Using Initial Condition and Model Physics Perturbations in Short-Range Ensemble Simulations of Mesoscale Convective Systems

Abstract

Two separate numerical model ensembles are created by using model configurations with different model physical process parameterization schemes and identical initial conditions, and by using different model initial conditions from a Monte Carlo approach and the identical model configuration. Simulations from these two ensembles are investigated for two 48-h periods during which large, long-lived mesoscale convective systems develop. These two periods are chosen because, in some respects, they span the range of convective forecast problems routinely handled by operational forecasters. Calculations of the root-mean-square error, equitable threat score, and ranked probability score from both ensembles indicate that the model physics ensemble is more skillful than the initial-condition ensemble when the large-scale forcing for upward motion is weak. When the large-scale forcing for upward motion is strong, the initial-condition ensemble is more skillful than the model physics ensemble. This result is consistent with the expectation that model physics play a larger role in model simulations when the large-scale signal is weak and the assumptions used within the model parameterization schemes largely determine the evolution of the simulated weather events. The variance from the two ensembles is created at significantly different rates, with the variance in the physics ensemble being produced two to six times faster during the first 12 h than the variance in the initial-condition ensemble. Therefore, within a very brief time period, the variance from the physics ensemble often greatly exceeds that produced by the initial-condition ensemble. These results suggest that varying the model physics is a potentially powerful method to use in creating an ensemble. In essence, by using different model configurations, the systematic errors of the individual ensemble members are different and, hence, this may allow one to determine a probability density function from this ensemble that is more diffuse than can be accomplished using a single model configuration.