Cargando…
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...
Autores principales: | , , , , , , , , , , |
---|---|
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 |
_version_ | 1784731167085297664 |
---|---|
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. |
format | Online Article Text |
id | pubmed-9215217 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2022 |
publisher | The Royal Society |
record_format | MEDLINE/PubMed |
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 |
work_keys_str_mv | AT hyunsangwon oceanmoversdistanceusingoptimaltransportforanalysingoceanographicdata AT mishraaditya oceanmoversdistanceusingoptimaltransportforanalysingoceanographicdata AT follettchristopherl oceanmoversdistanceusingoptimaltransportforanalysingoceanographicdata AT jonssonbror oceanmoversdistanceusingoptimaltransportforanalysingoceanographicdata AT kulkgemma oceanmoversdistanceusingoptimaltransportforanalysingoceanographicdata AT forgetgael oceanmoversdistanceusingoptimaltransportforanalysingoceanographicdata AT racaultmariefanny oceanmoversdistanceusingoptimaltransportforanalysingoceanographicdata AT jacksonthomas oceanmoversdistanceusingoptimaltransportforanalysingoceanographicdata AT dutkiewiczstephanie oceanmoversdistanceusingoptimaltransportforanalysingoceanographicdata AT mullerchristianl oceanmoversdistanceusingoptimaltransportforanalysingoceanographicdata AT bienjacob oceanmoversdistanceusingoptimaltransportforanalysingoceanographicdata |