气候变化领域集成服务门户(CCPortal): Application of Deep Learning to Estimate Atmospheric Gravity Wave Parameters in Reanalysis Data Sets

CCPortal

DOI	10.1029/2020GL089436
	Application of Deep Learning to Estimate Atmospheric Gravity Wave Parameters in Reanalysis Data Sets
	Matsuoka D.; Watanabe S.; Sato K.; Kawazoe S.; Yu W.; Easterbrook S.
发表日期	2020
ISSN	0094-8276
卷号	47 期号:19
英文摘要	Gravity waves play an essential role in driving and maintaining global circulation. To understand their contribution in the atmosphere, the accurate reproduction of their distribution is important. Thus, a deep learning approach for the estimation of gravity wave momentum fluxes was proposed, and its performance at 100 hPa was tested using data from low-resolution zonal and meridional winds, temperature, and specific humidity at 300, 700, and 850 hPa in the Hokkaido region (Japan). To this end, a deep convolutional neural network was trained on 29-year reanalysis data sets (JRA-55 and DSJRA-55), and the final 5-year data were reserved for evaluation. The results showed that the fine-scale momentum flux distribution of the gravity waves could be estimated at a reasonable computational cost. Particularly, in winter, when gravity waves are stronger, the median root means square errors (RMSEs) of the maximum momentum flux and the characteristic zonal wavenumber were 0.06–0.13 mPa and 1.0 × 10−5, respectively. ©2020. The Authors.
英文关键词	Atmospheric humidity; Cell proliferation; Convolutional neural networks; Deep neural networks; Gravity waves; Momentum; Atmospheric gravity waves; Computational costs; Global circulation; Gravity wave momentum; Learning approach; Meridional winds; Root-means-square errors; Specific humidity; Deep learning; artificial neural network; atmospheric circulation; data set; gravity wave; machine learning; meridional circulation; momentum transfer; parameter estimation; zonal wind; Hokkaido; Japan
语种	英语
来源期刊	Geophysical Research Letters
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/169661
作者单位	Japan Agency for Marine-Earth Science and Technology (JAMSTEC), Yokohama, Japan; Japan Science and Technology Agency (JST), Kawaguchi, Japan; Department of Computer Science, University of Toronto, Toronto, ON, Canada; Department of Earth and Planetary Science, The University of Tokyo, Tokyo, Japan; Department of Earth and Planetary Sciences, Hokkaido University, Sapporo, Japan
推荐引用方式 GB/T 7714	Matsuoka D.,Watanabe S.,Sato K.,et al. Application of Deep Learning to Estimate Atmospheric Gravity Wave Parameters in Reanalysis Data Sets[J],2020,47(19).
APA	Matsuoka D.,Watanabe S.,Sato K.,Kawazoe S.,Yu W.,&Easterbrook S..(2020).Application of Deep Learning to Estimate Atmospheric Gravity Wave Parameters in Reanalysis Data Sets.Geophysical Research Letters,47(19).
MLA	Matsuoka D.,et al."Application of Deep Learning to Estimate Atmospheric Gravity Wave Parameters in Reanalysis Data Sets".Geophysical Research Letters 47.19(2020).