High-resolution simulations of turbulent flow over complex terrain provide accurate wind energy resource assessments and inform turbine design requirements. Large-eddy simulations (LESs) can capture the fine-scale features of turbulent flow; however, they are difficult to configure and prohibitively computationally expensive for use outside of the research community. Hence, a compound generative adversarial network (GAN) was developed to leverage the skill of an LES and produce a high-resolution (30 m) flow field with plausible fine-scale turbulence features and characteristics when conditioned on a coarse, computationally cheap mesoscale grid. Training data included flow and temperature fields from an LES in the vicinity of Mount Hood in the Pacific Northwest with high-resolution topographical information. The trained compound GAN generates high-resolution simulations over a sizable domain (90 km 3 180 km) in seconds using a single general-purpose graphical processing unit as compared to the large high-performance computing resources needed to produce a similar LES. GAN-generated winds from the test dataset compared well with the LES in terms of energy spectra, flow statistics, and visual inspection. Finally, our models were applied in a region of complex terrain significantly different from the training region. Statistical analysis of GAN output and LES in this independent secondary testing region established that our models were able to leverage the skill of the LES in complex terrain for independent cases and regions. This result demonstrates that a deep learning approach to downscaling has the potential to greatly reduce cost and increase the availability of valuable high-resolution data products.

The dominant winter modes of large-scale atmospheric variability in the North Atlantic are the North Atlantic Oscillation (NAO), East Atlantic (EA), and Scandinavian (SCA) patterns. This study examines their multi-decadal variability over the Instrumental Period. We identify stages through which interactions between NAO and both EA and SCA phases alter westerly winds and precipitation anomalies in Europe, which would be overlooked if only NAO phases were considered. Additionally, we show periods where EA becomes the leading mode, likely driven by natural ocean-atmosphere coupling variability, which can enhance climate reconstructions and projections. This study also provides new insights into shifts in NAO action centers. The recent shift (post-1980s) is notably distinct within the Instrumental Period. Unlike previous NAO pattern variations, EA and SCA intensify simultaneously, contributing to an expansion of the Azores High, combined with the Atlantic Ridge and/or Scandinavian Blocking. This results in drier conditions in southern Europe and wetter conditions in the north. The simultaneous intensification of NAO, EA, and SCA supports hypotheses of the Azores High expanding towards Central Europe. While this study does not attribute the shift to global warming, the eastward displacement of the northern NAO center post-1980s suggests some anthropogenic forcing through atmospheric and oceanic warming. We thus propose keeping this issue open, despite recent NAO reconstructions not identifying a singular signal in the latter half of the 20th century.

Sensitivities of microscale weather modeling to atmospheric conditions and urban layout are investigated utilizing a combination of automated surface observing systems (ASOS) data, 1-km mesoscale numerical weather prediction (NWP), and 5-m nested large-eddy simulation (LES) modeled conditions. The 1-km mesoscale predictions in analysis mode satisfactorily reproduce the observed spatiotemporal evolution of the frontal boundary in terms of wind speed, wind direction, and temperature. The 5-m nested LES simulations follow the large-scale forcing trends while improving wind speed predictions due to explicitly resolving turbulence and building interactions. Moreover, 5-min averaged nested LES results reveal improved temporal variability particularly during the stronger wind and turbulence post-frontal conditions. The skill of the 1-km mesoscale NWP model prediction is compared to coarse-grained LES fields. Probability distributions extracted from the 5-m nested LES predictions exhibit the largest sensitivity to the contrasting meteorological conditions. In contrast, cumulative distributions of TKE additionally expose a marked dependency on the unique distribution of building heights, urban density and clustering in a given area. For the first time, an ensemble forecast methodological design at building-resolving grid spacing is explored. A larger microscale ensemble spread is found for TKE than for wind speed, decreasing with height and modulated by weather conditions.

A wind data base and a web tool have been developed to estimate the wind power over the Iberian Peninsula (IP).This tool also allows estimating the most appropriate Small Wind Turbine (SWT) for a given location.The wind database includes observational and simulated wind data.The simulated data consists on a 10 km hindcast, covering the period 1960-2007, generated with a Regional Climate Model (RCM) driven by ERA40.The observational data base has been built from hourly records encompassing the period 1999-2007 and includes more than 450 stations evenly distributed over Spain.A quality control has been applied to the data in order to assess the reliability of the observational data set.Although the web tool provides information which allows the users to estimate the reliability of the modelled data in the location of the interest, a previous general validation of the simulation has been performed.This previous validation indicates that most of the disagreements between simulation and observations are attributable to the too coarse spatial resolution of the regional model.

Dust storms negatively impact human health, and also lead to vehicle crashes due to reduced visibility. Improving our ability to model and predict the location, timing, and intensity of dust storms would therefore benefit society. The amount of dust emissions is controlled by soil composition, vegetation fraction, and other faster-varying factors like wind speed and soil moisture content. Greater soil moisture content increases the cohesive forces of soil particles, which in turn makes it more difficult for a given wind speed to loft dust particles in sufficient quantities to create a dust storm. Thus, improving the soil moisture representation in numerical weather prediction fully coupled with atmospheric chemistry models like WRF-Chem is hypothesized to be an important factor to improving predictions of dust aerosol content in the atmosphere. To test this hypothesis, we identified eight cases of dust storms over the western U.S. from 2015-2021. For each case we ran two seven-day WRF-Chem simulations: in one simulation we directly inserted soil moisture content retrievals from the Soil Moisture Active Passive (SMAP) satellite into WRF-Chem ("Insert SMAP"), while the other simulation had no soil moisture adjustments ("No SMAP"). The soil moisture representation in WRF was generally improved in many locations in the southwestern U.S. by directly inserting SMAP retrievals. Additionally, the Insert SMAP simulations consistently had higher values of aerosol optical depth (AOD) due to increased atmospheric dust loading; in cases where WRF-Chem under-predicted dust loading, the AOD simulations were improved in these regions, but in the cases where WRF-Chem already over-predicted dust loading, the additional dust in Insert SMAP produced slightly worse simulations of AOD. Overall, adjusting the soil moisture representation in WRF-Chem using SMAP data had a relatively small impact on AOD during these dust storms, which points to other lingering, larger sources of model error, such as the erodibility input dataset or dust emission parameterization in WRF-Chem, that improved soil moisture alone cannot resolve.

An electrostatic, implicit particle-in-cell (PIC) model for collisionless, fully magnetized, paraxial plasma expansions in a magnetic nozzle is introduced with exact charge, energy, and magnetic moment conservation properties. The approach is adaptive in configuration space by the use of mapped meshes, and exploits the strict conservation of the magnetic moment to reduce the dimensionality of velocity space. A new particle integrator is implemented, which allows for particle substepping without the need to stop particle motion at every cell for charge conservation. Particle suborbits are determined from accuracy considerations, and are allowed to span multiple cells. Novel particle injection and expansion-to-infinity boundary conditions are developed, including a control loop to prevent the formation of spurious sheaths at the edges of the domain. The algorithm is verified in a periodic magnetic mirror configuration, a uniform plasma test case (to test particle injection), and a propulsive magnetic nozzle. The algorithm's computational complexity is shown to scale favorably with timestep, and linearly with the number of particles and mesh cells (unlike earlier implicit PIC implementations, which scaled quadratically with the number of mesh cells in one dimension). Numerical experiments demonstrate that the proposed algorithm outperforms both explicit PIC and semi-Lagrangian Vlasov codes by more than an order of magnitude.

Predicting the evolution of burned area, smoke emissions, and energy release from wildfires is crucial to air quality forecasting and emergency response planning yet has long posed a significant scientific challenge. Here we compare predictions of burned area and fire radiative power from the coupled weather/fire‐spread model WRF‐Fire (Weather and Research Forecasting Tool with fire code), against simpler methods typically used in air quality forecasts. We choose the 2019 Williams Flats Fire as our test case due to a wealth of observations and ignite the fire on different days and under different configurations. Using a novel re‐gridding scheme, we compare WRF‐Fire's heat output to geostationary satellite data at 1‐hr temporal resolution. We also evaluate WRF‐Fire's time‐resolved burned area against high‐resolution imaging from the National Infrared Operations aircraft data. Results indicate that for this study, accounting for containment efforts in WRF‐Fire simulations makes the biggest difference in achieving accurate results for daily burned area predictions. When incorporating novel containment line inputs, fuel density increases, and fuel moisture observations into the model, the error in average daily burned area is 30% lower than persistence forecasting over a 5‐day forecast. Prescribed diurnal cycles and those resolved by WRF‐Fire simulations show a phase offset of at least an hour ahead of observations, likely indicating the need for dynamic fuel moisture schemes. This work shows that with proper configuration and input data, coupled weather/fire‐spread modeling has the potential to improve smoke emission forecasts.

Experiments and simulations are used to analyze a compact helicon plasma thruster with a cusp in its internal magnetic field. The former rely on a compensated Langmuir probe and a Faraday cup, while the latter employ a hybrid PIC/fluid transport model combined with a frequency-domain electromagnetic field model. Measurements serve to tune the anomalous transport parameters of the model and overall show the same trends as the numerical results, including a secondary peak of electron temperature downstream in the magnetic nozzle, where electron cyclotron resonance conditions for the 13.56 MHz excitation frequency are met. The cusp plays a central role in determining the plasma losses to the walls and the profile of electron temperature, which in turn defines the excitation and ionization losses. While losses to the rear wall are reduced, losses to the lateral wall are increased, which, together with the low production efficiency, limit the performance of the device.

Monitoring the fuel moisture content (FMC) of 10 h dead vegetation is crucial for managing and mitigating the impact of wildland fires. The combination of in situ FMC observations, numerical weather prediction (NWP) models, and satellite retrievals has facilitated the development of machine learning (ML) models to estimate 10 h dead FMC retrievals over the contiguous US (CONUS). In this study, ML models were trained using variables from the National Water Model, the High-Resolution Rapid Refresh (HRRR) NWP model, and static surface properties, along with surface reflectances and land surface temperature (LST) retrievals from the Visible Infrared Imaging Radiometer Suite (VIIRS) instrument on the Suomi-NPP satellite system. Extensive hyper-parameter optimization resulted in skillful FMC models compared to a daily climatography RMSE (+44%) and an hourly climatography RMSE (+24%). Notably, VIIRS retrievals played a significant role as predictors for estimating 10 h dead FMC, demonstrating their importance as a group due to their high band correlation. Conversely, individual predictors within the HRRR group exhibited relatively high importance according to explainability techniques. Removing both HRRR and VIIRS retrievals as model inputs led to a significant decline in performance, particularly with worse RMSE values when excluding VIIRS retrievals. The importance of the VIIRS predictor group reinforces the dynamic relationship between 10 h dead fuel, the atmosphere, and soil moisture. These findings underscore the significance of selecting appropriate data sources when utilizing ML models for FMC prediction. VIIRS retrievals, in combination with selected HRRR variables, emerge as critical components in achieving skillful FMC estimates.

The WRF-Solar Ensemble Prediction System (WRF-Solar EPS) and a calibration method, the analog ensemble (AnEn), are used to generate calibrated gridded ensemble forecasts of solar irradiance over the contiguous United States (CONUS). Global horizontal irradiance (GHI) and direct normal irradiance (DNI) retrievals, based on geostationary satellites from the National Solar Radiation Database (NSRDB) are used for both calibrating and verifying the day-ahead GHI and DNI predictions (GDIP). A 10-member ensemble of WRF-Solar EPS is run in a re-forecast mode to generate day-ahead GDIP for three years. The AnEn is used to calibrate GDIP at each grid point independently using the NSRDB as the "ground truth". Performance evaluations of deterministic and probabilistic attributes are carried out over the whole CONUS. The results demonstrate that using the AnEn calibrated ensemble forecast from WRF-Solar EPS contributes to improving the overall quality of the GHI predictions with respect to an AnEn calibrated system based only on the deterministic run of WRF-Solar. In fact, the calibrated WRF-Solar EPS's mean exhibits a lower bias and RMSE than the calibrated deterministic WRF-Solar. Moreover, using the ensemble mean and spread as predictors for the AnEn allows a more effective calibration than using variables only from the deterministic runs. Finally, it has been shown that the recently introduced algorithm of correction for rare events is of paramount importance to obtain the lowest values of GHI from the calibrated ensemble (WRF-Solar EPS AnEn), qualitatively consistent with those observed from the NSRDB.

Aerosol optical depth (AOD) is a primary source of solar irradiance forecast error in clear-sky conditions. Improving the accuracy of AOD in NWP models like WRF will thus reduce error in both direct normal irradiance (DNI) and global horizontal irradiance (GHI), which should improve solar power forecast errors, at least in cloud-free conditions. In this study clear-sky GHI and DNI was analyzed from four configurations of the WRF-Solar model with different aerosol representations: 1) the default Tegen climatology, 2) imposing AOD forecasts from the GEOS-5 model, 3) imposing AOD forecasts from the Copernicus Atmosphere Monitoring Service (CAMS) model, and 4) the Thompson-Eidhammer aerosol-aware water/ice-friendly aerosol climatology. More than 8 months of these 15-min output forecasts are compared with high-quality irradiance observations at NOAA SURFRAD and Solar Radiation (SOLRAD) stations located across CONUS. In general, WRF-Solar with GEOS-5 AOD had the lowest errors in clear-sky DNI, while WRF-Solar with CAMS AOD had the highest errors, higher even than the two aerosol climatologies, which is consistent with validation of the four AOD550 datasets against AERONET stations. For clear-sky GHI, the statistics differed little between the four models, as expected because of the lesser sensitivity of GHI to aerosol loading. Hourly average clear-sky DNI and GHI were also analyzed, and they were additionally compared with CAMS model output directly. CAMS irradiance performed competitively with the best WRF-Solar configuration (with GEOS-5 AOD). The markedly different performance of CAMS versus WRF-Solar with CAMS AOD indicates that CAMS is apparently less sensitive to AOD550 than WRF-Solar is.

Smoke from wildfires or burning biomass directly affects air quality and weather through modulating cloud microphysics and radiation. A simple wildfire emission coupling of black carbon (BC) and organic carbon (OC) with microphysics was implemented using the Weather Research and Forecasting model's fire module. A set of large‐eddy simulations inspired by unique surface and upper atmospheric observations from the 2021 Santa Coloma de Queralt Fire (Spain) were conducted to investigate the influence of background conditions and interactions between atmospheric and fire processes such as fire smoke, ambient moisture, and latent heat release on the formation and evolution of pyroconvective clouds. While the microphysical impact of BC and OC emissions on the dynamics of fire behavior is minimal on short time scales (<6 hr), their presence increased the cloud water content and decreased the rain rates in our case study. In our case study, atmospheric moisture played an important role in the formation and development of pyroconvective clouds, which in turn enhanced the surface winds (8%) and fire spread rate (25%). The influence of fuel moisture on the pyroconvective cloud formation is smaller when compared with the atmospheric moisture content. A better representation of cloud processes can improve the mesoscale forecasts, which is important for better fire behavior modeling.

WRF-Solar is a numerical weather prediction model specifically designed to meet the increasing demand for accurate solar irradiance forecasting. The model provides flexibility in the representation of the aerosol-cloud-radiation processes. This flexibility can be argued to make it more difficult to improve the model's performance because of the necessity of inspecting different configurations. To alleviate this situation, WRF-Solar has a reference configuration to use as a benchmark in sensitivity experiments. However, the scarcity of high-quality ground observations is a handicap to accurately quantify the model performance. An alternative to ground observations are satellite irradiance retrievals. Herein we analyze the adequacy of the National Solar Radiation Database (NSRDB) to validate the WRF-Solar performance using high-quality global horizontal irradiance (GHI) observations across the contiguous United States (CONUS). Based on the sufficient performance of NSRDB, we further analyze the WRF-Solar forecast errors across the CONUS, the growth of the forecasting errors as a function of the lead time, and sensitivities to the grid spacing and the representation of the radiative effects of unresolved clouds. Our results based on WRF-Solar forecasts spanning 2018 reveal a 7% median degradation of the mean absolute error (MAE) from the first to the second daytime period. Reducing the grid spacing from 9 to 3 km leads to a 4% improvement in the MAE, whereas activating the radiative effects of unresolved clouds is desirable over most of the CONUS even at 3 km of grid spacing. A systematic overestimation of the GHI is found. These results illustrate the potential of GHI retrievals to contribute to increasing theWRF-Solar performance.

The 2020 wildfire season (May through December) in the United States was exceptionally active, with the National Interagency Fire Center reporting over 10 million acres (>40 000 km(2)) burned. During the September 2020 wildfire events, large concentrations of smoke particulates were emitted into the atmosphere. As a result, smoke was responsible for similar to 10%-30% reduction in solar power production during peak hours as recorded by the California Independent System Operator (CAISO) sites. In this study, we focus on a 9 d period in September when wildfire smoke had a profound impact on solar energy production. During the smoke episodes, hour-ahead forecasts utilized by CAISO did not include the effects of smoke and therefore overestimated the expected power production by similar to 10%-50%. Here we use multiple observational networks and a numerical weather prediction (NWP) model to show that the wildfire events of 2020 had a significantly detrimental influence on solar energy production due to high aerosol loading. We find that including the contribution of biomass burning particles greatly improves the day-ahead solar energy bias forecast of both global horizontal irradiance and direct normal irradiance by nearly similar to 50%. Our results suggest that a more comprehensive treatment of aerosols, including biomass burning aerosols, in NWP models may be an important consideration for energy grid balancing, in addition to solar resource assessment, as solar power reliance increases.

We investigate the parametric sensitivity of solar irradiance to a set of parameters within the sub-grid cloud scheme (CLD3) and an upgraded aerosol-aware Thompson-Eidhammer scheme (TE14) in an upcoming enhanced version of the Weather Research and Forecasting-Solar model. We conduct ensemble simulations over the Southern Great Plains and Hanford, California, focusing on the parametric sensitivity under cloudy conditions with different aerosol loading. We adopt the Quasi-Monte Carlo sampling approach to explore the highdimensional parameter space and apply the generalized linear model to quantify the relative contribution of individual parameters to the total variance. We find that CLD3 parameters related to entrainment and the cloud condensation threshold contribute to most of the variance in the ensemble simulations under less-polluted conditions. Larger entrainment and a higher condensation threshold produce more solar irradiance via decreasing cloud fraction and cloud optical depth. As the sensitivities of cloud fraction and cloud optical properties to perturbed parameters vary with cloud cover, the relative contributions of parameters to irradiance variance also change with cloud cover. As a contrast, under the heavily-polluted wildfire conditions, the model sensitivities to the parameters within the enhanced TE14 scheme become pronounced, especially those related to water-friendly aerosol emission rate and the modal radius of black carbon. The variations of those aerosol parameters cause significant changes in simulated irradiance, especially over the clear sky, suggesting the importance of accurate representation of emission source for aerosols and their precursors.

We present a probabilistic framework tailored for solar energy applications referred to as the Weather Research and Forecasting-Solar ensemble prediction system (WRF-Solar EPS). WRF-Solar EPS has been developed by introducing stochastic perturbations into the most relevant physical variables for solar irradiance predictions. In this study, we comprehensively discuss the impact of the stochastic perturbations of WRF-Solar EPS on solar irradiance forecasting compared to a deterministic WRF-Solar prediction (WRF-Solar DET), a stochastic ensemble using the stochastic kinetic energy backscatter scheme (SKEBS), and a WRF-Solar multi-physics ensemble (WRF-Solar PHYS). The performances of the four forecasts are evaluated using irradiance retrievals from the National Solar Radiation Database (NSRDB) over the contiguous United States. We focus on the predictability of the day-ahead solar irradiance forecasts during the year of 2018. The results show that the ensemble forecasts improve the quality of the forecasts, compared to the deterministic prediction system, by accounting for the uncertainty derived by the ensemble members. However, the three ensemble systems are under-dispersive, producing unreliable and overconfident forecasts due to a lack of calibration. In particular, WRF-Solar EPS produces less optically thick clouds than the other forecasts, which explains the larger positive bias in WRF-Solar EPS (31.7 W/m2) than in the other models (22.7–23.6 W/m2). This study confirms that the WRF-Solar EPS reduced the forecast error by 7.5% in terms of the mean absolute error (MAE) compared to WRF-Solar DET, and provides in-depth comparisons of forecast abilities with the conventional scientific probabilistic approaches (i.e., SKEBS and a multi-physics ensemble). Guidelines for improving the performance of WRF-Solar EPS in the future are provided.

Generating accurate weather forecasts of planetary boundary layer (PBL) properties is challenging in many geographical regions, oftentimes due to complex topography or horizontal variability in, for example, land characteristics. While recent advances in high-performance computing platforms have led to an increase in the spatial resolution of numerical weather prediction (NWP) models, the horizontal gridcell spacing (Delta x) of many regional-scale NWP models currently fall within or are beginning to approach the gray zone (i.e., Delta Dx approximate to 100-1000 m). At these gridcell spacings, three-dimensional (3D) effects are important, as the most energetic turbulent eddies are neither fully parameterized (as in traditional meso-scale simulations) nor fully resolved [as in traditional large-eddy simulations (LES)]. In light of this modeling challenge, we have implemented a 3D PBL parameterization for high-resolution mesoscale simulations using the Weather Research and Forecasting Model. The PBL scheme, which is based on the algebraic model developed by Mellor and Yamada, accounts for the 3D effects of turbulence by calculating explicitly the momentum, heat, and moisture flux divergences in addition to the turbulent kinetic energy. In this study, we present results from idealized simulations in the gray zone that illustrate the benefit of using a fully consistent turbulence closure framework under convective conditions. While the 3D PBL scheme reproduces the evolution of convective features more appropriately than the traditional 1D PBL scheme, we highlight the need to improve the turbulent length scale formulation. SIGNIFICANCE STATEMENT: The spatial resolution of weather models continues to increase at a rapid rate in accordance with the enhancement of computing power. As a result, smaller-scale atmospheric features become more explicitly resolved. However, most numerical models still ignore the impact of horizontal weather variations on boundary layer flows, which becomes more important at these smaller spatial scales. To address this issue, we have implemented a new modeling approach, using fundamental principles, which accounts for horizontal variability. Our results show that including three-dimensional effects of turbulence is necessary to achieve realistic boundary layer characteristics. This novel technique may be useful for many applications including complex terrain flows, pollutant dispersion, and surface-atmosphere interaction studies.