.1) GCM are assessed for detecting both large-scale and localized conditions for TCG in the tropical North Atlantic by comparison with the 40-yr European Centre for Medium-Range Weather Forecasts Re-Analysis (ERA-40) and observed TCG occurrences. CAM3.1 seasonality of large-scale environmental factors conducive to TCG compares favorably with the ERA-40. It is determined that most of the TCG-related high-frequency temporal variability in the ERA-40 is explained by dynamic variability; each of the CAM3.1 ensemble members has lower variability in these dynamic fields. Seventeen environmental variables are evaluated as potential indicators of TCG activity based on daily anomalous variability with respect to a 15-day base period. Principal component analysis (PCA) is employed to synthesize these into a set of uncorrelated parameters. ERA-40 PCA composite variables are used to develop logistic and Poisson regression models for TCG detection and frequency in the North Atlantic main development region. Some skill metrics for the logistic model are promising, but the threat score and hit rate signify that further development of the logistic regression model is warranted; results from the Poisson regression model based on the same inputs are weaker, implying that weighting by TCG counts does not improve the results. These findings indicate merit in incorporating medium- to high-frequency variability in TCG metrics for diagnostics of seasonal activity and for application to climate models.

Corresponding author address: Jenni L. Evans, Department of Meteorology, The Pennsylvania State University, 503 Walker Building, University Park, PA 16802. E-mail: evans@meteo.psu.edu
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