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

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. Her...

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Autores principales: Hyun, Sangwon, Mishra, Aditya, Follett, Christopher L., Jonsson, Bror, Kulk, Gemma, Forget, Gael, Racault, Marie-Fanny, Jackson, Thomas, Dutkiewicz, Stephanie, Müller, Christian L., Bien, Jacob
Formato: Online Artículo Texto
Lenguaje:English
Publicado: The Royal Society 2022
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9215217/
https://www.ncbi.nlm.nih.gov/pubmed/35756877
http://dx.doi.org/10.1098/rspa.2021.0875
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author Hyun, Sangwon
Mishra, Aditya
Follett, Christopher L.
Jonsson, Bror
Kulk, Gemma
Forget, Gael
Racault, Marie-Fanny
Jackson, Thomas
Dutkiewicz, Stephanie
Müller, Christian L.
Bien, Jacob
author_facet Hyun, Sangwon
Mishra, Aditya
Follett, Christopher L.
Jonsson, Bror
Kulk, Gemma
Forget, Gael
Racault, Marie-Fanny
Jackson, Thomas
Dutkiewicz, Stephanie
Müller, Christian L.
Bien, Jacob
author_sort Hyun, Sangwon
collection PubMed
description 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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spelling pubmed-92152172022-06-24 Ocean mover’s distance: using optimal transport for analysing oceanographic data Hyun, Sangwon Mishra, Aditya Follett, Christopher L. Jonsson, Bror Kulk, Gemma Forget, Gael Racault, Marie-Fanny Jackson, Thomas Dutkiewicz, Stephanie Müller, Christian L. Bien, Jacob Proc Math Phys Eng Sci Research Articles 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. The Royal Society 2022-06 2022-06-22 /pmc/articles/PMC9215217/ /pubmed/35756877 http://dx.doi.org/10.1098/rspa.2021.0875 Text en © 2022 The Authors. https://creativecommons.org/licenses/by/4.0/Published by the Royal Society under the terms of the Creative Commons Attribution License http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) , which permits unrestricted use, provided the original author and source are credited.
spellingShingle Research Articles
Hyun, Sangwon
Mishra, Aditya
Follett, Christopher L.
Jonsson, Bror
Kulk, Gemma
Forget, Gael
Racault, Marie-Fanny
Jackson, Thomas
Dutkiewicz, Stephanie
Müller, Christian L.
Bien, Jacob
Ocean mover’s distance: using optimal transport for analysing oceanographic data
title Ocean mover’s distance: using optimal transport for analysing oceanographic data
title_full Ocean mover’s distance: using optimal transport for analysing oceanographic data
title_fullStr Ocean mover’s distance: using optimal transport for analysing oceanographic data
title_full_unstemmed Ocean mover’s distance: using optimal transport for analysing oceanographic data
title_short Ocean mover’s distance: using optimal transport for analysing oceanographic data
title_sort ocean mover’s distance: using optimal transport for analysing oceanographic data
topic Research Articles
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9215217/
https://www.ncbi.nlm.nih.gov/pubmed/35756877
http://dx.doi.org/10.1098/rspa.2021.0875
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