ArticleProceedings. Mathematical, physical, and engineering sciences2022
Ocean mover's distance: using optimal transport for analysing oceanographic data.
Article in Proceedings. Mathematical, physical, and engineering sciences, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
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Who cites it
2 citing papers in PubMed.
- Understanding opposing predictions of Prochlorococcus in a changing climate.Nature communications · 2023Article
- Ocean mover's distance: using optimal transport for analysing oceanographic data.Proceedings. Mathematical, physical, and engineering sciences · 2022Article
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Authors and funding
11 authors.
Funding
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,
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