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Estimating Ocean Surface Currents With Machine Learning

Sinha, Anirban and Abernathey, Ryan (2021) Estimating Ocean Surface Currents With Machine Learning. Frontiers in Marine Science, 8 . Art. No. 672477. ISSN 2296-7745. doi:10.3389/fmars.2021.672477.

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Global surface currents are usually inferred from directly observed quantities like sea-surface height, wind stress by applying diagnostic balance relations (like geostrophy and Ekman flow), which provide a good approximation of the dynamics of slow, large-scale currents at large scales and low Rossby numbers. However, newer generation satellite altimeters (like the upcoming SWOT mission) will capture more of the high wavenumber variability associated with the unbalanced components, but the low temporal sampling can potentially lead to aliasing. Applying these balances directly may lead to an incorrect un-physical estimate of the surface flow. In this study we explore Machine Learning (ML) algorithms as an alternate route to infer surface currents from satellite observable quantities. We train our ML models with SSH, SST, and wind stress from available primitive equation ocean GCM simulation outputs as the inputs and make predictions of surface currents (u,v), which are then compared against the true GCM output. As a baseline example, we demonstrate that a linear regression model is ineffective at predicting velocities accurately beyond localized regions. In comparison, a relatively simple neural network (NN) can predict surface currents accurately over most of the global ocean, with lower mean squared errors than geostrophy + Ekman. Using a local stencil of neighboring grid points as additional input features, we can train the deep learning models to effectively “learn” spatial gradients and the physics of surface currents. By passing the stenciled variables through convolutional filters we can help the model learn spatial gradients much faster. Various training strategies are explored using systematic feature hold out and multiple combinations of point and stenciled input data fed through convolutional filters (2D/3D), to understand the effect of each input feature on the NN's ability to accurately represent surface flow. A model sensitivity analysis reveals that besides SSH, geographic information in some form is an essential ingredient required for making accurate predictions of surface currents with deep learning models.

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URLURL TypeDescription Paper ItemData
Sinha, Anirban0000-0002-7122-3549
Abernathey, Ryan0000-0001-5999-4917
Alternate Title:Estimating Ocean Surface Currents from Satellite Observable Quantities with Machine Learning
Additional Information:© 2021 Sinha and Abernathey. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. Received: 25 February 2021; Accepted: 03 May 2021; Published: 09 June 2021. The work was started in 2018 and an early proof-of-concept was reported in AS's PhD dissertation (Sinha, 2019). Data Availability Statement: Publicly available datasets were analyzed in this study. This data can be found at: Author Contributions: AS and RA: study conception and design and draft manuscript preparation. AS: training and testing of statistical models and analysis and interpretation of results. All authors reviewed the results and approved the final version of the manuscript. The authors acknowledge support from NSF Award OCE 1553594 and NASA award NNX16AJ35G (SWOT Science Team). The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.
Funding AgencyGrant Number
Subject Keywords:deep learning-artificial neural network, surface current balance, geostrophic balance, Ekman flow, regression, predictive modeling
Record Number:CaltechAUTHORS:20210707-195534480
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Official Citation:Sinha A and Abernathey R (2021) Estimating Ocean Surface Currents With Machine Learning. Front. Mar. Sci. 8:672477. doi: 10.3389/fmars.2021.672477
Usage Policy:No commercial reproduction, distribution, display or performance rights in this work are provided.
ID Code:109749
Deposited By: Tony Diaz
Deposited On:08 Jul 2021 17:10
Last Modified:08 Jul 2021 17:10

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