Forecasting the maintenance of mesoscale convective systems

The problem of forecasting the maintenance of strong mesoscale convective systems (MCSs) is investigated through an examination of 211 observed proximity soundings from 1998-2004. Environmental variables (predictors) that are statistically different between mature and weakening MCSs are input into a logistic regression procedure to develop probabilistic guidance on MCS maintenance. We focus on warm-season quasi-linear systems that are organized on the mesoscale and have the tendency to produce severe surface winds for several hours.

Between the mature and weakening MCSs, shear vector magnitudes over very deep layers are the best discriminators among hundreds of kinematic and thermodynamic variables. This confirms that an integrated measure of shear over much of the storm depth is much better than a low-level-only shear measure for forecasting the weakening of MCSs. The lapse rates over a significant portion of the storm depth, the most unstable CAPE, and the deep-layer mean wind speed are also very good discriminators and collectively provide a high level of discrimination between the mature and dissipation soundings as revealed by linear discriminant analysis.

The probabilistic guidance developed from these predictors is evaluated using 45 independent events from the summer of 2005. The evaluation shows that the probabilities have significant utility in forecasting the transition of an MCS with a solid line of 50+ dBZ echoes that is producing severe surface winds to a more disorganized system with unsteady changes in structure and propagation, particularly for events in the central United States. This study suggests that empirical forecast tools based on environmental relationships still have the potential to provide forecasters with improved information on the qualitative characteristics of MCS structure and longevity. This is especially important since the current and near-term value added by explicit numerical forecasts of convection is still uncertain.