Editorial Type:
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Article Type:
Research Article

Probabilistic Multiple Linear Regression Modeling for Tropical Cyclone Intensity

Chia-Ying Lee International Research Institute of Climate and Society, Columbia University, Palisades, New York

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Michael K. Tippett Department of Applied Physics and Applied Mathematics, Columbia University, New York, New York, and Department of Meteorology, King Abdulaziz University, Jeddah, Saudi Arabia

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Suzana J. Camargo Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York

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Adam H. Sobel Department of Applied Physics and Applied Mathematics, Columbia University, New York, and Lamont-Doherty Earth Observatory, Columbia University, Palisades, New York

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Print Publication:
01 Mar 2015
DOI:
https://doi.org/10.1175/MWR-D-14-00171.1
Page(s):
933–954
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Abstract

The authors describe the development and verification of a statistical model relating tropical cyclone (TC) intensity to the local large-scale environment. A multiple linear regression framework is used to estimate the expected intensity of a tropical cyclone given the environmental and storm conditions. The uncertainty of the estimate is constructed from the empirical distribution of model errors. NCEP–NCAR reanalysis fields and historical hurricane data from 1981 to 1999 are used for model development, and data from 2000 to 2012 are used to evaluate model performance. Seven predictors are selected: initial storm intensity, the change of storm intensity over the past 12 h, the storm translation speed, the difference between initial storm intensity and its corresponding potential intensity, deep-layer (850–200 hPa) vertical shear, atmospheric stability, and 200-hPa divergence. The system developed here models storm intensity changes in response to changes in the surrounding environment with skill comparable to existing operational forecast tools. Since one application of such a model is to predict changes in TC activity in response to natural or anthropogenic climate change, the authors examine the performance of the model using data that is most readily available from global climate models, that is, monthly averages. It is found that statistical models based on monthly data (as opposed to daily) with only a few essential predictors, for example, the difference between storm intensity and potential intensity, perform nearly as well at short leads as when daily predictors are used.

Corresponding author address: Chia-Ying Lee, International Research Institute for Climate and Society, Columbia University, 61 Route 9W, 202 Monell, Palisades, NY 10964. E-mail: clee@iri.columbia.edu

Abstract

The authors describe the development and verification of a statistical model relating tropical cyclone (TC) intensity to the local large-scale environment. A multiple linear regression framework is used to estimate the expected intensity of a tropical cyclone given the environmental and storm conditions. The uncertainty of the estimate is constructed from the empirical distribution of model errors. NCEP–NCAR reanalysis fields and historical hurricane data from 1981 to 1999 are used for model development, and data from 2000 to 2012 are used to evaluate model performance. Seven predictors are selected: initial storm intensity, the change of storm intensity over the past 12 h, the storm translation speed, the difference between initial storm intensity and its corresponding potential intensity, deep-layer (850–200 hPa) vertical shear, atmospheric stability, and 200-hPa divergence. The system developed here models storm intensity changes in response to changes in the surrounding environment with skill comparable to existing operational forecast tools. Since one application of such a model is to predict changes in TC activity in response to natural or anthropogenic climate change, the authors examine the performance of the model using data that is most readily available from global climate models, that is, monthly averages. It is found that statistical models based on monthly data (as opposed to daily) with only a few essential predictors, for example, the difference between storm intensity and potential intensity, perform nearly as well at short leads as when daily predictors are used.

Corresponding author address: Chia-Ying Lee, International Research Institute for Climate and Society, Columbia University, 61 Route 9W, 202 Monell, Palisades, NY 10964. E-mail: clee@iri.columbia.edu
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