A Perturbation Method for Hurricane Ensemble Predictions

Z. Zhang Department of Meteorology, The Florida State University, Tallahassee, Florida

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T. N. Krishnamurti Department of Meteorology, The Florida State University, Tallahassee, Florida

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Abstract

This study illustrates the capability of the ensemble technique to improve hurricane forecasts in the Florida State University Global Spectral Model. A perturbation method for hurricane ensemble prediction is proposed. The perturbation method consists of perturbing hurricane initial position and the large-scale environment in which the storm is embedded. The position perturbation is done by displacing the observed hurricane toward different directions by a small distance. The empirical orthogonal function (EOF) analysis is used to find fast-growing modes in the initial state. It is shown that the model forecasts, in terms of both hurricane track and other physical variables, are very sensitive to the hurricane initial position, intensity, and its large-scale environment. The results also show the EOF-based perturbations are the fast-growing modes and can be used to reduce the initial uncertainty in the analysis.

The hurricane forecast obtained from ensemble statistics lead to a large improvement in the track forecasts. For the intensity forecasts, the ensemble prediction provides several statistical methods to display the forecasts. The statistical mean from individual ensemble members provide an overview of the forecast. The spatial distribution of the probability of predicted variables make it possible to find the most likely weather pattern.

Corresponding author address: Dr. Zhan Zhang, Department of Meteorology, The Florida State University, Tallahassee, FL 32306.

Abstract

This study illustrates the capability of the ensemble technique to improve hurricane forecasts in the Florida State University Global Spectral Model. A perturbation method for hurricane ensemble prediction is proposed. The perturbation method consists of perturbing hurricane initial position and the large-scale environment in which the storm is embedded. The position perturbation is done by displacing the observed hurricane toward different directions by a small distance. The empirical orthogonal function (EOF) analysis is used to find fast-growing modes in the initial state. It is shown that the model forecasts, in terms of both hurricane track and other physical variables, are very sensitive to the hurricane initial position, intensity, and its large-scale environment. The results also show the EOF-based perturbations are the fast-growing modes and can be used to reduce the initial uncertainty in the analysis.

The hurricane forecast obtained from ensemble statistics lead to a large improvement in the track forecasts. For the intensity forecasts, the ensemble prediction provides several statistical methods to display the forecasts. The statistical mean from individual ensemble members provide an overview of the forecast. The spatial distribution of the probability of predicted variables make it possible to find the most likely weather pattern.

Corresponding author address: Dr. Zhan Zhang, Department of Meteorology, The Florida State University, Tallahassee, FL 32306.

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