Joint spatial modeling of significant wave height and wave period using the SPDE approach
The ocean wave distribution in a specific region of space and time is described by its sea state. Knowledge about the sea states a ship encounters on a journey can be used to assess various parameters of risk and wear associated with this journey. Two important characteristics of the sea state are s...
Published in: | Probabilistic Engineering Mechanics |
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Main Authors: | , , |
Language: | unknown |
Published: |
2022
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Subjects: | |
Online Access: | https://doi.org/10.1016/j.probengmech.2022.103203 https://research.chalmers.se/en/publication/528984 |
_version_ | 1835018313133981696 |
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author | Hildeman, Anders Bolin, David Rychlik, Igor |
author_facet | Hildeman, Anders Bolin, David Rychlik, Igor |
author_sort | Hildeman, Anders |
collection | Unknown |
container_start_page | 103203 |
container_title | Probabilistic Engineering Mechanics |
container_volume | 68 |
description | The ocean wave distribution in a specific region of space and time is described by its sea state. Knowledge about the sea states a ship encounters on a journey can be used to assess various parameters of risk and wear associated with this journey. Two important characteristics of the sea state are significant wave height and mean wave period. We propose a joint spatial model of these two quantities on the north Atlantic ocean. The model describes the distribution of the logarithm of the two quantities as a bivariate Gaussian random field, modeled as a solution to a system of coupled fractional stochastic partial differential equations. The bivariate random field is non-stationary and allows for arbitrary, and different, smoothness for the two marginal fields. The parameters of the model are estimated from data using a stepwise maximum likelihood method. The fitted model is used to derive the distribution of accumulated fatigue damage for a ship sailing a transatlantic route. Also, a method for estimating the risk of capsizing due to broaching-to based on the joint distribution of the two sea state characteristics is investigated. The risks are calculated for a transatlantic route between America and Europe using both data and the fitted model. The results show that the model compares well with observed data. It further shows that the bivariate model is needed and cannot simply be approximated by a model of significant wave height alone. |
genre | North Atlantic |
genre_facet | North Atlantic |
id | ftchalmersuniv:oai:research.chalmers.se:528984 |
institution | Open Polar |
language | unknown |
op_collection_id | ftchalmersuniv |
op_doi | https://doi.org/10.1016/j.probengmech.2022.103203 |
op_relation | http://dx.doi.org/10.1016/j.probengmech.2022.103203 https://research.chalmers.se/en/publication/528984 |
publishDate | 2022 |
record_format | openpolar |
spelling | ftchalmersuniv:oai:research.chalmers.se:528984 2025-06-15T14:43:22+00:00 Joint spatial modeling of significant wave height and wave period using the SPDE approach Hildeman, Anders Bolin, David Rychlik, Igor 2022 text https://doi.org/10.1016/j.probengmech.2022.103203 https://research.chalmers.se/en/publication/528984 unknown http://dx.doi.org/10.1016/j.probengmech.2022.103203 https://research.chalmers.se/en/publication/528984 Meteorology and Atmospheric Sciences Oceanography Hydrology Water Resources Probability Theory and Statistics Significant wave height SPDE approach Wave period Non-stationary Gaussian random fields Stochastic weather generator 2022 ftchalmersuniv https://doi.org/10.1016/j.probengmech.2022.103203 2025-05-19T04:26:16Z The ocean wave distribution in a specific region of space and time is described by its sea state. Knowledge about the sea states a ship encounters on a journey can be used to assess various parameters of risk and wear associated with this journey. Two important characteristics of the sea state are significant wave height and mean wave period. We propose a joint spatial model of these two quantities on the north Atlantic ocean. The model describes the distribution of the logarithm of the two quantities as a bivariate Gaussian random field, modeled as a solution to a system of coupled fractional stochastic partial differential equations. The bivariate random field is non-stationary and allows for arbitrary, and different, smoothness for the two marginal fields. The parameters of the model are estimated from data using a stepwise maximum likelihood method. The fitted model is used to derive the distribution of accumulated fatigue damage for a ship sailing a transatlantic route. Also, a method for estimating the risk of capsizing due to broaching-to based on the joint distribution of the two sea state characteristics is investigated. The risks are calculated for a transatlantic route between America and Europe using both data and the fitted model. The results show that the model compares well with observed data. It further shows that the bivariate model is needed and cannot simply be approximated by a model of significant wave height alone. Other/Unknown Material North Atlantic Unknown Probabilistic Engineering Mechanics 68 103203 |
spellingShingle | Meteorology and Atmospheric Sciences Oceanography Hydrology Water Resources Probability Theory and Statistics Significant wave height SPDE approach Wave period Non-stationary Gaussian random fields Stochastic weather generator Hildeman, Anders Bolin, David Rychlik, Igor Joint spatial modeling of significant wave height and wave period using the SPDE approach |
title | Joint spatial modeling of significant wave height and wave period using the SPDE approach |
title_full | Joint spatial modeling of significant wave height and wave period using the SPDE approach |
title_fullStr | Joint spatial modeling of significant wave height and wave period using the SPDE approach |
title_full_unstemmed | Joint spatial modeling of significant wave height and wave period using the SPDE approach |
title_short | Joint spatial modeling of significant wave height and wave period using the SPDE approach |
title_sort | joint spatial modeling of significant wave height and wave period using the spde approach |
topic | Meteorology and Atmospheric Sciences Oceanography Hydrology Water Resources Probability Theory and Statistics Significant wave height SPDE approach Wave period Non-stationary Gaussian random fields Stochastic weather generator |
topic_facet | Meteorology and Atmospheric Sciences Oceanography Hydrology Water Resources Probability Theory and Statistics Significant wave height SPDE approach Wave period Non-stationary Gaussian random fields Stochastic weather generator |
url | https://doi.org/10.1016/j.probengmech.2022.103203 https://research.chalmers.se/en/publication/528984 |