Traditional verification schemes tend to penalize mesoscale numerical weather prediction (NWP) systems which realistically portray high amplitude, short duration mesoscale phenomena. Small phase, timing or location errors for high amplitude features result in apparent poor performance when traditional verification schemes based on synoptic observations or grid point analyses are employed. Yet these same NWP systems provide much more realistic and often more operationally useful depictions of weather events than smoother global NWP or ensemble-mean systems. Unfortunately, taking into consideration small phase or timing errors generally requires labor-intensive case studies which are unable to address the large number of cases required for reliable mesoscale NWP or mesoscale ensemble verification. NWP centers and forecasters need automated, rapid and more realistic evaluation of mesoscale NWP and ensemble performance.

The technique of Van Galen (1970) and Hoffman, et.al., (1995) which decomposes forecast error into amplitude, phase and distortion has been automated into a very efficient mesoscale verification tool, MesoVerT, for verification of gridded forecast fields. MesoVerT has an easy to manipulate GUI which allows the user to rapidly select fields, field sequences, or groups of ensemble members for verification. It also allows the user to quickly specify features or regions of the grid for verification. The Van Galen technique has been significantly accelerated (30 to 50x) through the use of image motion processing techniques from the motion picture industry (Chan, 1993). MesoVerT is being implemented as a forecaster and developer tool to rapidly verify mesoscale ensemble predictions produced by the University of Washington Short Range Ensemble Prediction System (SREF) and is funded under grants from the Office of Naval Research.