On the Predictability of Mesoscale Convective Systems: Two-Dimensional Simulations

Abstract

Abstract Mesoscale convective systems (MCSs) are a dominant climatological feature of the central United States and are responsible for a substantial fraction of warm season rainfall. Yet very little is known about the predictability of MCSs. To help alleviate this situation, a series of ensemble simulations of an MCS are performed on a two-dimensional, storm-scale (Δx = 1 km) model. Ensemble member perturbations in wind speed, relative humidity, and instability are based on current 24-h forecast errors from the North American Model (NAM). The ensemble results thus provide an upper bound on the predictability of mesoscale convective systems within realistic estimates of environmental uncertainty, assuming successful convective initiation. The simulations are assessed by considering an ensemble member a success when it reproduces a convective system of at least 20 km in length (roughly the size of two convective cells) within 100 km on either side of the location of the MCS in the control run. By that standard, MCSs occur roughly 70% of the time for perturbation magnitudes consistent with 24-h forecast errors. Reducing the perturbations for all fields to one-half the 24-h error values increases the MCS success rate to over 90%. The same improvement in forecast accuracy would lead to a 30%–40% reduction in maximum surface wind speed uncertainty and a roughly 20% reduction in the uncertainty in maximum updraft strength, and initially slower growth in the uncertainty in the size of the MCS. However, the occurrence of MCSs drops below 50% as the midlayer mean relative humidity falls below 65%. The response of the model to reductions in forecast errors for instability, moisture, and wind speed is not consistent and cannot be easily generalized, but each can have a substantial impact on forecast uncertainty.