气候变化领域集成服务门户(CCPortal): Deep learning to infer eddy heat fluxes from sea surface height patterns of mesoscale turbulence

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DOI	10.1038/s41467-020-20779-9
	Deep learning to infer eddy heat fluxes from sea surface height patterns of mesoscale turbulence
	George T.M.; Manucharyan G.E.; Thompson A.F.
发表日期	2021
ISSN	2041-1723
卷号	12 期号:1
英文摘要	Mesoscale eddies have strong signatures in sea surface height (SSH) anomalies that are measured globally through satellite altimetry. However, monitoring the transport of heat associated with these eddies and its impact on the global ocean circulation remains difficult as it requires simultaneous observations of upper-ocean velocity fields and interior temperature and density properties. Here we demonstrate that for quasigeostrophic baroclinic turbulence the eddy patterns in SSH snapshots alone contain sufficient information to estimate the eddy heat fluxes. We use simulations of baroclinic turbulence for the supervised learning of a deep Convolutional Neural Network (CNN) to predict up to 64% of eddy heat flux variance. CNNs also significantly outperform other conventional data-driven techniques. Our results suggest that deep CNNs could provide an effective pathway towards an operational monitoring of eddy heat fluxes using satellite altimetry and other remote sensing products. © 2021, The Author(s).
语种	英语
scopus关键词	artificial neural network; global ocean; heat flux; machine learning; mesoscale eddy; oceanic circulation; remote sensing; satellite altimetry; sea surface height; supervised learning; turbulence; article; convolutional neural network; deep learning; heat; remote sensing; simulation
来源期刊	Nature Communications
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/251528
作者单位	Division of Geological and Planetary Sciences, California Institute of Technology, 1200 E California Blvd, Pasadena, CA 91125, United States; The Cavendish Laboratory of Physics, University of Cambridge, JJ Thompson Avenue, Cambridge, CB1 3FZ, United Kingdom; School of Oceanography, University of Washington, Seattle, WA 98195, United States
推荐引用方式 GB/T 7714	George T.M.,Manucharyan G.E.,Thompson A.F.. Deep learning to infer eddy heat fluxes from sea surface height patterns of mesoscale turbulence[J],2021,12(1).
APA	George T.M.,Manucharyan G.E.,&Thompson A.F..(2021).Deep learning to infer eddy heat fluxes from sea surface height patterns of mesoscale turbulence.Nature Communications,12(1).
MLA	George T.M.,et al."Deep learning to infer eddy heat fluxes from sea surface height patterns of mesoscale turbulence".Nature Communications 12.1(2021).