Multiscale modeling of a diurnal cycle of real‐world conditions is presented for the first time, validated using data from the CWEX‐13 field experiment. Dynamical downscaling from synoptic‐scale down to resolved three‐dimensional eddies in the atmospheric boundary layer (ABL) was performed, spanning 4 orders of magnitude in horizontal grid resolution: from 111 km down to 8.2 m (30 m) in stable (convective) conditions. Computationally efficient mesoscale‐to‐microscale transition was made possible by the generalized cell perturbation method with time‐varying parameters derived from mesoscale forcing conditions, which substantially reduced the fetch to achieve fully developed turbulence. In addition, careful design of the simulations was made to inhibit the presence of under‐resolved convection at convection‐resolving mesoscale resolution and to ensure proper turbulence representation in stably‐stratified conditions. Comparison to in situ wind‐profiling lidar and near‐surface sonic anemometer measurements demonstrated the ability to reproduce the ABL structure throughout the entire diurnal cycle with a high degree of fidelity. The multiscale simulations exhibit realistic atmospheric features such as convective rolls and global intermittency. Also, the diurnal evolution of turbulence was accurately simulated, with probability density functions of resolved turbulent velocity fluctuations nearly identical to the lidar measurements. Explicit representation of turbulence in the stably‐stratified ABL was found to provide the right balance with larger scales, resulting in the development of intra‐hour variability as observed by the wind lidar; this variability was not captured by the mesoscale model. Moreover, multiscale simulations improved mean ABL characteristics such as horizontal velocity, vertical wind shear, and turbulence.

Emissions estimates of anthropogenic methane (CH4) sources are highly uncertain and many sources related to energy production are localized yet difficult to quantify.Airborne imaging spectrometers like the next generation Airborne Visible/Infrared Imaging Spectrometer (AVIRIS-NG) are well suited for locating CH4 point sources due to their ability to map concentrations over large regions with the high spatial resolution necessary to resolve localized emissions. AVIRIS-NG was deployed during a field campaign to measure controlled CH4 releases at the Rocky Mountain Oilfield Testing Center (RMOTC) in Wyoming, U.S. for multiple flux rates and flight altitudes. Two algorithms were applied to AVIRIS-NG scenes, a matched filter detection algorithm and a hybrid retrieval approach using the Iterative Maximum a Posteriori Differential Optical Absorption Spectroscopy (IMAP-DOAS) algorithm and Singular Value Decomposition.Plumes for releases as low as 14.16 m 3 /h (0.09 kt/year) were consistently observed by AVIRIS-NG at multiple flight altitudes and images of plumes were in agreement with wind directions measured at ground stations. In some cases plumes as low as 3.40 m 3 /h (0.02 kt/year) were detected, indicating that AVIRIS-NG has the capability of detecting a wide range of fugitive CH4 source categories for natural gas fields. This controlled release experiment is the first of its kind using AVIRIS-NG and demonstrates the utility of imaging spectrometers for direct attribution of emissions to individual point source locations. This is particularly useful given the large uncertainties associated with anthropogenic CH4 emissions, including those from industry, gas transmission lines, and the oil and gas sectors.

Landscape heterogeneity shapes species distributions, interactions, and fluctuations. Historically, in dry forest ecosystems, low canopy cover and heterogeneous fuel patterns often moderated disturbances like fire. Over the last century, however, increases in canopy cover and more homogeneous patterns have contributed to altered fire regimes with higher fire severity. Fire management strategies emphasize increasing within-stand heterogeneity with aggregated fuel patterns to alter potential fire behavior. Yet, little is known about how such patterns may affect fire behavior, or how sensitive fire behavior changes from fuel patterns are to winds and canopy cover. Here, we used a physics-based fire behavior model, FIRETEC, to explore the impacts of spatially aggregated fuel patterns on the mean and variability of stand-level fire behavior, and to test sensitivity of these effects to wind and canopy cover. Qualitative and quantitative approaches suggest that spatial fuel patterns can significantly affect fire behavior. Based on our results we propose three hypotheses: (1) aggregated spatial fuel patterns primarily affect fire behavior by increasing variability; (2) this variability should increase with spatial scale of aggregation; and (3) fire behavior sensitivity to spatial pattern effects should be more pronounced under moderate wind and fuel conditions.

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This paper introduces a new large-eddy simulation model, FastEddy ® , purpose built for leveraging the accelerated and more power-efficient computing capacity of graphics processing units (GPUs) toward adopting microscale turbulence-resolving atmospheric boundary layer simulations into future numerical weather prediction activities. Here a basis for future endeavors with the FastEddy ® model is provided by describing the model dry dynamics formulation and investigating several validation scenarios that establish a baseline of model predictive skill for canonical neutral, convective, and stable boundary layer regimes, along with boundary layer flow over heterogeneous terrain. The current FastEddy ® GPU performance and efficiency gains versus similarly formulated, state-of-the-art CPU-based models is determined through scaling tests as 1 GPU to 256 CPU cores. At this ratio of GPUs to CPU cores, FastEddy ® achieves 6 times faster prediction rate than commensurate CPU models under equivalent power consumption. Alternatively, FastEddy ® uses 8 times less power at this ratio under equivalent CPU/GPU prediction rate. The accelerated performance and efficiency gains of the FastEddy ® model permit more broad application of large-eddy simulation to emerging atmospheric boundary layer research topics through substantial reduction of computational resource requirements and increase in model prediction rate. Plain Language Summary This paper introduces a new model for atmospheric flows, FastEddy ® , engineered to permit faster, more power-efficient, and more broad engagement in simulation of atmospheric flows at high levels of spatial and temporal detail by using graphics cards for accelerating computations. A model description and set of pertinent validation efforts are provided along with performance intercomparison versus two state-of-the-art and widely used models of a similar vein. The documentation of formulation and validity along with the accelerated performance and more power-efficient capability of FastEddy ® provides a comprehensive and robust basis for future adoption and extension as an enabling technology for high-impact atmospheric boundary layer research and applications.

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Microscale turbulence in the atmospheric boundary layer (ABL) is characterized by significant spatiotemporal variability. Consequently, a change in the turbulence forecasting paradigm needs to occur, moving beyond average turbulence estimates at mesoscale grid resolutions (several kilometers) to eddy‐resolving forecasts. To that end, the viability of dynamic downscaling to large‐eddy simulation scales is evaluated. We present for the first time, multiday dynamic downscaling from currently available numerical weather prediction forecasts to a high‐resolution grid spacing of 25 m. It is found that these eddy‐resolving forecasts can realistically reproduce turbulence levels and peak events in the bulk of the daytime ABL, adequately capturing turbulence variability at subminute intervals. Moreover, probability distributions of turbulence quantities are in very good agreement when compared to in situ sonic‐anemometer observations. These results demonstrate the feasibility of eddy‐resolving forecasts to derive accurate probabilistic estimates of turbulence in the ABL and provide a path toward real‐time large‐eddy simulation scale prediction.

As a first step toward achieving full physics urban weather simulation capabilities within the resident-GPU large-eddy simulation (LES) FastEddy ® model, we have implemented and verified/validated a method for explicit representation of building effects. Herein, we extend the immersed body force method (IBFM) from Chan and Leach (2007, https://doi.org/10.1175/2006JAMC1321.1) to (i) be scale independent and (ii) control building surface temperatures. Through a specific drag-like term in the momentum equations, the IBFM is able to enforce essentially zero velocities within the buildings, in turn resulting in a no-slip boundary condition at the building walls. In addition, we propose similar forcing terms in the energy and mass conservation equations that allow an accurate prescription of the building temperature. The extended IBFM is computationally efficient and has the potential to be coupled to building energy models. The IBFM exhibits excellent agreement with laboratory experiments of an array of staggered cubes at a grid spacing of Δ = 1 mm, demonstrating the applicability of the method beyond the atmospheric scale. In addition, the IBFM is validated at atmospheric scale through simulations of downtown Oklahoma City (Δ = 2 m) using data collected during the Joint Urban 2003 (JU03) field campaign. Our LES IBFM results for mean wind speed, turbulence kinetic energy, and SF 6 transport and dispersion compare well to observations and produce turbulence spectra that are in good agreement with sonic anemometer data. Statistical performance metrics for the JU03 simulations are within the range of other LES models in the literature. Plain Language Summary A significant majority of social and economic activities are logically concentrated around densely populated urban areas. Consequently, accurate modeling and prediction of urban-scale weather entails a tremendous benefit to society in many ways. Herein, we extend the immersed body force method (IBFM), which allows explicit representation of building effects in microscale numerical models, to be applicable to disparate scales and to effectively control building surface temperatures. This computationally efficient method is implemented into the GPU-accelerated large-eddy simulation (LES) FastEddy ® model, with the purpose of facilitating a path toward realistic street-scale operational weather forecasting in the near future. We validate the extended IBFM with observations at laboratory scale and urban-scale field measurements over downtown Oklahoma City during the Joint Urban 2003 field campaign. Our LES IBFM results for mean wind speed, turbulence kinetic energy, and SF 6 transport and dispersion compare well to observations, and the corresponding statistical performance metrics are within the range of other LES models in the literature employing body fitted and immersed boundary approaches.