The Sensitivity of Simulated Summer MCS Activity to Select WRF Parameters

Recent work has identified a dry bias in the central U.S. during the northern hemisphere summer in long-term, dynamically downscaled, climate simulations. Based on the relative importance of MCS rainfall to overall rainfall in this region and during this time of the year, this study examines the influence of a number of WRF parameter choices on MCS activity during June of 2008. A 3.2 km grid over the entire Continental United States (CONUS) is used to run multiple convection-allowing simulations over this period. An automated tracking procedure, augmented with machine learning models, is used to identify MCS and QLCS events in the WRF output data. The occurrence of these events are compared to data extracted from national composite reflectivity to determine the bias affiliated with each parameter choice. We discuss the strengths and weaknesses of each approach, and discuss recommendations and implications for simulating summertime rainfall, and specifically, rainfall associated with MCS events.