An Evaluation of Sea Level Cyclone Forecasts Produced by NMC's Nested-Grid Model and Global Spectral Model

Abstract

Sea level cyclone errors are computed for the National Meteorological Center's Nested-Grid Model (NGM) and the Aviation Run of the Global Spectral 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 center are computed. Aggregate errors can be summarized as follows: NGM forecasts of central pressure are too low (forecast pressure lower than analyzed) by 0.72 mb at 24 h and 0.66 mb at 48 h, while AVN forecasts are too high by 2.06 mb at 24 h and 2.50 mb at 48 h. Variance statistics for the pressure error indicate that AVN forecasts possess less variability than those of the NGM. Both mean absolute displacement errors and mean vector displacement errors are smaller for the AVN. The NGM moves surface cyclones too slowly and places them too far poleward into the cold air; the AVN possesses a smaller, slow bias only. Both models contain a weak cold bias as judged from the 1000-500-mb thickness over the cyclone center. The aforementioned aggregate error characteristics exhibit significant variability when the data are stratified by geographical region, observed central pressure, and observed 12-h pressure change, however. For most regional, central pressure, and pressure change categories, the AVN performs better than the NGM in terms of smaller mean pressure errors, reduced pressure error variances, and shorter displacement errors. One noteworthy exception is deepening systems where the NGM's systematic pressure errors are generally 2?3 mb smaller than the AVN's errors. The impact that ensemble averaging of individual NGM and AVN cyclone forecasts has on skill is examined. An equally weighted average of the NGM and AVN increasingly becomes the best forecast (more skillful than both the AVN and NGM individually) as the 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.