Consulta: subjectFacets:"Sea surface height interpolation"
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Data/hora: 10/06/2024 13:47:28

A Deep Learning approach to spatiotemporal SSH interpolation and estimation of deep currents in geostrophic ocean turbulence
Provedor de dados: 5
Autores: Manucharyan , Georgy E.; Siegelman , Lia; Klein, Patrice.
Satellite altimeters provide global observations of sea surface height (SSH) and present a unique dataset for advancing our theoretical understanding of upper ocean dynamics and monitoring its variability. Considering that mesoscale SSH patterns can evolve on timescales comparable to or shorter than satellite return periods, it is challenging to accurately reconstruct the continuous SSH evolution as currently available altimetry observations are still spatially and temporally sparse. Here we explore the possibility of SSH interpolation via Deep Learning by using synthetic observations from an idealized quasigeostrophic (QG) model of baroclinic ocean turbulence. We demonstrate that Convolutional Neural Networks with Residual Learning are superior in SSH...
Tipo: Text
Palavras-chave: Baroclinic instability; Deep Learning; Deep ocean flows; Mesoscale eddies; Sea surface height interpolation; State estimation.
Ano: 2021
URL: https://archimer.ifremer.fr/doc/00663/77502/79235.pdf