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Challenges in the selection of atmospheric circulation patterns for the wind energy sector
International Journal of Climatology ( IF 3.5 ) Pub Date : 2020-10-13 , DOI: 10.1002/joc.6881 Verónica Torralba 1 , Nube Gonzalez‐Reviriego 1 , Nicola Cortesi 1 , Andrea Manrique‐Suñén 1 , Llorenç Lledó 1 , Raül Marcos 1 , Albert Soret 1 , Francisco J. Doblas‐Reyes 1, 2
International Journal of Climatology ( IF 3.5 ) Pub Date : 2020-10-13 , DOI: 10.1002/joc.6881 Verónica Torralba 1 , Nube Gonzalez‐Reviriego 1 , Nicola Cortesi 1 , Andrea Manrique‐Suñén 1 , Llorenç Lledó 1 , Raül Marcos 1 , Albert Soret 1 , Francisco J. Doblas‐Reyes 1, 2
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Atmospheric circulation patterns that prevail for several consecutive days over a specific region can have consequences for the wind energy sector as they may lead to a reduction of the wind power generation, impacting market prices or repayments of investments. The main goal of this study is to develop a user‐oriented classification of atmospheric circulation patterns in the Euro‐Atlantic region that helps to mitigate the impact of the atmospheric variability on the wind industry at seasonal timescales. Particularly, the seasonal forecasts of these frequencies of occurrence can be also beneficial to reduce the risk of the climate variability in wind energy activities. K‐means clustering has been applied on the sea level pressure from the ERA5 reanalysis to produce a classification with three, four, five and six clusters per season. The spatial similarity between the different ERA5 classifications has revealed that four clusters are a good option for all the seasons except for summer when the atmospheric circulation can be described with only three clusters. However, the use of these classifications to reconstruct wind speed and temperature, key climate variables for the wind energy sector, has shown that four clusters per season are a good choice. The skill of five seasonal forecast systems in simulating the year‐to‐year variations in the frequency of occurrence of the atmospheric patterns is more dependent on the inherent skill of the sea level pressure than on the number of clusters employed. This result suggests that more work is needed to improve the performance of the seasonal forecast systems in the Euro‐Atlantic domain to extract skilful forecast information from the circulation classification. Finally, this analysis illustrates that from a user perspective it is essential to consider the application when selecting a classification and to take into account different forecast systems.
中文翻译：
在选择风能行业的大气环流模式方面面临的挑战
在特定区域连续几天出现的大气环流模式可能会对风能行业产生影响，因为它们可能导致风力发电量减少，影响市场价格或偿还投资。这项研究的主要目的是在欧洲大西洋地区建立一种以用户为导向的大气环流模式分类，这有助于减轻季节性变化时大气变化对风能行业的影响。特别地，这些发生频率的季节预报也可能有利于减少风能活动中气候变化的风险。ķ对ERA5再分析的海平面压力进行了均值聚类，得出了每个季节分为三个，四个，五个和六个聚类的分类。不同ERA5类别之间的空间相似性表明，对于所有季节，四个群集都是一个不错的选择，但夏季除外，因为夏季只能用三个群集来描述大气环流。但是，使用这些分类来重建风速和温度（风能领域的关键气候变量）表明，每个季节四个群集是一个不错的选择。五个季节预报系统模拟大气模式发生频率逐年变化的技巧更多地取决于海平面压力的固有技巧，而不是所采用的星团数量。这一结果表明，需要做更多的工作来改善欧洲-大西洋地区的季节预报系统的性能，以便从环流分类中提取熟练的预报信息。最后，该分析表明，从用户角度出发，在选择分类时考虑应用程序并考虑不同的预测系统至关重要。
更新日期：2020-10-13
中文翻译：
在选择风能行业的大气环流模式方面面临的挑战
在特定区域连续几天出现的大气环流模式可能会对风能行业产生影响，因为它们可能导致风力发电量减少，影响市场价格或偿还投资。这项研究的主要目的是在欧洲大西洋地区建立一种以用户为导向的大气环流模式分类，这有助于减轻季节性变化时大气变化对风能行业的影响。特别地，这些发生频率的季节预报也可能有利于减少风能活动中气候变化的风险。ķ对ERA5再分析的海平面压力进行了均值聚类，得出了每个季节分为三个，四个，五个和六个聚类的分类。不同ERA5类别之间的空间相似性表明，对于所有季节，四个群集都是一个不错的选择，但夏季除外，因为夏季只能用三个群集来描述大气环流。但是，使用这些分类来重建风速和温度（风能领域的关键气候变量）表明，每个季节四个群集是一个不错的选择。五个季节预报系统模拟大气模式发生频率逐年变化的技巧更多地取决于海平面压力的固有技巧，而不是所采用的星团数量。这一结果表明，需要做更多的工作来改善欧洲-大西洋地区的季节预报系统的性能，以便从环流分类中提取熟练的预报信息。最后，该分析表明，从用户角度出发，在选择分类时考虑应用程序并考虑不同的预测系统至关重要。
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