Synergy between Observation Systems Oceanic in Turbulent Regions
- URL: http://arxiv.org/abs/2012.14516v2
- Date: Wed, 27 Jan 2021 16:21:31 GMT
- Title: Synergy between Observation Systems Oceanic in Turbulent Regions
- Authors: Van-Khoa Nguyen, Santiago Agudelo
- Abstract summary: Ocean dynamics constitute a source of incertitude in determining the ocean's role in complex climatic phenomena.
Current observation systems have limitations in achieving sufficiently statistical precision for three-dimensional oceanic data.
We present the data-driven approaches which explore latent class regressions and deep regression neural networks in modeling ocean dynamics in the extensions of Gulf Stream and Kuroshio currents.
- Score: 0.0
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Ocean dynamics constitute a source of incertitude in determining the ocean's
role in complex climatic phenomena. Current observation systems have
limitations in achieving sufficiently statistical precision for
three-dimensional oceanic data. It is crucial knowledge to describe the
behavior of internal ocean structures. We present the data-driven approaches
which explore latent class regressions and deep regression neural networks in
modeling ocean dynamics in the extensions of Gulf Stream and Kuroshio currents.
The obtained results show a promising data-driven direction for understanding
the ocean's characteristics, including salinity and temperature, in both
spatial and temporal dimensions in the turbulent regions. Our source codes are
publicly available at https://github.com/v18nguye/gulfstream-lrm and at
https://github.com/sagudelor/Kuroshio.
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