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

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.

Derecho Evolving from a Mesocyclone—A Study of 11 August 2017 Severe Weather Outbreak in Poland: Event Analysis and High-Resolution Simulation

“…Several studies can be found, where predictability of MCSs within a convectionpermitting resolution is provided. In spite of limitations associated with initial conditions and physical parameterizations, in many studies it was possible to perform a successful MCS forecast along with mesoscale features such as MCV, RIJ or bow echo (e.g., Melhauser and Zhang 2012;Weisman et al 2013;French and Parker 2014;Snively and Gallus 2014;Xu et al 2015a,b, among others). Weisman et al (2008) used the Weather Research and Forecasting (WRF) Model (Skamarock et al 2008) and a horizontal resolution of 4 km to perform 0-36-h real-time explicit convective forecasts and concluded that a significant improvement was observed FIG.…”

Simulação Numérica Regional para a Temperatura do Ar no Continente Antártico

“…Este problema pode ser devido à má representação da umidade específica e de água precipitável pelo modelo, que prejudica o desempenho da simulação da temperatura do ar (Bromwich et al, 2013) e pelo déficit no fluxo de ondas longas (Guo et al, 2003) sobre a Antártica. Outros estudos mostraram que o modelo WRF não simulou adequadamente a precipitação de chuva de nuvens estratiformes, por exemplo, Snively & Gallus Jr. (2014). Essa deficiência na cobertura de nuvens simulada pelo WRF pode impactar na correta estimativa das temperaturas.…”

Sensitivity of a Bowing Mesoscale Convective System to Horizontal Grid Spacing in a Convection-Allowing Ensemble

“…Before reaching the archives, Base Reflectivity product data are composited with the GEMPAK program nex2img, after which false echoes are removed after comparison with the Net Echo Top product. Total absolute SAL (taSAL) is computed as follows: (1) and varies between 0 (perfect forecast) and 6. The three components are combined this way in the absence of strong evidence to suggest otherwise, though unequal weightings are used in similar schemes (e.g., Method for Object-Based Diagnostic Evaluation; [33]).…”

“…Within the group of mesoscale convective systems (MCSs), systems that display bowing structures along the convective line are among the most poorly forecast [1]. Lawson and Gallus [2] showed that smaller (progressive) bow echoes were likely poorly forecast due to inherent low predictability more so than deficiencies in microphysical parameterizations, and that improvements in synopticand mesoscale initial and lateral-boundary conditions (ICs and LBCs, respectively) would yield only minor skill increases at best.…”