FRINK Linked Data Fragments server

Matches in SemOpenAlex for { <https://semopenalex.org/work/W4283077416> ?p ?o ?g. }

• W4283077416 endingPage "102059" @default.
• W4283077416 startingPage "102059" @default.
• W4283077416 abstract "In ocean and climate models, the simulation of upper-ocean temperature and salinity depends on mixing parameterizations for ocean surface boundary layer turbulence. Existing mixing parameterizations are based on physical principles with empirical parameters. However, they are still imperfect, leading to biases in the simulation of physical states in the upper ocean. In this study, we explore the use of the data-based machine learning technique, specifically, a deep neural network model, for the effects of vertical mixing in the ocean surface boundary layer. The model is trained using process-oriented simulations of the upper-ocean turbulence driven by realistic forcing conditions at the Ocean Station Papa that is a mid-latitude ocean climate station. The deep neural network model outperforms traditional physics-based parameterizations that relate the mixing effects to surface forcing using deterministic formulas. The deep neural network model is also used to explore two currently debated issues in the development of physics-based mixing parameterizations, including the representation of wave forcing and the history of forcing conditions." @default.
• W4283077416 created "2022-06-19" @default.
• W4283077416 creator A5007146074 @default.
• W4283077416 creator A5040935534 @default.
• W4283077416 creator A5043195595 @default.
• W4283077416 creator A5061415859 @default.
• W4283077416 creator A5068251958 @default.
• W4283077416 creator A5078651012 @default.
• W4283077416 creator A5086269392 @default.
• W4283077416 date "2022-08-01" @default.
• W4283077416 modified "2023-09-26" @default.
• W4283077416 title "Exploring the use of machine learning to parameterize vertical mixing in the ocean surface boundary layer" @default.
• W4283077416 cites W1533727705 @default.
• W4283077416 cites W1659870916 @default.
• W4283077416 cites W1750723425 @default.
• W4283077416 cites W1847296711 @default.
• W4283077416 cites W1851385321 @default.
• W4283077416 cites W1891422992 @default.
• W4283077416 cites W1897776936 @default.
• W4283077416 cites W1904978519 @default.
• W4283077416 cites W1963549306 @default.
• W4283077416 cites W1963732111 @default.
• W4283077416 cites W1976775119 @default.
• W4283077416 cites W1984459738 @default.
• W4283077416 cites W1987114395 @default.
• W4283077416 cites W1987875313 @default.
• W4283077416 cites W1988048665 @default.
• W4283077416 cites W1992304053 @default.
• W4283077416 cites W1996430294 @default.
• W4283077416 cites W1997997020 @default.
• W4283077416 cites W2010621148 @default.
• W4283077416 cites W2024837388 @default.
• W4283077416 cites W2033757634 @default.
• W4283077416 cites W2045558668 @default.
• W4283077416 cites W2056497566 @default.
• W4283077416 cites W2072853764 @default.
• W4283077416 cites W2083778668 @default.
• W4283077416 cites W2089004326 @default.
• W4283077416 cites W2093965092 @default.
• W4283077416 cites W2101918617 @default.
• W4283077416 cites W2108532992 @default.
• W4283077416 cites W2109691029 @default.
• W4283077416 cites W2110234104 @default.
• W4283077416 cites W2122486065 @default.
• W4283077416 cites W2131809220 @default.
• W4283077416 cites W2132456459 @default.
• W4283077416 cites W2149602788 @default.
• W4283077416 cites W2156522996 @default.
• W4283077416 cites W2159514770 @default.
• W4283077416 cites W2271173301 @default.
• W4283077416 cites W2594375450 @default.
• W4283077416 cites W2763547740 @default.
• W4283077416 cites W2792413338 @default.
• W4283077416 cites W2803408063 @default.
• W4283077416 cites W2804943168 @default.
• W4283077416 cites W2808400960 @default.
• W4283077416 cites W2883063559 @default.
• W4283077416 cites W2883881127 @default.
• W4283077416 cites W2884369965 @default.
• W4283077416 cites W2896915413 @default.
• W4283077416 cites W2908155528 @default.
• W4283077416 cites W2915637003 @default.
• W4283077416 cites W2954912816 @default.
• W4283077416 cites W2977733150 @default.
• W4283077416 cites W2982705197 @default.
• W4283077416 cites W3003946302 @default.
• W4283077416 cites W3012721431 @default.
• W4283077416 cites W3034877539 @default.
• W4283077416 cites W3047626512 @default.
• W4283077416 cites W3050945765 @default.
• W4283077416 cites W3086166742 @default.
• W4283077416 cites W3104276532 @default.
• W4283077416 cites W3130245216 @default.
• W4283077416 cites W3197340961 @default.
• W4283077416 cites W4206239396 @default.
• W4283077416 doi "https://doi.org/10.1016/j.ocemod.2022.102059" @default.
• W4283077416 hasPublicationYear "2022" @default.
• W4283077416 type Work @default.
• W4283077416 citedByCount "4" @default.
• W4283077416 countsByYear W42830774162022 @default.
• W4283077416 countsByYear W42830774162023 @default.
• W4283077416 crossrefType "journal-article" @default.
• W4283077416 hasAuthorship W4283077416A5007146074 @default.
• W4283077416 hasAuthorship W4283077416A5040935534 @default.
• W4283077416 hasAuthorship W4283077416A5043195595 @default.
• W4283077416 hasAuthorship W4283077416A5061415859 @default.
• W4283077416 hasAuthorship W4283077416A5068251958 @default.
• W4283077416 hasAuthorship W4283077416A5078651012 @default.
• W4283077416 hasAuthorship W4283077416A5086269392 @default.
• W4283077416 hasBestOaLocation W42830774161 @default.
• W4283077416 hasConcept C111368507 @default.
• W4283077416 hasConcept C111603439 @default.
• W4283077416 hasConcept C116400278 @default.
• W4283077416 hasConcept C121332964 @default.
• W4283077416 hasConcept C122120755 @default.
• W4283077416 hasConcept C127313418 @default.
• W4283077416 hasConcept C129513315 @default.
• W4283077416 hasConcept C132651083 @default.

• next