Initial Results of a Mesoscale Short-Range Ensemble Forecasting System over the Pacific Northwest

Abstract

Motivated by the promising results of global-scale ensemble forecasting, a number of groups have attempted mesoscale, short-range ensemble forecasting (SREF), focusing mainly over the eastern half of the United States. To evaluate the performance of mesoscale SREF over the Pacific Northwest and to test the value of using different initial analyses as a means of ensemble forecast generation, a five-member mesoscale SREF system was constructed in which the Pennsylvania State University–National Center for Atmospheric Research fifth-generation Mesoscale Model (MM5) was run with initializations and forecast boundary conditions from major operational centers. The ensemble system was evaluated over the Pacific Northwest from January to June 2000. The model verification presented in this study considers only near-surface weather variables, especially the observed 10-m wind direction. The ensemble mean forecast displays lower mean absolute wind direction errors than the component ensemble members when averaged over all cases. The frequency with which the ensemble mean forecast verifies best is no better than the frequency of any individual member forecast. The wind direction forecast errors for the 12-km ensemble mean forecasts are comparable to 4-km deterministic forecast errors. Ensemble mean forecasts are observed to retain much of the orographically forced mesoscale structure in the component forecasts while smoothing out phase differences for propagating features. The correlation between forecast spread and forecast error for wind direction is approximately 0.6 for most lead times. Spread–error correlations rise to roughly 0.8 when only cases with high or low spread are considered. Such high correlations suggest that the ensemble system possesses the ability to predict forecast skill for high- and low-spread cases. The tendency toward higher spread–error correlation when cases with medium spread are filtered out is also found for each component member of the ensemble.