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Status: Unchecked ×Keyword: satellite altimetry ×Clear all

3 claims from 1 paper

1. Earth and Planetary Sciences › Oceanographic and Atmospheric Processes

   Applications of Deep Learning to Ocean Data Inference and Subgrid Parameterization

   Bolton and Zanna · Journal of Advances in Modeling Earth Systems · 2019

   The authors trained convolutional neural networks on degraded data from a high-resolution ocean model to predict small-scale eddy forcing and subsurface flow, as a proof of concept for ocean parameterisations.

   Unchecked3 claims
   Show 3 claims

   1. Unchecked“We demonstrate that convolutional neural networks successfully replicate the spatiotemporal variability of the subgrid eddy momentum forcing, are capable of generalizing to a range of dynamical behaviors, and can be forced to respect global momentum conserva…
   2. UncheckedIn this study, the convolutional neural networks could be trained on 10–20% of the original data with no significant drop in accuracy.“The training data of our convolutional neural networks can be subsampled to 10–20% of the original size without a significant decrease in accuracy.”
   3. UncheckedIn a proof-of-concept ocean model, neural networks predicted the flow below the surface using only surface information such as satellite altimetry.“We also show that the subsurface flow field can be predicted using only information at the surface (e.g., using only satellite altimetry data).”