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Despite decades of effort, there remains an inability to measure snow water equivalent (SWE) at high spatial resolutions using remote sensing. Passive gamma ray spectrometry is one of the only well-established methods to reliably remotely sense SWE , but airborne applications to date have been limited to observing kilometre-scale areal averages. Noting the increasing capabilities of unoccupied aerial vehicles (UAVs) and miniaturization of passive gamma ray spectrometers, this study tested the ability of a UAV-borne gamma spectrometer and concomitant UAV-borne lidar to quantify the spatial variability of SWE at high spatial resolutions. Gamma and lidar observations from a UAV (UAV-gamma and UAV-lidar) were collected over two seasons from shallow, wind-blown, prairie snowpacks in Saskatchewan, Canada, with validation data collected from manual snow depth and density observations. A fine-resolution (0.25 m) reference dataset of SWE , to test UAV-gamma methods, was developed from UAV-lidar snow depth and snow survey snow density observations. The ability of UAV-gamma to resolve the areal average and spatial variability of SWE was promising with appropriate flight characteristics. Survey flights flown at a velocity of 5 ms-1 , altitude of 15 m , and line spacing of 15 m were unable to capture the average or spatial variability of SWE within the uncertainty of the reference dataset. Slower, lower, and denser flight lines at a velocity of 4 ms-1 , altitude of 8 m , and line spacing of 8 m were able to successfully observe areal average SWE and its variability at spatial resolutions greater than 22.5 m. Using a combination of UAV-based gamma SWE and UAV-based lidar snow depth improved the spatial representation of SWE substantially and permitted estimation of SWE at a spatial resolution 0.25 m with a ± 14.3 mm error relative to the reference SWE dataset. UAV-borne gamma spectrometry to estimate SWE is a promising and novel technique that has the potential to improve the measurement of shallow prairie snowpacks, and when combined with UAV-borne lidar snow depths, can provide fine-resolution, high-accuracy estimates of prairie SWE. Research on optimal hardware, data processing, and interpolation techniques is called for to further improve this remote sensing product and explore its application in other environments. [ABSTRACT FROM AUTHOR]

Improving the calculation of land‐atmosphere fluxes of heat and water vapor in mountain terrain requires better resolution of thermally driven diurnal winds (i.e., valley, slope winds) due to differential heating by terrain and radiative fluxes. In this study, the Weather Research and Forecasting model is used to simulate flow in large‐eddy simulation (LES) mode over the complex terrain of the Fortress Mountain and Marmot Creek research basins, Kananaskis Valley, Canadian Rockies, Alberta in mid‐summer. The model was used to examine the temporal and spatial evolution of local winds and near‐surface boundary layer processes with variability in topography and elevation. Numerically resolving complex terrain wind flow effects require smaller grid cell size. However, the use of terrain‐following coordinates in most numerical weather prediction models results in large numerical errors when flow over steep terrain is simulated. These errors propagate through the domain and can result in numerical instability. To avoid this issue when simulating flow over steep terrain a local smoothing approach was used, where smoothing is applied only where slope exceeds some predetermined threshold. LES results from local smoothing were compared with a mesoscale model and LES with global smoothing. Simulations are evaluated using sounding data and meteorological stations. The differences in flow patterns and reversals in two mountain basins suggest that valley geometry and volume is relevant to the break up of inversion layers, removal of cold‐air pools, and strength of thermally driven winds. Key Points: Large‐eddy simulations in complex terrain utilizing smoothing techniques resolve thermally driven mountain flowsMesoscale flow interactions with mountainous terrain can influence valley windsTopographical differences in mountain basins affects local flows and cold‐air pooling [ABSTRACT FROM AUTHOR]