How to More Accurately Model Massive Storms
Massive deep convective storms, also called mesoscale convective systems (MCSs), contribute a large proportion of warm-season rainfall over the central United States. Previous studies have shown that the multi-scale modeling framework (MMF) provides a promising tool for simulating massive convective storms in the tropics. However, it is unclear whether the MMF can simulate similar thunderstorms in mid-latitudes such as in the central United States. This study evaluates MMF by comparing the simulations with detailed available observations over the central United States. We found that commonly used MMF model configuration had difficulty reproducing the observed rainfall and identified a configuration with an increased grid-spacing in the host model to improve rainfall simulation, as a result of resolving mesoscale dynamics.
This study evaluates how well the MMF can simulate massive deep convective storms. In addition to identifying shortcomings, it offers a model configuration with an increased grid-spacing in the host model to resolve the mesoscale dynamics associated with midlatitude MCSs, which more accurately predicts those MCSs.
MCSs are a major source of precipitation in many regions of the world. Traditional global climate models (GCMs) do not have adequate model parameters to represent specific convection patterns. In contrast, the MMF simulates convection by embedding a cloud-resolving model (CRM) into each GCM grid column. This level of specificity has been shown to be a promising tool for simulating MCSs over the tropics. In this study, we evaluated the MCS-associated precipitation over the central United States predicted by a prototype MMF simulation with a 2° host-grid spacing. Using ground-based radar-observed precipitation, North American Regional Reanalysis (NARR) data, and a high-resolution Weather Research and Forecasting (WRF) simulation, we showed that the prototype MMF failed to capture the convection that initiates in three out of four major MCS events during May 2011 and under-predicted the precipitation rates for the remainder of the event. We determined these shortcomings were because the model cannot resolve important drivers for initiating convection over the Southern Great Plains region. By reducing the host-grid spacing to 0.25° in the MMF, the simulation is able to better capture the mesoscale dynamics, which drastically improves the model performance. We also show that the MMF model performs better than the traditional GCM in capturing the precipitation intensity. Our results suggest that increasing host-grid resolution plays a dominant role in improving the simulation of precipitation in the MMF, and the CRM embedded in each GCM column further helps to boost precipitation rate.