Matches in SemOpenAlex for { <https://semopenalex.org/work/W3110661901> ?p ?o ?g. }
- W3110661901 abstract "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." @default.
- W3110661901 created "2020-12-21" @default.
- W3110661901 creator A5011763455 @default.
- W3110661901 creator A5058289238 @default.
- W3110661901 creator A5068427811 @default.
- W3110661901 date "2021-02-05" @default.
- W3110661901 modified "2023-10-11" @default.
- W3110661901 title "Deep learning to infer eddy heat fluxes from sea surface height patterns of mesoscale turbulence" @default.
- W3110661901 cites W1642306980 @default.
- W3110661901 cites W1727290854 @default.
- W3110661901 cites W1798648676 @default.
- W3110661901 cites W1978241036 @default.
- W3110661901 cites W1982637878 @default.
- W3110661901 cites W1982724300 @default.
- W3110661901 cites W1988115241 @default.
- W3110661901 cites W1995341919 @default.
- W3110661901 cites W2011253372 @default.
- W3110661901 cites W2013380149 @default.
- W3110661901 cites W2014356541 @default.
- W3110661901 cites W2024769910 @default.
- W3110661901 cites W2024966118 @default.
- W3110661901 cites W2026716919 @default.
- W3110661901 cites W2027629974 @default.
- W3110661901 cites W2033767602 @default.
- W3110661901 cites W2038175117 @default.
- W3110661901 cites W2044738244 @default.
- W3110661901 cites W2048536661 @default.
- W3110661901 cites W2061350806 @default.
- W3110661901 cites W2072325951 @default.
- W3110661901 cites W2075036463 @default.
- W3110661901 cites W2084698730 @default.
- W3110661901 cites W2094892216 @default.
- W3110661901 cites W2095469430 @default.
- W3110661901 cites W2098743982 @default.
- W3110661901 cites W2099753271 @default.
- W3110661901 cites W2101186745 @default.
- W3110661901 cites W2103303959 @default.
- W3110661901 cites W2103496339 @default.
- W3110661901 cites W2107362510 @default.
- W3110661901 cites W2110136835 @default.
- W3110661901 cites W2111658827 @default.
- W3110661901 cites W2112796928 @default.
- W3110661901 cites W2117539524 @default.
- W3110661901 cites W2119390085 @default.
- W3110661901 cites W2121735894 @default.
- W3110661901 cites W2126204609 @default.
- W3110661901 cites W2144640818 @default.
- W3110661901 cites W2145336865 @default.
- W3110661901 cites W2145680191 @default.
- W3110661901 cites W2165698076 @default.
- W3110661901 cites W2173760124 @default.
- W3110661901 cites W2177118528 @default.
- W3110661901 cites W2178879045 @default.
- W3110661901 cites W2193503481 @default.
- W3110661901 cites W2194775991 @default.
- W3110661901 cites W2294798173 @default.
- W3110661901 cites W2343412684 @default.
- W3110661901 cites W2488406825 @default.
- W3110661901 cites W2520558834 @default.
- W3110661901 cites W2534240011 @default.
- W3110661901 cites W2618068449 @default.
- W3110661901 cites W2745167257 @default.
- W3110661901 cites W2791129043 @default.
- W3110661901 cites W2899148217 @default.
- W3110661901 cites W2909240409 @default.
- W3110661901 cites W2913043267 @default.
- W3110661901 cites W2951205098 @default.
- W3110661901 cites W3015681226 @default.
- W3110661901 cites W4231178963 @default.
- W3110661901 cites W4238160257 @default.
- W3110661901 cites W4240683144 @default.
- W3110661901 doi "https://doi.org/10.1038/s41467-020-20779-9" @default.
- W3110661901 hasPubMedCentralId "https://www.ncbi.nlm.nih.gov/pmc/articles/7865057" @default.
- W3110661901 hasPubMedId "https://pubmed.ncbi.nlm.nih.gov/33547299" @default.
- W3110661901 hasPublicationYear "2021" @default.
- W3110661901 type Work @default.
- W3110661901 sameAs 3110661901 @default.
- W3110661901 citedByCount "23" @default.
- W3110661901 countsByYear W31106619012021 @default.
- W3110661901 countsByYear W31106619012022 @default.
- W3110661901 countsByYear W31106619012023 @default.
- W3110661901 crossrefType "journal-article" @default.
- W3110661901 hasAuthorship W3110661901A5011763455 @default.
- W3110661901 hasAuthorship W3110661901A5058289238 @default.
- W3110661901 hasAuthorship W3110661901A5068427811 @default.
- W3110661901 hasBestOaLocation W31106619011 @default.
- W3110661901 hasConcept C121332964 @default.
- W3110661901 hasConcept C127313418 @default.
- W3110661901 hasConcept C1276947 @default.
- W3110661901 hasConcept C134097258 @default.
- W3110661901 hasConcept C142672198 @default.
- W3110661901 hasConcept C153294291 @default.
- W3110661901 hasConcept C159188206 @default.
- W3110661901 hasConcept C17534553 @default.
- W3110661901 hasConcept C179065325 @default.
- W3110661901 hasConcept C19269812 @default.
- W3110661901 hasConcept C196558001 @default.
- W3110661901 hasConcept C39432304 @default.
- W3110661901 hasConcept C40382383 @default.
- W3110661901 hasConcept C49204034 @default.