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Satellite winds as a tool for offshore wind resource assessment: The Great Lakes Wind Atlas

机译:卫星风作为海上风资源评估的工具:五大湖风图集

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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 11 years in duration. Remotely-sensed equivalent neutral winds provide spatial information on the wind climate. NASA QuikSCAT winds are temporally consistent at a 25 km resolution. ESA Synthetic Aperture Radar winds are temporally sparse but at a resolution of 500 m. As an initial step, each data set is processed independently to create a map of 90 m 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 0.1 ms(-1) and the RMSE 03 ms(-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. (C) 2015 Elsevier Inc. All rights reserved.
机译:这项工作为大湖区提供了一个新的观测风向图集,并提出了一种将原位和卫星风向观测相结合以进行海上风资源评估的方法。有效的风能项目依赖准确的风资源估算,由于观测的时空稀疏以及冬季尤其是在淡水湖中由于冰的形成而造成的时空数据缺口的潜在可能性,因此很难在海上获取风能。对于本研究,从70个沿海站点和20个浮标进行的原位观测可提供昼夜,季节和年际风变率信息,持续时间范围为3至11年。遥感等效中性风提供有关风气候的空间信息。 NASA QuikSCAT风在时间上是一致的,分辨率为25 km。 ESA合成孔径雷达风在时间上稀疏,但分辨率为500 m。首先,每个数据集都将被独立处理以创建90 m风速的地图。使用Weibull分布的均值和均方的比率以及北美地区再分析的参考时间完整时间序列,对浮冰数据进行了冰期间隙校正。使用WAsP风模型获得每个浮标和沿海站点的广义风气候,并将其合并为大湖地区的单个风速估计。该类方法用于解决SAR数据集中的时间稀疏性,并将所有场景组合成一个风速图。由于在寒冷季节缺少基于其相对于浮标时间序列的比率的数据,因此QuikSCAT的风要经过季节性校正。所有的处理步骤都减少了单个地图相对于浮标观测到的风向的偏差。通过使用QuikSCAT缩放SAR地图的大小,可以组合遥感地图。最后,结合了原地预测的风速。与浮标时间序列相比,最终地图的平均空间偏差为0.1 ms(-1)和RMSE 03 ms(-1),与仅使用SAR相比,不确定性降低了50%,而相对于仅使用SAR,降低了40%仅使用SAR和QuikSCAT,而无需现场观察。 (C)2015 Elsevier Inc.保留所有权利。

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