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Hyun, S.* ; Mishra, A.* ; Follett, C.L.* ; Jönsson, B.* ; Kulk, G.* ; Forget, G.* ; Racault, M.F.* ; Jackson, T.* ; Dutkiewicz, S.* ; Müller, C.L. ; Bien, J.*

Ocean mover's distance: Using optimal transport for analysing oceanographic data.

Proc. R. Soc. London A 478:20210875 (2022)
Postprint Forschungsdaten DOI PMC
Open Access Hybrid
Creative Commons Lizenzvertrag
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
Schlagwörter Wasserstein Distance ; Chlorophyll ; Data-model Comparison ; Earth Mover’s Distance ; Optimal Transport ; Remote Sensing
Sprache englisch
Veröffentlichungsjahr 2022
HGF-Berichtsjahr 2022
ISSN (print) / ISBN 1364-5021
e-ISSN 1471-2946
Zeitschrift Proceedings of the Royal Society of London
Quellenangaben Band: 478, Heft: 2262, Seiten: , Artikelnummer: 20210875 Supplement: ,
Verlag Royal Society of London
Verlagsort London
Begutachtungsstatus Peer reviewed
Institut(e) Institute of Computational Biology (ICB)
POF Topic(s) 30205 - Bioengineering and Digital Health
Forschungsfeld(er) Enabling and Novel Technologies
PSP-Element(e) G-503800-001
Förderungen Frontiers of instability in marine ecosystems and carbon export (Marine Frontiers)
National Science Foundation (NSF) NSF - Office of the Director (OD)
United States Department of Health & Human Services National Institutes of Health (NIH) - USA
Simons Collaboration on Computational Biogeochemical Modeling of Marine Ecosystems/CBIOMES
DOI 10.1098/rspa.2021.0875
WOS ID WOS:000814685100010
Scopus ID 85142711237
PubMed ID 35756877
Erfassungsdatum 2022-09-26
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