Bridging observations, theory and numerical simulation of the ocean using machine learning

Sonnewald, Maike; Lguensat, Redouane; Jones, Daniel C. ORCID: https://orcid.org/0000-0002-8701-4506; Dueben, Peter D.; Brajard, Julien; Balaji, Venkatramani. 2021 Bridging observations, theory and numerical simulation of the ocean using machine learning. Environmental Research Letters, 16 (7), 073008. 28, pp. https://doi.org/10.1088/1748-9326/ac0eb0

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Abstract/Summary

Progress within physical oceanography has been concurrent with the increasing sophistication of tools available for its study. The incorporation of machine learning (ML) techniques offers exciting possibilities for advancing the capacity and speed of established methods and for making substantial and serendipitous discoveries. Beyond vast amounts of complex data ubiquitous in many modern scientific fields, the study of the ocean poses a combination of unique challenges that ML can help address. The observational data available is largely spatially sparse, limited to the surface, and with few time series spanning more than a handful of decades. Important timescales span seconds to millennia, with strong scale interactions and numerical modelling efforts complicated by details such as coastlines. This review covers the current scientific insight offered by applying ML and points to where there is imminent potential. We cover the main three branches of the field: observations, theory, and numerical modelling. Highlighting both challenges and opportunities, we discuss both the historical context and salient ML tools. We focus on the use of ML in situ sampling and satellite observations, and the extent to which ML applications can advance theoretical oceanographic exploration, as well as aid numerical simulations. Applications that are also covered include model error and bias correction and current and potential use within data assimilation. While not without risk, there is great interest in the potential benefits of oceanographic ML applications; this review caters to this interest within the research community.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	https://doi.org/10.1088/1748-9326/ac0eb0
ISSN:	17489326
Additional Keywords:	Ocean Science, physical oceanography, machine learning, observations, theory, modelling, supervised machine learning, unsupervised machine learning
Date made live:	31 Jul 2021 06:00 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/530155

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