A numerical investigation of mesoscale predictability

Abstract

As mesoscale models increase in resolution there is a greater need to understand predictability on smaller scales. The predictability of a model is related to forecast skill. It is possible that the uncertainty of one scale of motion can affect the other scales due to the nonlinearity of the atmosphere. Some suggest that topography is one factor that can lead to an increase of forecast skill and therefore predictability. This study examines the uncertainty of a mesoscale model and attempts to characterize the predictability of the wind field. The data collected is from the summer, when the synoptic forcing is relatively benign. Mesoscale Model 5 (MM5) lagged forecasts are used to create a three-member ensemble over a 12-hour forecast cycle. The differences in these forecasts are used to determine the spread of the wind field. Results show that some mesoscale features have high uncertainty and others have low uncertainty, shedding light on the potential predictability of these features with a mesoscale model. Results indicate that topography is a large source of uncertainty. This is seen in all data sets, contrary to other studies. The ability of the model to properly forecast the diurnal cycle also impacted substantially on the character and evolution of forecast spread. The persistent mesoscale features were represented reasonably well, however the detailed structure of these features had a fair amount of uncertainty.