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A preliminary evaluation of aviation-impact variables derived from numerical models
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1993
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[PDF-131.64 MB]
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Description:This report describes the Aviation Division’s Verification Program, located in the Forecast Systems Laboratory, and the results of the first evaluation of four analysis and forecast model systems. The impetus for the evaluation is to get baseline statistics of forecasts of aviation-impact variables (AIVs; e.g., clouds and visibility), which are commonly not forecast or verified in numerical weather prediction models. The results of this study are very preliminary. They are intended to serve as a baseline for future evaluations, not as judgments about the current model capabilities. Four systems were evaluated: Local Analysis and Prediction System (LAPS), Colorado State University Regional Atmospheric Modeling System (RAMS), Mesoscale Analysis and Prediction System (MAPS), and the Eta model. Analyses and forecasts were generated during a 10-day period, 1-10 April 1991, and verified at surface, upper-air, and profiler stations in the continental United States. This evaluation also included state-of-the-atmosphere variables (SAVs; e.g., temperature, winds, pressure). LAPS and RAMS were run in the Weather Forecast Office scale domain (approximately 500 nm by 500 nm); MAPS and Eta were run on a nationalscale domain. In general, it was found that all systems produced good analyses and forecasts of SAVs in all areas, except that the surface winds were too strong and wind directions were commonly 30° off. Also, all models were too dry. For AIVs, detection was good for ceiling, visibility, and cloud amount, but the ability to distinguish categories (e.g., scattered or broken clouds) was poor. This could be related to moisture problems in each system. Analyses and forecast skill of cloud top varied. For instance, Eta appears to predict too much cirrus cloud, whereas MAPS has too little, resulting in large cloud top errors. Only MAPS, Eta, and RAMS produced forecasts of precipitation amounts. Precipitation events were nearly always overforecast, and precipitation amounts were largely underforecast. The sources of the errors noted here are difficult to determine. Contributing factors could include the model itself (e.g., model topography, moisture initialization), the derivation of the AIVs, and the verification data quality. One important result of this study is the recognition of the need for the AIV algorithms to reduce model data to the actual station elevation. Areas of improvement could be better model resolution, parameterizations, and initializations, as well as the AIV algorithms and quality control of the verification data. Overall, we are encouraged by the results of this first evaluation.
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Rights Information:CC0 Public Domain
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