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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