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A range of multi‐year observational data sets are used to characterize the hydroclimate of the Dallas Fort‐Worth area (DFW) and to investigate the impact of urban land cover on daily accumulated precipitation, RADAR composite reflectivity (cREF), and cloud top height (CTH) during the warm season. Analyses of observational data indicate rainfall rates (RR) in a 45° annulus sector 50–100 km downwind of the city are enhanced relative to an upwind area of comparable size. Enhancement of mean precipitation intensity in this annulus sector is not observed on days with spatially averaged RR > 6 mm/day. Under some flow directions, the probability of cREF >30 dBZ, occurrence of hail, and the probability of CTH >10,000 geopotential meters are also enhanced up to 200 km downwind of DFW. Two deep convection events that passed over DFW are simulated with the Weather Research and Forecasting model using a range of microphysical schemes and evaluated using RADAR observations. Model configurations that exhibit the highest fidelity in these control simulations are used in a series of perturbation experiments where the areal extent of the city is varied between zero (replacement with grassland) and eight times its current size. These perturbation experiments indicate a non‐linear response of Mesoscale Convective System properties to the urban areal extent and a very strong sensitivity to the microphysical scheme used. The impact on precipitation from the urban area, even when it is expanded to eight‐times the current extent, is much less marked for deep convection with stronger synoptic forcing.

We present a new ensemble of 36 numerical experiments aimed at comprehensively gauging the sensitivity of nested large-eddy simulations (LES) driven by large-scale dynamics. Specifically, we explore 36 multiscale configurations of the Weather Research and Forecasting (WRF) Model to simulate the boundary layer flow over the complex topography at the Perdigão field site, with five nested domains discretized at horizontal resolutions ranging from 11.25 km to 30 m. Each ensemble member has a unique combination of the following input factors: (i) large-scale initial and boundary conditions, (ii) subgrid turbulence modeling in thegray zoneof turbulence, (iii) subgrid-scale (SGS) models in LES, and (iv) topography and land-cover datasets. We probe their relative importance for LES calculations of velocity, temperature, and moisture fields. Variance decomposition analysis unravels large sensitivities to topography and land-use datasets and very weak sensitivity to the LES SGS model. Discrepancies within ensemble members can be as large as 2.5 m s⁻¹for the time-averaged near-surface wind speed on the ridge and as large as 10 m s⁻¹without time averaging. At specific time points, a large fraction of this sensitivity can be explained by the different turbulence models in the gray zone domains. We implement a horizontal momentum and moisture budget routine in WRF to further elucidate the mechanisms behind the observed sensitivity, paving the way for an increased understanding of the tangible effects of the gray zone of turbulence problem.

The Weather Research and Forecasting model with coupled Chemistry is used to study the impact of anthropogenic emission changes between 2005 and 2015 on historical extreme aerosol optical depth (AOD) events that occurred during 2003–2007 over the eastern USA. An ensemble of simulations is generated where individual and all combined emissions of SO₂, NO_x, and NH₃are perturbed relative to the 2005 levels for three subregions (Midwest, Northeast, and Southeast). These simulations are used to quantify fractional changes in the spatial and temporal characteristics of mean and peak AOD and near‐surface particulate matter (PM_2.5), as well as changes in radiative forcing. Simulated AOD exhibits a spatially averaged decrease of 39%–63% during the six extreme events in response to the combined perturbed emissions. The impact on near‐surface PM_2.5concentrations is larger, with average decreases of ∼41%–69%. Peak AOD is reduced to below 1 in the perturbed simulations from initial values of 1.73–3.02 in the control runs driven by 2005 emissions. Radiative fluxes at the ground and top‐of‐the‐atmosphere exhibit considerably smaller and less consistent fractional changes across events, although changes in radiative fluxes during these extreme events are found to be larger than previously reported changes in seasonal mean values over the period 2005 to 2015.

Improved characterization of the spatiotemporal extent, intensity, and causes of extreme aerosol optical depth events is critical to quantifying their regional climate forcing and the link to near‐surface air quality. An analysis of regional‐scale extreme aerosol events over the eastern United States is undertaken using output from the Modern‐Era Retrospective analysis for Research and Applications, version 2 (MERRA‐2) reanalysis and observations from the MODerate resolution Imaging Spectroradiometers (MODIS). Six extreme aerosol optical depth (AOD) events during 2003–2007, dominated by anthropogenic emissions and characterized by a regional scale extent, are identified and simulated using the Weather Research and Forecasting model coupled with Chemistry (WRF‐Chem) applied at 12 km resolution. Statistical analyses show output from WRF‐Chem during these events is generally negatively biased in terms of the mean AOD and PM_2.5, but WRF‐Chem exhibits skill in capturing the peak AOD. WRF‐Chem also exhibits fidelity in reproducing the spatiotemporal characteristics of the extreme AOD events in intensity, location of centroid, propagation, duration, and their spatial extension. Considerable event‐to‐event variability in model skill in simulating spatial patterns of extreme events is observed, with the highest spatial correlation with MERRA‐2 AOD noted for events centered in the Midwest. Mean fractional bias in modeled peak AOD is minimized for the most intense events and for events centered over the southeastern USA. WRF‐Chem output is also negatively biased in downwelling shortwave radiation at the ground and specific humidity consistent with a positive bias in simulated precipitation relative to MERRA‐2.