A Statistical-Climatological Tropical Cyclone Track Prediction Technique Using an EOF Representation of the Synoptic Forcing

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  • 1 Department of Meteorology, Naval Postgraduate School, Monterey, CA 93940
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Abstract

An empirical orthogonal function (EOF) analysis is performed on a series of 504 normalized D-value fields at 500 mb. Each field is defined on a 120-point grid that is centered on the geographical position of a tropical cyclone. The EOF representation (eigenvectors) of the environmental flow field requires less than 10% of the storage needed for the original grid points. Each eigenvector represents a distinctly different synoptic-scale forcing pattern for the tropical cyclone motion.

Statistical regression equations are developed to predict the zonal and meridional displacements of the tropical cyclone at 12 h intervals to 84 h. The EOF coefficients are used to represent the synoptic-scale forcing in the equations. The track forecast errors are competitive with other statistical schemes. The average displacement errors in an independent sample were ∼15% smaller than the official Joint Typhoon Warning Center forecasts. Thus, the EOF-based regression approach provides a simple and low cost technique for predicting tropical cyclone motion.

Abstract

An empirical orthogonal function (EOF) analysis is performed on a series of 504 normalized D-value fields at 500 mb. Each field is defined on a 120-point grid that is centered on the geographical position of a tropical cyclone. The EOF representation (eigenvectors) of the environmental flow field requires less than 10% of the storage needed for the original grid points. Each eigenvector represents a distinctly different synoptic-scale forcing pattern for the tropical cyclone motion.

Statistical regression equations are developed to predict the zonal and meridional displacements of the tropical cyclone at 12 h intervals to 84 h. The EOF coefficients are used to represent the synoptic-scale forcing in the equations. The track forecast errors are competitive with other statistical schemes. The average displacement errors in an independent sample were ∼15% smaller than the official Joint Typhoon Warning Center forecasts. Thus, the EOF-based regression approach provides a simple and low cost technique for predicting tropical cyclone motion.

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