Joint spatial modeling of significant wave height and wave period using the SPDE approach
Artikel i vetenskaplig tidskrift, 2022

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.

Significant wave height

SPDE approach

Wave period

Non-stationary Gaussian random fields

Stochastic weather generator

Författare

Anders Hildeman

Chalmers, Rymd-, geo- och miljövetenskap, Geovetenskap och fjärranalys

David Bolin

King Abdullah University of Science and Technology (KAUST)

Igor Rychlik

Chalmers, Matematiska vetenskaper

Probabilistic Engineering Mechanics

0266-8920 (ISSN)

Vol. 68 103203

Ämneskategorier

Meteorologi och atmosfärforskning

Oceanografi, hydrologi, vattenresurser

Sannolikhetsteori och statistik

DOI

10.1016/j.probengmech.2022.103203

Mer information

Senast uppdaterat

2022-03-14