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Short‐term forecasting of wind gusts, particularly those of higher intensity, is of great societal importance but is challenging due to the presence of multiple gust generation mechanisms. Wind gust observations from eight high‐passenger‐volume airports across the continental United States (CONUS) are summarized and used to develop predictive models of wind gust occurrence and magnitude. These short‐term (same hour) forecast models are built using multiple logistic and linear regression, as well as artificial neural networks (ANNs) of varying complexity. A suite of 19 upper‐air predictors drawn from the ERA5 reanalysis and an autoregressive (AR) term are used. Stepwise procedures instruct predictor selection, and resampling is used to quantify model stability. All models are developed separately for the warm (April–September) and cold (October–March) seasons. Results show that ANNs of 3–5 hidden layers (HLs) generally exhibit higher hit rates than logistic regression models and also improve skill with respect to wind gust magnitudes. However, deeper networks with more HLs increase false alarm rates in occurrence models and mean absolute error in magnitude models due to model overfitting. For model skill, inclusion of the AR term is critical while the majority of the remaining skill derives from wind speeds and lapse rates. A predictive ceiling is also clearly demonstrated, particularly for the strong and damaging gust magnitudes, which appears to be partially due to ERA5 predictor characteristics and the presence of mixed wind climates.

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.

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.

Despite the widespread application of statistical downscaling tools, uncertainty remains regarding the role of model formulation in determining model skill for daily maximum and minimum temperature (T_maxandT_min), and precipitation occurrence and intensity. Impacts of several key aspects of statistical transfer function form on model skill are evaluated using a framework resistant to model overspecification. We focus on: (a) model structure: simple (generalized linear models, GLMs) versus complex (artificial neural networks, ANNs) models. (b) Predictor selection: Fixed number of predictors chosena prioriversus stepwise selection of predictors and inclusion of grid point values versus predictors derived from application of principal components analysis (PCA) to spatial fields. We also examine the influence of domain size on model performance. For precipitation downscaling, we consider the role of the threshold used to characterize a wet day and apply three approaches (Poisson and Gamma distributions in GLM and ANN) to downscale wet‐day precipitation amounts. While no downscaling formulation is optimal for all predictands and at 10 locations representing diverse U.S. climates, and due to the exclusion of variance inflation all of the downscaling formulations fail to reproduce the range of observed variability, models with larger suites of prospective predictors generally have higher skill. For temperature downscaling, ANNs generally outperform GLM, with greater improvements forT_minthanT_max. Use of PCA‐derived predictors does not systematically improve model skill, but does improve skill for temperature extremes. Model skill for precipitation occurrence generally increases as the wet‐day threshold increases and models using PCA‐derived predictors tend to outperform those based on grid cell predictors. Each model for wet‐day precipitation intensity overestimates annual total precipitation and underestimates the proportion derived from extreme precipitation events, but ANN‐based models and those with larger predictor suites tend to have the smallest bias.

High-resolution simulations are conducted with the Weather Research and Forecasting Model to evaluate the sensitivity of wake effects and power production from two wind farm parameterizations [the commonly used Fitch scheme and the more recently developed Explicit Wake Parameterization (EWP)] to the resolution at which the model is applied. The simulations are conducted for a 9-month period for a domain encompassing much of the U.S. Midwest. The two horizontal resolutions considered are 4 km × 4 km and 2 km × 2 km grid cells, and the two vertical discretizations employ either 41 or 57 vertical layers (with the latter having double the number in the lowest 1 km). Higher wind speeds are observed close to the wind turbine hub height when a larger number of vertical layers are employed (12 in the lowest 200 m vs 6), which contributes to higher power production from both wind farm schemes. Differences in gross capacity factors for wind turbine power production from the two wind farm parameterizations and with resolution are most strongly manifest under stable conditions (i.e., at night). The spatial extent of wind farm wakes when defined as the area affected by velocity deficits near to wind turbine hub heights in excess of 2% of the simulation without wind turbines is considerably larger in simulations with the Fitch scheme. This spatial extent is generally reduced by increasing the horizontal resolution and/or increasing the number of vertical levels. These results have important applications to projections of expected annual energy production from new wind turbine arrays constructed in the wind shadow from existing wind farms.