Ocean mover’s distance: using optimal transport for analysing oceanographic data

Hyun, S, Mishra, A, Follett, CL, Jonsson, B, Kulk, G, Forget, G, Racault, M-FLP, Jackson, T, Dutkiewicz, S, Müller, CL and Bien, J 2022 Ocean mover’s distance: using optimal transport for analysing oceanographic data. Proceedings of the Royal Society A: Mathematical, Physical and Engineering Sciences, 478 (2262). https://doi.org/10.1098/rspa.2021.0875

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Official URL: http://dx.doi.org/10.1098/rspa.2021.0875


Remote sensing observations from satellites and global biogeochemical models have combined to revolutionize the study of ocean biogeochemical cycling, but comparing the two data streams to each other and across time remains challenging due to the strong spatial-temporal structuring of the ocean. Here, we show that the Wasserstein distance provides a powerful metric for harnessing these structured datasets for better marine ecosystem and climate predictions. The Wasserstein distance complements commonly used point-wise difference methods such as the root-mean-squared error, by quantifying differences in terms of spatial displacement in addition to magnitude. As a test case, we consider chlorophyll (a key indicator of phytoplankton biomass) in the northeast Pacific Ocean, obtained from model simulations, in situ measurements, and satellite observations. We focus on two main applications: (i) comparing model predictions with satellite observations, and (ii) temporal evolution of chlorophyll both seasonally and over longer time frames. The Wasserstein distance successfully isolates temporal and depth variability and quantifies shifts in biogeochemical province boundaries. It also exposes relevant temporal trends in satellite chlorophyll consistent with climate change predictions. Our study shows that optimal transport vectors underlying the Wasserstein distance provide a novel visualization tool for testing models and better understanding temporal dynamics in the ocean.

Item Type: Publication - Article
Additional Keywords: Wasserstein distance, earth mover’s distance, data-model comparison, optimal transport, chlorophyll, remote sensing
Divisions: Plymouth Marine Laboratory > Science Areas > Earth Observation Science and Applications
Depositing User: S Hawkins
Date made live: 12 May 2023 14:35
Last Modified: 12 May 2023 14:35
URI: https://plymsea.ac.uk/id/eprint/9921

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