The Dependence of Ensemble Dispersion on Analysis–Forecast Systems: Implications to Short-Range Ensemble Forecasting of Precipitation

The impact of differences in analysis–forecast systems on dispersion of an ensemble forecast is examined for a case of cyclogenesis. Changes in the dispersion properties between two 25-member ensemble forecasts with different cumulus parameterization schemes and different initial analyses are compared. The statistical significance of the changes is assessed. Error growth due to initial condition uncertainty depends significantly on the analysis–forecast system. Quantitative precipitation forecasts and probabilistic quantitative precipitation forecasts are extremely sensitive to the specification of physical parameterizations in the model. Regions of large variability tend to coincide with a high likelihood of parameterized convection. Analysis of other model fields suggests that those with relatively large energy in the mesoscale also exhibit highly significant differences in dispersion. The results presented here provide evidence that the combined effect of uncertainties in model physics and the initial state provides a means to increase the dispersion of ensemble prediction systems, but care must be taken in the construction of mixed ensemble systems to ensure that other properties of the ensemble distribution are not overly degraded.