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Hyun, Sangwon, Mishra, Aditya, Follett, Christopher, Jonsson, Bror, Kulk, Gemma, Forget, Gael, Racault, Marie-Fanny 
ORCID: https://orcid.org/0000-0002-7584-2515, Jackson, Thomas, Dutkiewicz, Stephanie, Muller, Christian and Bien, Jacob
(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).
ISSN 1364-5021
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Abstract
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: | Article |
|---|---|
| Additional Information: | Data accessibility statement: Data and code are available at https://github.com/sangwon-hyun/omd. All data used in this article is already available to the public via the UCI Machine Learning Respository. We have submitted the code used to generate the graphs and tables in the paper as electronic supplementary material [62] and grant permission for this to be made public. Funding Information: This work was supported by grants by the Simons Collaboration on Computational Biogeochemical Modeling of Marine Ecosystems/CBIOMES (grant no. 549939 to B.J.; 827829 and 553242 to C.L.F.; 549931 to M.-F.R.). Dr J.B. was also supported in part by NIH grant no. R01GM123993 and NSF CAREER award DMS-1653017. T.J. was also supported by the National Centre for Earth Observations of the UK. M.-F.R. was also partially funded by the ‘Frontiers of instability in marine ecosystems and carbon export (Marine Frontiers) [NE/V011103/1]’. |
| Uncontrolled Keywords: | wasserstein distance,chlorophyll,data-model comparison,earth mover's distance,optimal transport,remote sensing,mathematics(all),engineering(all),physics and astronomy(all) ,/dk/atira/pure/subjectarea/asjc/2600 |
| Faculty \ School: | Faculty of Science > School of Environmental Sciences University of East Anglia Research Groups/Centres > Theme - ClimateUEA |
| UEA Research Groups: | Faculty of Science > Research Centres > Centre for Ecology, Evolution and Conservation |
| Related URLs: | |
| Depositing User: | LivePure Connector |
| Date Deposited: | 25 Jul 2022 09:30 |
| Last Modified: | 08 Dec 2024 01:35 |
| URI: | https://ueaeprints.uea.ac.uk/id/eprint/86840 |
| DOI: | 10.1098/rspa.2021.0875 |
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