Evaluation of Multiple Planetary Boundary Layer Parameterization Schemes in Southeast U.S. Cold Season Severe Thunderstorm Environments

Southeast U.S. cold season severe weather events can be difficult to predict because of the marginal thermodynamic instability in these regimes. This work summarizes characteristics of the southeast U.S. cold season severe weather environment and planetary boundary layer (PBL) parameterization schemes used in mesoscale modeling. Moreover, we provide a focused investigation of the performance of nine different representations of the PBL in this environment by comparing simulated thermodynamic and kinematic profiles to observationally influenced ones. We demonstrate that simultaneous representation of both nonlocal and local mixing, using version 2 of the Asymmetric Convective Model (ACM2) scheme, yields the lowest overall errors for the southeast U.S. cold season tornado regime. For storm-relative helicity, strictly nonlocal schemes provide the largest overall differences from observationally influenced datasets (underforecast). Meanwhile, for low-level lapse rate and depth of the PBL, strictly local schemes yield the most extreme differences from these observationally influenced datasets (underforecast) in a mean sense. A hybrid local–nonlocal scheme is found to mitigate these mean difference extremes. These findings are traced to a tendency for local schemes to incompletely mix the PBL while nonlocal schemes overmix the PBL, whereas the hybrid schemes represent more intermediate mixing in a regime where vertical shear enhances mixing and limited instability suppresses mixing. Example applications will be presented, including an example across South Florida.