Development and application of an operational, relocatable, mesogamma-scale weather analysis and forecasting system

Abstract

We report on the results from an operational forecast system built to predict local circulationsforced by complex terrain and other variations in land-surface characteristics. The cornerstoneof the prediction system is the Penn State University/National Center for Atmospheric Research(PSU/NCAR) mesoscale model, version 5 (MM5), a nonhydrostatic regional model. The specificapplication reported herein is for the region surrounding Dugway Proving Ground (DPG) inwest-central Utah. The nature of the terrain requires a horizontal resolution of about 1 km inorder to capture the important local features. This resolution is achieved by the use of gridnesting. To our knowledge, this resolution is finer than that being used in any other currentlyoperational forecast system tasked with local and regional weather prediction. For verificationpurposes, forecasts are stratified according to season and mean-flow characteristics. Data forverification consist of DPG surface mesonet data. Root-mean-square errors (RMSE), time meancirculations and spatial anomaly correlation statistics are computed and composited for eachhour of the day. These are compared with identical forecasts of lagged persistence, with thetime lag being 24 h (MM5 forecasts are all initialized at 1200 UTC). For wind and temperature, the RMSE from MM5 is consistently lower than that of persistence. In addition, MM5 showsskill in predicting the time-mean circulations on the test range (variations of a few m/sand °Celsius). MM5 forecast errors grow slowly with time until around sunset, after which theydecrease slightly, suggesting that local nighttime “forcing” dominates the error growth, as thesurface layer decouples from the free atmosphere. Finally, spatial anomaly correlations suggestthat the non-systematic, range-scale circulations exhibit low predictability.