Model Verification Using Gaussian Mixture Models
Valliappa Lakshmanan  and Jack Kain
University of Oklahoma, National Severe Storms Laboratory

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 field into a mixture of Gaussians (the "objects") and
reconstruct the model forecast using scaled and displaced versions
of these Gaussians. 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.