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Improving forecasts of individual ocean eddies using feature mapping

Marine industries, war fighters, and world leaders demand accurate maps of ocean properties to underpin tactical and strategic decisions. Oceanographers have approached this challenge by borrowing mapping techniques from weather forecasters. However, compared to the atmosphere, the spatial scales of...

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Autor principal: Rykova, Tatiana
Formato: Online Artículo Texto
Lenguaje:English
Publicado: Nature Publishing Group UK 2023
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10110584/
https://www.ncbi.nlm.nih.gov/pubmed/37069250
http://dx.doi.org/10.1038/s41598-023-33465-9
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author Rykova, Tatiana
author_facet Rykova, Tatiana
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description Marine industries, war fighters, and world leaders demand accurate maps of ocean properties to underpin tactical and strategic decisions. Oceanographers have approached this challenge by borrowing mapping techniques from weather forecasters. However, compared to the atmosphere, the spatial scales of the ocean are small, and ocean properties are vastly under-sampled. Not surprisingly, despite decades of dedicated effort, the quality of maps of under-sea conditions remains poor. Feature mapping is a new approach to this problem that treats every ocean eddy individually. It strictly limits the influence of each observation to the oceanographic feature that it directly observes. Resulting maps are precise and realistic and may revolutionise ocean forecasting.
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spelling pubmed-101105842023-04-19 Improving forecasts of individual ocean eddies using feature mapping Rykova, Tatiana Sci Rep Article Marine industries, war fighters, and world leaders demand accurate maps of ocean properties to underpin tactical and strategic decisions. Oceanographers have approached this challenge by borrowing mapping techniques from weather forecasters. However, compared to the atmosphere, the spatial scales of the ocean are small, and ocean properties are vastly under-sampled. Not surprisingly, despite decades of dedicated effort, the quality of maps of under-sea conditions remains poor. Feature mapping is a new approach to this problem that treats every ocean eddy individually. It strictly limits the influence of each observation to the oceanographic feature that it directly observes. Resulting maps are precise and realistic and may revolutionise ocean forecasting. Nature Publishing Group UK 2023-04-17 /pmc/articles/PMC10110584/ /pubmed/37069250 http://dx.doi.org/10.1038/s41598-023-33465-9 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open AccessThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) .
spellingShingle Article
Rykova, Tatiana
Improving forecasts of individual ocean eddies using feature mapping
title Improving forecasts of individual ocean eddies using feature mapping
title_full Improving forecasts of individual ocean eddies using feature mapping
title_fullStr Improving forecasts of individual ocean eddies using feature mapping
title_full_unstemmed Improving forecasts of individual ocean eddies using feature mapping
title_short Improving forecasts of individual ocean eddies using feature mapping
title_sort improving forecasts of individual ocean eddies using feature mapping
topic Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10110584/
https://www.ncbi.nlm.nih.gov/pubmed/37069250
http://dx.doi.org/10.1038/s41598-023-33465-9
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