A Mesoscale Model Intercomparison

Abstract

An intercomparison of four mesoscale numerical prediction models that could lead to the selection of a model for use in the theater of operations by United States Air Force (USAF) meteorological personnel is described. Mesoscale numerical prediction models have matured, and recent advances in computer hardware make this a realizable objective. Two studies were launched to determine if a mesoscale model could be used operationally in theater and to select the model that produced the best forecast under simulated operational conditions. Of prime concern was not whether the model could produce reliable forecasts in data-rich areas, but how well the models operated and thus produced forecasts in data-sparse areas. The first study did an overall review of the available mesoscale numerical weather prediction models resulting in a general ranking of the models by expected forecast ability and operational maturity. At the conclusion of this study it became apparent that a more in-depth analysis was needed to distinguish among the higher-ranking models. Thus, this study was initiated. This study compared four models for quality of forecasts in different climate regions in the world. Two are considered state-of-the-art models that could easily be made operational. These are the Pennsylvania State University?National Center for Atmospheric Research Mesoscale Model 5 (MM5) and the Colorado State University Regional Atmospheric Modeling System (RAMS). The third model was the Navy Operational Regional Prediction System Version 6 (NORAPS6), the navy's operational regional forecast model. The fourth model is the current USAF mesoscale model, the Relocatable Window Model (RWM), that was used to provide a baseline of the current USAF capability. The models were scored by comparing the forecast values with observations. The relative ranking of the models varied with parameter, but overall, the rank order was RAMS, MM5, NORAPS6, and RWM. The score disparity between the models was not large.