NWC SEMINAR SERIES

A Gaussian Mixture Model Approach to Forecast Verification

Valliappa Lakshmanan
CIMMS/U. Oklahoma & NOAA/National Severe Storms Laboratory

15 September 2009, 3:30 PM
National Weather Center, Room 1313
120 David L. Boren Blvd.
University of Oklahoma
Norman, OK
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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 threshold-based objects are subject to association errors. In this paper, we introduce a new approach that breaks down the observed and forecast fields into a mixture of Gaussians and examine the parameters of the GMM fit to identify translation, rotation and scaling errors. We discuss the advantages of this method in terms of the traditional filtering or object-based methods and interpret resulting scores on a standard verification dataset.

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