AN EVALUATION OF SEA-LEVEL CYCLONE FORECASTS PRODUCED BY NMCS NESTED-GRID MODEL AND GLOBAL SPECTRAL MODEL

Sea level cyclone errors are computed for the National Meteorological Center's Nested-Grid Model (NGM) and the Aviation Run of the Global Sp ectral Model (AVN). The study is performed for the 1987/88 and 1989/90 cool seasons. All available 24- and 48-h forecast cycles are analyzed for North America and adjacent ocean regions. Forecast errors in the central pressure, position, and 1000-500-mb thickness of the cyclone c enter are computed. Aggregate errors can be summarized as follows: NGM forecasts of central pressure are too low (forecast pressure lower th an analyzed) by 0.72 mb at 24 h and 0.66 mb at 48 h, while AVN forecas ts are too high by 2.06 mb at 24 h and 2.50 mb at 48 h. Variance stati stics for the pressure error indicate that AVN forecasts possess less variability than those of the NGM. Both mean absolute displacement err ors and mean vector displacement errors are smaller for the AVN. The N GM moves surface cyclones too slowly and places them too far poleward into the cold air; the AVN possesses a smaller. slow bias only. Both m odels contain a weak cold bias as judged from the 1000-500-mb thicknes s over the cyclone center. The aforementioned aggregate error characte ristics exhibit significant variability when the data are stratified b y geographical region. observed central pressure, and observed 12-h pr essure change, however. For most regional, central pressure, and press ure change categories, the AVN performs better than the NGM in terms o f smaller mean pressure errors, reduced pressure error variances, and shorter displacement errors. One noteworthy exception is deepening sys tems where the NGM's systematic pressure errors are generally 2-3 mb s maller than the AVN's errors. The impact that ensemble averaging of in dividual NGM and AVN cyclone forecasts has on skill is examined. An eq ually weighted average of the NGM and AVN increasingly becomes the bes t forecast (more skillful than both the AVN and NGM individually) as t he difference between the two models increases. This finding suggests that ensemble averaging offers increased skill during situations when the NGM and AVN forecasts diverge widely.