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Novel methods for coupled prediction of extreme wind speeds and wave heights
Two novel methods are being outlined that, when combined, can be used for spatiotemporal analysis of wind speeds and wave heights, thus contributing to global climate studies. First, the authors provide a unique reliability approach that is especially suited for multi-dimensional structural and envi...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9860070/ https://www.ncbi.nlm.nih.gov/pubmed/36670233 http://dx.doi.org/10.1038/s41598-023-28136-8 |
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author | Gaidai, Oleg Xing, Yihan Xu, Xiaosen |
author_facet | Gaidai, Oleg Xing, Yihan Xu, Xiaosen |
author_sort | Gaidai, Oleg |
collection | PubMed |
description | Two novel methods are being outlined that, when combined, can be used for spatiotemporal analysis of wind speeds and wave heights, thus contributing to global climate studies. First, the authors provide a unique reliability approach that is especially suited for multi-dimensional structural and environmental dynamic system responses that have been numerically simulated or observed over a substantial time range, yielding representative ergodic time series. Next, this work introduces a novel deconvolution extrapolation technique applicable to a wide range of environmental and engineering applications. Classical reliability approaches cannot cope with dynamic systems with high dimensionality and responses with complicated cross-correlation. The combined study of wind speed and wave height is notoriously difficult, since they comprise a very complex, multi-dimensional, non-linear environmental system. Additionally, global warming is a significant element influencing ocean waves throughout the years. Furthermore, the environmental system reliability method is crucial for structures working in any particular region of interest and facing actual and often harsh weather conditions. This research demonstrates the effectiveness of our approach by applying it to the concurrent prediction of wind speeds and wave heights from NOAA buoys in the North Pacific. This study aims to evaluate the state-of-the-art approach that extracts essential information about the extreme responses from observed time histories. |
format | Online Article Text |
id | pubmed-9860070 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | Nature Publishing Group UK |
record_format | MEDLINE/PubMed |
spelling | pubmed-98600702023-01-22 Novel methods for coupled prediction of extreme wind speeds and wave heights Gaidai, Oleg Xing, Yihan Xu, Xiaosen Sci Rep Article Two novel methods are being outlined that, when combined, can be used for spatiotemporal analysis of wind speeds and wave heights, thus contributing to global climate studies. First, the authors provide a unique reliability approach that is especially suited for multi-dimensional structural and environmental dynamic system responses that have been numerically simulated or observed over a substantial time range, yielding representative ergodic time series. Next, this work introduces a novel deconvolution extrapolation technique applicable to a wide range of environmental and engineering applications. Classical reliability approaches cannot cope with dynamic systems with high dimensionality and responses with complicated cross-correlation. The combined study of wind speed and wave height is notoriously difficult, since they comprise a very complex, multi-dimensional, non-linear environmental system. Additionally, global warming is a significant element influencing ocean waves throughout the years. Furthermore, the environmental system reliability method is crucial for structures working in any particular region of interest and facing actual and often harsh weather conditions. This research demonstrates the effectiveness of our approach by applying it to the concurrent prediction of wind speeds and wave heights from NOAA buoys in the North Pacific. This study aims to evaluate the state-of-the-art approach that extracts essential information about the extreme responses from observed time histories. Nature Publishing Group UK 2023-01-20 /pmc/articles/PMC9860070/ /pubmed/36670233 http://dx.doi.org/10.1038/s41598-023-28136-8 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This 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 Gaidai, Oleg Xing, Yihan Xu, Xiaosen Novel methods for coupled prediction of extreme wind speeds and wave heights |
title | Novel methods for coupled prediction of extreme wind speeds and wave heights |
title_full | Novel methods for coupled prediction of extreme wind speeds and wave heights |
title_fullStr | Novel methods for coupled prediction of extreme wind speeds and wave heights |
title_full_unstemmed | Novel methods for coupled prediction of extreme wind speeds and wave heights |
title_short | Novel methods for coupled prediction of extreme wind speeds and wave heights |
title_sort | novel methods for coupled prediction of extreme wind speeds and wave heights |
topic | Article |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9860070/ https://www.ncbi.nlm.nih.gov/pubmed/36670233 http://dx.doi.org/10.1038/s41598-023-28136-8 |
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