ABSTRACT
High‐quality tall mast and wind lidar measurements over the North and Baltic Seas are used to validate the wind climatology produced from winds simulated by the Weather, Research and ...Forecasting (WRF) model in analysis mode. Biases in annual mean wind speed between model and observations at heights around 100 m are smaller than 3.2% at offshore sites, except for those that are affected by the wake of a wind farm or the coastline. These biases are smaller than those obtained by using winds directly from the reanalysis. We study the sensitivity of the WRF‐simulated wind climatology to various model setup parameters. The results of the year‐long sensitivity simulations show that the long‐term mean wind speed simulated by the WRF model offshore in the region studied is quite insensitive to the global reanalysis, the number of vertical levels, and the horizontal resolution of the sea surface temperature used as lower boundary conditions. Also, the strength and form (grid vs spectral) of the nudging is quite irrelevant for the mean wind speed at 100 m. Large sensitivity is found to the choice of boundary layer parametrization, and to the length of the period that is discarded as spin‐up to produce a wind climatology. It is found that the spin‐up period for the boundary layer winds is likely larger than 12 h over land and could affect the wind climatology for points offshore for quite a distance downstream from the coast.
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.
► We use 10 years of QSCAT and WRF reanalysis in the Northern European Seas. ► Examine the spatial wind variability and satellite minus reanalysis differences. ► Use SAR for a case study with directionally strong land components. ► Examine the temporal wind variability. ► Evaluate the temporal representativity of QuikSCAT compared to WRF reanalysis.
The offshore wind climatology in the Northern European seas is analysed from ten years of Envisat synthetic aperture radar (SAR) images using a total of 9256 scenes, ten years of QuikSCAT and two ...years of ASCAT gridded ocean surface vector wind products and high-quality wind observations from four meteorological masts in the North Sea. The traditional method for assessment of the wind resource for wind energy application is through analysis of wind speed and wind direction observed during one or more years at a meteorological mast equipped with well-calibrated anemometers at several levels. The cost of such measurements is very high and therefore they are only sparsely available. An alternative method is the application of satellite remote sensing. Comparison of wind resource statistics from satellite products is presented and discussed including the uncertainty on the wind resource. The diurnal wind variability is found to be negligible at some location but up to 0.5ms−1 at two sites. Synergetic use of observations from multiple satellites in different orbits provides wind observations at six times in the diurnal cycle and increases the number of observations. At Horns Rev M2, FINO1 and Greater Gabbard satellite and in situ collocated samples show differences in mean wind speed of −2%, −1% and 3%, respectively. At Egmond aan Zee the difference is 10%. It is most likely due to scatterometer data sampled further offshore than at the meteorological mast. Comparing energy density with all samples at Horns Rev M2 shows overestimation 7–19% and at FINO1 underestimation 2–5% but no clear conclusion can be drawn as the comparison data are not collocated. At eight new offshore wind farm areas in Denmark, the variability in mean energy density observed by SAR ranges from 347Wm−2 in Sejerøbugten to 514Wm−2 at Horns Rev 3. The spatial variability in the near-shore areas is much higher than at areas located further offshore.
•We combine satellite surface ocean wind vector data from three satellites.•Offshore wind climatology is calculated for wind energy resource.•Satellite winds are compared to offshore observations in the North Sea.•SAR wind climatology near-shore identify coastal gradients.
Using accurate inputs of wind speed is crucial in wind resource assessment, as predicted power is proportional to the wind speed cubed. This study outlines a methodology for combining multiple ocean ...satellite winds and winds from WRF simulations in order to acquire the accurate reconstructed offshore winds which can be used for offshore wind resource assessment. First, wind speeds retrieved from Synthetic Aperture Radar (SAR) and Scatterometer ASCAT images were validated against in situ measurements from seven coastal meteorological stations in South China Sea (SCS). The wind roses from the Navy Operational Global Atmospheric Prediction System (NOGAPS) and ASCAT agree well with these observations from the corresponding in situ measurements. The statistical results comparing in situ wind speed and SAR-based (ASCAT-based) wind speed for the whole co-located samples show a standard deviation (SD) of 2.09 m/s (1.83 m/s) and correlation coefficient of R 0.75 (0.80). When the offshore winds (i.e., winds directed from land to sea) are excluded, the comparison results for wind speeds show an improvement of SD and R, indicating that the satellite data are more credible over the open ocean. Meanwhile, the validation of satellite winds against the same co-located mast observations shows a satisfactory level of accuracy which was similar for SAR and ASCAT winds. These satellite winds are then assimilated into the Weather Research and Forecasting (WRF) Model by WRF Data Assimilation (WRFDA) system. Finally, the wind resource statistics at 100 m height based on the reconstructed winds have been achieved over the study area, which fully combines the offshore wind information from multiple satellite data and numerical model. The findings presented here may be useful in future wind resource assessment based on satellite data.
Abstract Wind turbine blade erosion poses a significant challenge to the durability and performance of wind turbines. Modeling of rain erosion damage, considering atmospheric conditions, improves our ...understanding of the progression of leading-edge erosion on wind turbine blades. In this study, we investigate the impact of varying raindrop characteristics on rain erosion damage development. We analyse 2.5 years of data from a disdrometer, which measures the size and velocity of falling rain droplets, at Risø campus. Various post-processing methods of the disdrometer data are used for estimating representative droplet diameters and fall velocities for each rain event. We compare measured droplet fall velocities with theoretical terminal velocities, revealing a necessity for revising theoretical approaches to raindrop fall velocity for erosion damage modeling. The measured rain rates and representative fall velocities are used to calculate the liquid water content in the air. We introduce a bin-wise summation method for estimating the liquid water content, circumventing the need for representative droplet assumptions. As this method provides the most accurate input for the damage model, we benchmark the other post-processing methods against it and employ it to evaluate bias estimates of associated damage predictions. The largest bias (22%) in accumulated damage is found with an arithmetic mean droplet diameter approach and the smallest bias (-2%) with the median volume estimation method for damage model input. Furthermore, we demonstrate that, for a given rainfall volume, smaller droplets contribute to larger accumulated damage compared to larger droplets.
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.
This work presents a new observational wind atlas for the Great Lakes, and proposes a methodology to combine in situ and satellite wind observations for offshore wind resource assessment. Efficient ...wind energy projects rely on accurate wind resource estimates, which are complex to obtain offshore due to the temporal and spatial sparseness of observations, and the potential for temporal data gaps introduced by the formation of ice during winter months, especially in freshwater lakes. For this study, in situ observations from 70 coastal stations and 20 buoys provide diurnal, seasonal, and interannual wind variability information, with time series that range from 3 to 11years in duration. Remotely-sensed equivalent neutral winds provide spatial information on the wind climate. NASA QuikSCAT winds are temporally consistent at a 25km resolution. ESA Synthetic Aperture Radar winds are temporally sparse but at a resolution of 500m. As an initial step, each data set is processed independently to create a map of 90m wind speeds. Buoy data are corrected for ice season gaps using ratios of the mean and mean cubed of the Weibull distribution, and reference temporally-complete time series from the North American Regional Reanalysis. Generalized wind climates are obtained for each buoy and coastal site with the wind model WAsP, and combined into a single wind speed estimate for the Great Lakes region. The method of classes is used to account for the temporal sparseness in the SAR data set and combine all scenes into one wind speed map. QuikSCAT winds undergo a seasonal correction due to lack of data during the cold season that is based on its ratio relative to buoy time series. All processing steps reduce the biases of the individual maps relative to the buoy observed wind climates. The remote sensing maps are combined by using QuikSCAT to scale the magnitude of the SAR map. Finally, the in situ predicted wind speeds are incorporated. The mean spatial bias of the final map when compared to buoy time series is 0.1ms−1 and the RMSE 0.3ms−1, which represents an uncertainty reduction of 50% relative to using only SAR, and of 40% to using only SAR and QuikSCAT without in situ observations.
•We present the first full observational wind atlas for the Great Lakes.•We describe a method to integrate SAR and QuikSCAT wind speeds.•We describe a method to integrate satellite and in situ wind observations.•We present methods for correction of wind speeds for the ice season.•Wind prediction errors are reduced by integrating different data sets.
Leading edge erosion (LEE) of wind turbine blades causes decreased aerodynamic performance leading to lower power production and revenue and increased operations and maintenance costs. LEE is caused ...primarily by materials stresses when hydrometeors (rain and hail) impact on rotating blades. The kinetic energy transferred by these impacts is a function of the precipitation intensity, droplet size distributions (DSD), hydrometeor phase and the wind turbine rotational speed which in turn depends on the wind speed at hub-height. Hence, there is a need to better understand the hydrometeor properties and the joint probability distributions of precipitation and wind speeds at prospective and operating wind farms in order to quantify the potential for LEE and the financial efficacy of LEE mitigation measures. However, there are relatively few observational datasets of hydrometeor DSD available for such locations. Here, we analyze six observational datasets from spatially dispersed locations and compare them with existing literature and assumed DSD used in laboratory experiments of material fatigue. We show that the so-called Best DSD being recommended for use in whirling arm experiments does not represent the observational data. Neither does the Marshall Palmer approximation. We also use these data to derive and compare joint probability distributions of drivers of LEE; precipitation intensity (and phase) and wind speed. We further review and summarize observational metrologies for hydrometeor DSD, provide information regarding measurement uncertainty in the parameters of critical importance to kinetic energy transfer and closure of data sets from different instruments. A series of recommendations are made about research needed to evolve towards the required fidelity for a priori estimates of LEE potential.
The effect of large offshore wind farms on the local wind climate is studied using satellite synthetic aperture radar (SAR). Wind maps are derived from a total of 30 ERS-2 SAR scenes and 11 Envisat ...ASAR scenes over two large offshore wind farms in Denmark: Horns Rev in the North Sea (80 turbines) and Nysted in the Baltic Sea (78 turbines). The wind farms are the world's largest to date. For the first time, high-resolution SAR-derived wind speed images are utilized to identify regions of reduced wind speed and high turbulence intensity (i.e. wind wakes) downstream of wind turbine arrays. After quality control, 19 SAR scenes are available for the study of wake effects. A decrease of the mean wind speed is found as the wind flows through the wind farms, leaving a velocity deficit of 8–9% on average, immediately downstream of the wind turbine arrays. From this point, wind speed recovers to within 2% of the free stream velocity over a distance of 5–20 km depending on the ambient wind speed, the atmospheric stability and the number of turbines in operation. The wake magnitude and extent found from SAR wind maps is consistent with in situ measurements and results from wake models. The standard deviation of SAR-derived wind speeds is an indicator of turbulence intensity. Added turbulence intensity downstream of a wind farm is found for 7 of the 19 cases. Turbulence intensity is not as easily identified from SAR images as changes in the mean wind speed. This may be due to a low impact of wind turbine generated turbulence on the sea surface where SAR measurements are obtained.
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