Harvested by advanced technical systems honed over decades of research and development, wind energy has become a mainstream energy resource. However, continued innovation is needed to realize the potential of wind to serve the global demand for clean energy. Here, we outline three interdependent, cross-disciplinary grand challenges underpinning this research endeavor. The first is the need for a deeper understanding of the physics of atmospheric flow in the critical zone of plant operation. The second involves science and engineering of the largest dynamic, rotating machines in the world. The third encompasses optimization and control of fleets of wind plants working synergistically within the electricity grid. Addressing these challenges could enable wind power to provide as much as half of our global electricity needs and perhaps beyond.

12Regional wind integration studies in the United States require detailed wind power output 13 data at many locations to perform simulations of how the power system will operate 14 under high-penetration scenarios. The wind datasets that serve as inputs into the study 15 must realistically reflect the ramping characteristics, spatial and temporal correlations, 16and capacity factors of the simulated wind plants, as well as be time synchronized with 17 available load profiles. The Wind Integration National Dataset (WIND) Toolkit described 18in this article fulfills these requirements as the largest and most complete grid integration 19dataset publicly available to date. A meteorological dataset, wind power production time 20series, and simulated forecasts created using the Weather Research and Forecasting 21Model run on a 2-kilometer grid over the continental United States at a 5-minute 22resolution is now publicly available for more than 126,000 land-based and offshore wind 23 power production sites. State-of-the-art forecast accuracy was mimicked by reforecasting 24 the years 2007-2013 using industry-standard techniques. Our meteorological and power 25validation results show that the WIND Toolkit data is satisfactory for wind energy 26integration studies. Users are encouraged to validate according to their phenomena and 27 application of interest. 28 29 Keywords 30Grid integration, WRF, wind energy, integration data set, WIND Toolkit, numerical 31 simulations 32

[1] Terrestrial and airborne laser scanning (TLS and ALS) techniques have only recently developed to the point where they allow wide-area measurements of snow distribution in varying terrain. In this paper we present multiple TLS measurements showing the snow depth development for a series of precipitation events. We observe that the pattern of maximum accumulation is similar for the two years presented here (correlation up to r ¼ 0.97). Storms arriving from the northwest show persistent snow depth distributions and contribute most to the final accumulation pattern. Snow depth patterns of maximum accumulation for the two years are more similar than the distribution created by any two pairs of individual storms. Based on the strong link between accumulation patterns and terrain, we investigated the ability of a model based on terrain and wind direction to predict accumulation patterns. This approach of , which describes wind exposure and shelter, was able to predict the general accumulation pattern over scales of slopes but failed to match observed variance. Furthermore, a high sensitivity to the local wind direction was demonstrated. We suggest that Winstral et al.'s model could form a useful tool for application from hydrology and avalanche risk assessment to glaciology.

ABSTRACT. Wind tunnel measurements of snowdrift in a turbulent, logarithmic velocity boundary layer have been made in Davos, Switzerland, using natural snow. Regression analysis gives the drift threshold friction velocity (u Ãt ), assuming an exponential drift profile and a simple drift to friction velocity relationship. Measurements over 15 snow covers show that u Ãt is influenced more by snow density and particle size than by ambient temperature and humidity, and varies from 0.27 to 0.69 m s

Abstract. Wind-profiling lidars are now regularly used in boundary-layer meteorology and in applications such as wind energy and air quality. Lidar wind profilers exploit the Doppler shift of laser light backscattered from particulates carried by the wind to measure a line-of-sight (LOS) velocity. The Doppler beam swinging (DBS) technique, used by many commercial systems, considers measurements of this LOS velocity in multiple radial directions in order to estimate horizontal and vertical winds. The method relies on the assumption of homogeneous flow across the region sampled by the beams. Using such a system in inhomogeneous flow, such as wind turbine wakes or complex terrain, will result in errors.To quantify the errors expected from such violation of the assumption of horizontal homogeneity, we simulate inhomogeneous flow in the atmospheric boundary layer, notably stably stratified flow past a wind turbine, with a mean wind speed of 6.5 m s −1 at the turbine hub-height of 80 m. This slightly stable case results in 15 • of wind direction change across the turbine rotor disk. The resulting flow field is sampled in the same fashion that a lidar samples the atmosphere with the DBS approach, including the lidar range weighting function, enabling quantification of the error in the DBS observations. The observations from the instruments located upwind have small errors, which are ameliorated with time averaging. However, the downwind observations, particularly within the first two rotor diameters downwind from the wind turbine, suffer from errors due to the heterogeneity of the wind turbine wake. Errors in the stream-wise component of the flow approach 30 % of the hub-height inflow wind speed close to the rotor disk. Errors in the cross-stream and vertical velocity components are also significant: crossstream component errors are on the order of 15 % of the hubheight inflow wind speed (1.0 m s −1 ) and errors in the vertical velocity measurement exceed the actual vertical velocity. By three rotor diameters downwind, DBS-based assessments of wake wind speed deficits based on the stream-wise velocity can be relied on even within the near wake within 1.0 m s −1 (or 15 % of the hub-height inflow wind speed), and the cross-stream velocity error is reduced to 8 % while vertical velocity estimates are compromised. Measurements of inhomogeneous flow such as wind turbine wakes are susceptible to these errors, and interpretations of field observations should account for this uncertainty.