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...

Full description

Bibliographic Details
Published in:Probabilistic Engineering Mechanics
Main Authors: Hildeman, Anders, Bolin, David, Rychlik, Igor
Language:unknown
Published: 2022
Subjects:
Online Access:https://doi.org/10.1016/j.probengmech.2022.103203
https://research.chalmers.se/en/publication/528984
_version_ 1835018313133981696
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