Prediction of Convective Morphology in Near-Cloud-Permitting WRF Model Simulations

Abstract

Abstract The Weather Research and Forecasting (WRF) model’s ability to forecast convective morphological evolution is examined for 37 convective systems. The simulations used Thompson microphysics with 3-km horizontal grid spacing. Ten convective mode classifications were used. An objective score was developed to determine the accuracy of the simulated morphologies considering a normalized duration of each mode simulated and its agreement with observations. Rapid Update Cycle analyses were used to compare larger-scale preinitiation conditions to simulated morphology accuracy, as well as to examine how the WRF model’s skill at predicting these larger-scale conditions influenced its prediction of morphology. Two case studies selected as representative of the most common simulated morphology deficiencies were examined in detail. The model simulated cellular systems relatively well but struggled more with linear systems, particularly bow echoes and squall lines having trailing stratiform rain regions. Morphological evolution was generally better simulated in environments with enhanced deep-layer shear and cooler potential temperatures at the level of maximum θe. Weaker deep-layer shear, cooler potential temperatures at the surface, and quickly warming potential temperatures with height increased the likelihood of timing errors. The first case study showed that a warmer cold pool, much larger line-normal shear, and excessive midlevel drying were present in the model run that failed to develop a trailing stratiform region. The second case study showed that weak shear and the absence of a well-developed cold pool may have played a role in the lack of bowing.