Editorial Type:
Article
Article Type:
Research Article

A Gaussian Mixture Model Approach to Forecast Verification

Valliappa Lakshmanan Cooperative Institute of Mesoscale Meteorological Studies, University of Oklahoma, and National Oceanic and Atmospheric Administration/National Severe Storms Laboratory, Norman, Oklahoma

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John S. Kain National Oceanic and Atmospheric Administration/National Severe Storms Laboratory, Norman, Oklahoma

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Print Publication:
01 Jun 2010
Collections:
Spatial Forecast Verification Methods Inter-Comparison Project (ICP)
DOI:
https://doi.org/10.1175/2010WAF2222355.1
Page(s):
908–920
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Abstract

Verification methods for high-resolution forecasts have been based either on filtering or on objects created by thresholding the images. The filtering methods do not easily permit the use of deformation while identifying objects based on thresholds can be problematic. In this paper, a new approach is introduced in which the observed and forecast fields are broken down into a mixture of Gaussians, and the parameters of the Gaussian mixture model fit are examined to identify translation, rotation, and scaling errors. The advantages of this method are discussed in terms of the traditional filtering or object-based methods and the resulting scores are interpreted on a standard verification dataset.

Corresponding author address: V. Lakshmanan, 120 David L. Boren Blvd., Norman, OK 73072. Email: lakshman@ou.edu

This article included in the Spatial Forecast Verification Methods Inter-Comparison Project (ICP) special collection.

Abstract

Verification methods for high-resolution forecasts have been based either on filtering or on objects created by thresholding the images. The filtering methods do not easily permit the use of deformation while identifying objects based on thresholds can be problematic. In this paper, a new approach is introduced in which the observed and forecast fields are broken down into a mixture of Gaussians, and the parameters of the Gaussian mixture model fit are examined to identify translation, rotation, and scaling errors. The advantages of this method are discussed in terms of the traditional filtering or object-based methods and the resulting scores are interpreted on a standard verification dataset.

Corresponding author address: V. Lakshmanan, 120 David L. Boren Blvd., Norman, OK 73072. Email: lakshman@ou.edu

This article included in the Spatial Forecast Verification Methods Inter-Comparison Project (ICP) special collection.

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