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dc.contributor.authorYao Yu
dc.contributor.authorSarah T. Gille
dc.contributor.authorDavid T. Sandwell
dc.contributor.authorJulian McAuley
dc.date.accessioned2023-04-12T18:52:15Z
dc.date.available2023-04-12T18:52:15Z
dc.date.copyright2022/07/01
dc.date.issued2022
dc.identifier.otherAIES-D-21-0008.1.pdf
dc.identifier.urihttp://yetl.yabesh.ir/yetl1/handle/yetl/4290386
dc.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
dc.publisherAmerican Meteorological Society
dc.titleGlobal Mesoscale Ocean Variability from Multiyear Altimetry: An Analysis of the Influencing Factors
dc.typeJournal Paper
dc.journal.volume1
dc.journal.issue3
dc.journal.titleArtificial Intelligence for the Earth Systems
dc.identifier.doi10.1175/AIES-D-21-0008.1
dc.treeArtificial Intelligence for the Earth Systems:;2022:;volume( 001 ):;issue: 003
dc.contenttypeFulltext


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