气候变化领域集成服务门户(CCPortal): Adversarial super-resolution of climatological wind and solar data

CCPortal

DOI	10.1073/pnas.1918964117
	Adversarial super-resolution of climatological wind and solar data
	Stengel K.; Glaws A.; Hettinger D.; King R.N.
发表日期	2020
ISSN	0027-8424
起始页码	16805
结束页码	16815
卷号	117 期号:29
英文摘要	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. © 2020 National Academy of Sciences. All rights reserved.
英文关键词	Adversarial training; Climate downscaling; Deep learning
语种	英语
scopus关键词	article; climate change; deep learning; noise; perception; renewable energy; turbulent flow; validation study
来源期刊	Proceedings of the National Academy of Sciences of the United States of America
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/160879
作者单位	Stengel, K., Computational Science Center, National Renewable Energy Laboratory, Golden, CO 80401, United States; Glaws, A., Computational Science Center, National Renewable Energy Laboratory, Golden, CO 80401, United States; Hettinger, D., Strategic Energy Analysis Center, National Renewable Energy Laboratory, Golden, CO 80401, United States; King, R.N., Computational Science Center, National Renewable Energy Laboratory, Golden, CO 80401, United States
推荐引用方式 GB/T 7714	Stengel K.,Glaws A.,Hettinger D.,et al. Adversarial super-resolution of climatological wind and solar data[J],2020,117(29).
APA	Stengel K.,Glaws A.,Hettinger D.,&King R.N..(2020).Adversarial super-resolution of climatological wind and solar data.Proceedings of the National Academy of Sciences of the United States of America,117(29).
MLA	Stengel K.,et al."Adversarial super-resolution of climatological wind and solar data".Proceedings of the National Academy of Sciences of the United States of America 117.29(2020).