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AbstractDownscaling simulations performed with the Weather Research and Forecasting (WRF) model were used to determine the large-scale wind energy potential of Iceland. Local wind speed distributions are represented by Weibull statistics. The shape parameter across Iceland varies between 1.2 and 3.6, with the lowest values indicative of near-exponential distributions at sheltered locations, and the highest values indicative of normal distributions at exposed locations in winter. Compared with summer, average power density in winter is increased throughout Iceland by a factor of 2.0–5.5. In any season, there are also considerable spatial differences in average wind power density. Relative to the average value within 10 km of the coast, power density across Iceland varies between 50 and 250%, excluding glaciers, or between 300 and 1500 W m−2 at 50 m above ground level in winter. At intermediate elevations of 500–1000 m above mean sea level, power density is independent of the distance to the coast. In addition to seasonal and spatial variability, differences in average wind speed and power density also exist for different wind directions. Along the coast in winter, power density of onshore winds is higher by 100–700 W m−2 than that of offshore winds. Based on these results, 14 test sites were selected for more detailed analyses using the Wind Atlas Analysis and Application Program (WAsP).

This Special Issue hosts papers on aspects of remote sensing for atmospheric conditions for wind energy applications. The wind lidar technology is presented from a theoretical view on the coherent focused Doppler lidar principles. Furthermore, wind lidar for applied use for wind turbine control, wind farm wake, and gust characterizations are presented, as well as methods to reduce uncertainty when using lidar in complex terrain. Wind lidar observations are used to validate numerical model results. Wind Doppler lidar mounted on aircraft used for observing winds in hurricane conditions and Doppler radar on the ground used for very short-term wind forecasting are presented. For the offshore environment, floating lidar data processing is presented as well as an experiment with wind-profiling lidar on a ferry for model validation. Assessments of wind resources in the coastal zone using wind-profiling lidar and global wind maps using satellite data are presented.

Abstract Satellite data are used to characterize the near-surface winds over the Northern European Shelf Seas. We compare mean winds from QuikSCAT with reanalysis fields from the Weather Research and Forecasting (WRF) model and in situ data from the FINO-1 offshore research mast. The aim is to evaluate the spatial and temporal variability of the near-surface wind field, including the inter- and intra-annual variability for resource assessment purposes. This study demonstrates the applicability of satellite observations as the means to provide information useful for selecting areas to perform higher resolution model runs or for mast installations. Comparisons between QuikSCAT and WRF reanalyses show biases ranging mostly between 0.6 and −0.6 m s−1 with a standard deviation of 1.8–2.8 m s−1. The combined analyses of inter- and intra-annual indices and the wind speed and direction distributions allow the identification of 3 sub-domains with similar intra-annual variability. Local characteristics observed from the long-term QuikSCAT wind rose distributions are depicted in high-resolution satellite Synthetic Aperture Radar (SAR) wind fields. The winds derived from the WRF reanalysis dataset miss seasonal features observed by QuikSCAT and at FINO-1.

ABSTRACTWind direction is required as input to the geophysical model function (GMF) for the retrieval of sea surface wind speed from a synthetic aperture radar (SAR) images. The present study verifies the effectiveness of using the wind direction obtained from the weather research and forecasting model (WMF) as input to the GMF to retrieve accurate wind fields in coastal waters adjacent to complex onshore terrain. The wind speeds retrieved from 42 ENVISAT ASAR images are validated based on in situ measurements at an offshore platform in Japan. Accuracies are also compared with cases using wind directions: the meso‐analysis of the Japan Meteorological Agency (MANAL), the SeaWinds microwave scatterometer on QuikSCAT and the National Center for Environmental Prediction final operational global analysis data (NCEP FNL).In comparison with the errors of the SAR‐retrieved wind speeds obtained using the WRF, MANAL, QuikSCAT and NCEP FNL wind directions, the magnitudes of the errors do not appear to be correlated with the errors of the wind directions themselves. In addition to wind direction, terrain factors are considered to be a main source of error other than wind direction. Focusing on onshore winds (blowing from the sea to land), the root mean square errors on wind speed are found to be 0.75 m s  − 1 (in situ), 0.96 m s  − 1 (WRF), 1.75 m s  − 1 (MANAL), 1.58 m s  − 1 (QuikSCAT) and 2.00 m s  − 1 (NCEP FNL), respectively, but the uncertainty is of the same order of magnitude because of the low number of cases. These results indicate that although the effectiveness of using the accurate WRF wind direction for the wind retrieval is partly confirmed, further efforts to remove the error due to factors other than wind direction are necessary for more accurate wind retrieval in coastal waters. Copyright © 2012 John Wiley & Sons, Ltd.

A wind resource estimation study based on a series of 62 satellite wind field maps is presented. The maps were retrieved from imaging synthetic aperture radar (SAR) data. The wind field maps were used as input to the software RWT, which calculates the offshore wind resource based on spatial averaging (footprint modelling) of the wind statistic in each satellite image. The calculated statistics can then be input to the program WAsP and used in lieu of in-situ observations by meteorological instruments. A regional wind climate map based on satellite SAR images delineates significant spatial wind speed variations. The site of investigation was Horns Rev in the North Sea, where a meteorological time series is used for comparison. The advantages and limitations of these new techniques, which seem particularly useful for mapping of the regional wind climate, are discussed. Copyright © 2005 John Wiley & Sons, Ltd.

Precipitation is a key driver of leading-edge erosion of wind turbine blades, which leads to a loss in annual energy production and high cost for repair of wind turbines. Precipitation type, drop size and their frequency are relevant parameters, but not easily available. Reflectivity-Rain Rate (Z‑R) relationships as well as annual sums of rainfall amount and rainfall kinetic energy potentially could be used to estimate leading-edge erosion. Although Z‑R relationships and amounts are known for several places, their spatial variation and dependence on weather types is unknown in the North Sea and Baltic Sea area. We analysed time series of multiple disdrometers located on the coast of the North Sea and Baltic Sea to characterize the variation and weather-type dependence of the Z‑R relationship, precipitation type, rainfall amount and kinetic energy.The Z‑R relationship as indication for the mean drop-size distribution showed small variations within different locations, but had a large variability for specific, but rare weather types. Only the precipitation types snow and hail showed some tendencies of weather-type dependence. Rainfall amount and rainfall kinetic energy were higher for stations in the eastern part of the North Sea compared to the western part and the Baltic Sea. Highest values were related to advection from the West. Overall, variations with location and weather type were found. These results will need to be considered in leading-edge erosion modelling and site assessment.

We study the wind climate and its long-term variability in the North Sea and South China Sea, areas relevant for offshore wind energy development, using satellite-based wind data, because very few reliable long-term in-situ sea surface wind observations are available. The Special Sensor Microwave Imager (SSM/I) ocean winds extrapolated from 10 m to 100 m using the Charnock relationship and the logarithmic profile method are compared to Weather Research and Forecasting (WRF) model results in both seas and to in-situ observations in the North Sea. The mean wind speed from SSM/I and WRF differ only by 0.1 m/s at Fino1 in the North Sea, while west of Hainan in the South China Sea the difference is 1.0 m/s. Linear regression between SSM/I and WRF winds at 100 m show correlation coefficients squared of 0.75 and 0.67, standard deviation of 1.67 m/s and 1.41 m/s, and mean difference of −0.12 m/s and 0.83 m/s for Fino1 and Hainan, respectively. The WRF-derived winds overestimate the values in the South China Sea. The inter-annual wind speed variability is estimated as 4.6% and 4.4% based on SSM/I at Fino1 and Hainan, respectively. We find significant changes in the seasonal wind pattern at Fino1 with springtime winds arriving one month earlier from 1988 to 2013 and higher winds in June; no yearly trend in wind speed is observed in the two seas.

Abstract Leading edge erosion on wind turbine blades can reduce aerodynamic efficiency and cause increased maintenance costs, potentially impacting the overall economic viability. Erosion-safe operation is the concept of reducing the blade tip speed during periods of heavy rain, thereby significantly reducing the erosion development and progression. This study explores the application of reinforcement learning, namely using a double deep Q-network, to implement erosion-safe operation. The proposed methodology involves learning a policy for tip speed control that maximizes revenue over a specific period of time. We demonstrate the concept based on 5 years of simulation of the DTU 10MW reference turbine and mesoscale weather simulation from Horns Rev. The trained model was found to increase the cumulative revenue by 1.6 % compared to not using erosion-safe operation. The model was able to effectively adapt to varying weather conditions and stochastic damage progression. Based on 10,000 random simulations, the trained model outperforms two baseline models in more than 98 % of the simulations.