Fidelity of Warm-Season MCS Structure and Evolution in the Central US Simulated by Regional Cloud-System Resolving WRF Model

Some of the world’s most intense mesoscale convective systems (MCSs) commonly occur in the central US east of the Rocky Mountains. MCSs contribute over 60% of the total warm-season precipitation in this region. To date, most of climate models do not yet have an adequate way of representing MCSs. Such deficiencies manifest as large errors in the diurnal cycle of precipitation and surface temperature simulated by climate models. Recent advancements in computational power have begun to allow convection permitting simulations using dynamical downscaling in regional models or regional refinement in global models. Explicit representation of convection shows promising improvement in simulating “MCS-like” features in climate models.

In this study, we examine the fidelity of the WRF model (4 km grid spacing) for warm-season MCSs using high-resolution geostationary satellite and ground-based NEXRAD radar network observations. Two sets of warm season (May-Aug) simulations with different commonly used two-moment microphysics schemes are conducted over the entire central US region. An automated cloud-tracking algorithm is applied to the model simulation and satellite observations to identify MCSs. Composite analysis of the evolution of simulated three-dimensional MCS structures and associated precipitation are compared with NEXRAD radar network observations. Overall, the model is able to capture key features of MCSs identified by radar observations, including lifetime, precipitation intensity and diurnal cycle. Sensitivities to microphysics mainly manifest in magnitude of MCS frequency, precipitation area and amount, suggesting that advances in microphysics parameterizations could be one of the limitations in the next-generation cloud-system resolving climate model simulations of organized convection and associated extreme precipitation.