The challenge of land in a neural network ocean model

Furner, Rachel ORCID: https://orcid.org/0000-0003-0535-4388; Haynes, Peter; Jones, Dani C. ORCID: https://orcid.org/0000-0002-8701-4506; Munday, Dave ORCID: https://orcid.org/0000-0003-1920-708X; Paige, Brooks; Shuckburgh, Emily. 2025 The challenge of land in a neural network ocean model. Environmental Data Science, 3, e40. 19, pp. 10.1017/eds.2024.49

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Text (Open Access) © The Author(s), 2024. Published by Cambridge University Press. This is an Open Access article, distributed under the terms of the Creative Commons Attribution licence (http://creativecommons.org/licenses/by/4.0), which permits unrestricted re-use, distribution and reproduction, provided the original article is properly cited. the-challenge-of-land-in-a-neural-network-ocean-model.pdf - Published Version Available under License Creative Commons Attribution 4.0. Download (7MB) | Preview

Abstract/Summary

Machine learning (ML) techniques have emerged as a powerful tool for predicting weather and climate systems. However, much of the progress to date focuses on predicting the short-term evolution of the atmosphere. Here, we look at the potential for ML methodology to predict the evolution of the ocean. The presence of land in the domain is a key difference between ocean modeling and previous work looking at atmospheric modeling. Here, we look to train a convolutional neural network (CNN) to emulate a process-based General Circulation Model (GCM) of the ocean, in a configuration which contains land. We assess performance on predictions over the entire domain and near to the land (coastal points). Our results show that the CNN replicates the underlying GCM well when assessed over the entire domain. RMS errors over the test dataset are low in comparison to the signal being predicted, and the CNN model gives an order of magnitude improvement over a persistence forecast. When we partition the domain into near land and the ocean interior and assess performance over these two regions, we see that the model performs notably worse over the near land region. Near land, RMS scores are comparable to those from a simple persistence forecast. Our results indicate that ocean interaction with land is something the network struggles with and highlight that this is may be an area where advanced ML techniques specifically designed for, or adapted for, the geosciences could bring further benefits.

Item Type:	Publication - Article
Digital Object Identifier (DOI):	10.1017/eds.2024.49
ISSN:	2634-4602
Additional Keywords:	data science; machine learning; oceanography; data-driven; forecasting; coast
Date made live:	27 Jan 2025 12:04 +0 (UTC)
URI:	https://nora.nerc.ac.uk/id/eprint/538806

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