Forecasting Tropical Cyclogenesis over the Atlantic Basin Using Large-Scale Data

Christopher C. Hennon The Ohio State University, Columbus, Ohio

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Jay S. Hobgood The Ohio State University, Columbus, Ohio

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Abstract

A new dataset of tropical cloud clusters, which formed or propagated over the Atlantic basin during the 1998–2000 hurricane seasons, is used to develop a probabilistic prediction system for tropical cyclogenesis (TCG). Using data from the National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) reanalysis (NNR), eight large-scale predictors are calculated at every 6-h interval of a cluster's life cycle. Discriminant analysis is then used to find a linear combination of the predictors that best separates the developing cloud clusters (those that became tropical depressions) and nondeveloping systems. Classification results are analyzed via composite and case study points of view.

Despite the linear nature of the classification technique, the forecast system yields useful probabilistic forecasts for the vast majority of the hurricane season. The daily genesis potential (DGP) and latitude predictors are found to be the most significant at nearly all forecast times. Composite results show that if the probability of development P < 0.7, TCG rarely occurs; if P > 0.9, genesis occurs about 40% of the time. A case study of Tropical Depression Keith (2000) illustrates the ability of the forecast system to detect the evolution of the large-scale environment from an unfavorable to favorable one. An additional case study of an early-season nondeveloping cluster demonstrates some of the shortcomings of the system and suggests possible ways of mitigating them.

Corresponding author address: Dr. Christopher C. Hennon, Postdoctoral Fellow, UCAR Visiting Scientist Program, NOAA/TPC/National Hurricane Center, 11691 SW 17th St., Miami, FL 33165-2149. Email: Hennon@noaa.gov

Abstract

A new dataset of tropical cloud clusters, which formed or propagated over the Atlantic basin during the 1998–2000 hurricane seasons, is used to develop a probabilistic prediction system for tropical cyclogenesis (TCG). Using data from the National Centers for Environmental Prediction (NCEP)–National Center for Atmospheric Research (NCAR) reanalysis (NNR), eight large-scale predictors are calculated at every 6-h interval of a cluster's life cycle. Discriminant analysis is then used to find a linear combination of the predictors that best separates the developing cloud clusters (those that became tropical depressions) and nondeveloping systems. Classification results are analyzed via composite and case study points of view.

Despite the linear nature of the classification technique, the forecast system yields useful probabilistic forecasts for the vast majority of the hurricane season. The daily genesis potential (DGP) and latitude predictors are found to be the most significant at nearly all forecast times. Composite results show that if the probability of development P < 0.7, TCG rarely occurs; if P > 0.9, genesis occurs about 40% of the time. A case study of Tropical Depression Keith (2000) illustrates the ability of the forecast system to detect the evolution of the large-scale environment from an unfavorable to favorable one. An additional case study of an early-season nondeveloping cluster demonstrates some of the shortcomings of the system and suggests possible ways of mitigating them.

Corresponding author address: Dr. Christopher C. Hennon, Postdoctoral Fellow, UCAR Visiting Scientist Program, NOAA/TPC/National Hurricane Center, 11691 SW 17th St., Miami, FL 33165-2149. Email: Hennon@noaa.gov

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