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Adversarial super-resolution of climatological wind and solar data.
Proceedings of the National Academy of Sciences of the United States of America ( IF 11.1 ) Pub Date : 2020-07-21 , DOI: 10.1073/pnas.1918964117
Karen Stengel 1 , Andrew Glaws 1 , Dylan Hettinger 2 , Ryan N King 3
Affiliation  

Accurate and high-resolution data reflecting different climate scenarios are vital for policy makers when deciding on the development of future energy resources, electrical infrastructure, transportation networks, agriculture, and many other societally important systems. However, state-of-the-art long-term global climate simulations are unable to resolve the spatiotemporal characteristics necessary for resource assessment or operational planning. We introduce an adversarial deep learning approach to super resolve wind velocity and solar irradiance outputs from global climate models to scales sufficient for renewable energy resource assessment. Using adversarial training to improve the physical and perceptual performance of our networks, we demonstrate up to a 50× resolution enhancement of wind and solar data. In validation studies, the inferred fields are robust to input noise, possess the correct small-scale properties of atmospheric turbulent flow and solar irradiance, and retain consistency at large scales with coarse data. An additional advantage of our fully convolutional architecture is that it allows for training on small domains and evaluation on arbitrarily-sized inputs, including global scale. We conclude with a super-resolution study of renewable energy resources based on climate scenario data from the Intergovernmental Panel on Climate Change’s Fifth Assessment Report.



中文翻译:

气候风和太阳数据的对抗性超分辨率。

决策者在决定未来能源,电力基础设施,交通网络,农业以及许多其他社会重要系统的发展时,对于决策者而言至关重要的是,反映不同气候情景的准确和高分辨率数据至关重要。但是,最新的长期全球气候模拟无法解决资源评估或运营规划所需的时空特征。我们引入对抗性深度学习方法,以超级解析全球气候模型中的风速和太阳辐照度输出,以达到足以评估可再生能源资源的规模。通过对抗性训练来改善我们网络的物理和感知性能,我们展示了50×提高风能和太阳能数据的分辨率。在验证研究中,推断的场对输入噪声具有鲁棒性,具有大气湍流和太阳辐照度的正确小尺度特性,并在大尺度上与粗略数据保持一致。我们完全卷积的体系结构的另一个优点是,它允许在小范围内进行培训,并可以对任意大小的输入(包括全球规模)进行评估。我们以政府间气候变化专门委员会的《第五次评估报告》中的气候情景数据为基础,对可再生能源进行超分辨率研究。

更新日期:2020-07-22
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