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contributor authorYao Yu
contributor authorSarah T. Gille
contributor authorDavid T. Sandwell
contributor authorJulian McAuley
date accessioned2023-04-12T18:52:15Z
date available2023-04-12T18:52:15Z
date copyright2022/07/01
date issued2022
identifier otherAIES-D-21-0008.1.pdf
identifier urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4290386
description abstractSea surface slope (SSS) responds to oceanic processes and other environmental parameters. This study aims to identify the parameters that influence SSS variability. We use SSS calculated from multiyear satellite altimeter observations and focus on small resolvable scales in the 30–100-km wavelength band. First, we revisit the correlation of mesoscale ocean variability with seafloor roughness as a function of depth, as proposed by Gille et al. Our results confirm that in shallow water there is statistically significant positive correlation between rough bathymetry and surface variability, whereas the opposite is true in the deep ocean. In the next step, we assemble 27 features as input variables to fit the SSS with a linear regression model and a boosted trees regression model, and then we make predictions. Model performance metrics for the linear regression model are
publisherAmerican Meteorological Society
titleGlobal Mesoscale Ocean Variability from Multiyear Altimetry: An Analysis of the Influencing Factors
typeJournal Paper
journal volume1
journal issue3
journal titleArtificial Intelligence for the Earth Systems
identifier doi10.1175/AIES-D-21-0008.1
treeArtificial Intelligence for the Earth Systems:;2022:;volume( 001 ):;issue: 003
contenttypeFulltext


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