An evaluation of conditional spatial predictions of significant wave height based on the nonstationary spde model
Paper i proceeding, 2020

The sea state plays an important role in offshore-and marine operations. It affects both direct costs as well as risks for human and/or material loss. A better understanding of the present-, near-future-, and far-future sea states will increase efficiency and safety in shipping since it allow a ship to reroute to a safer and/or more cost effective route. In the offshore industry it allows for minimizing downtime and aids in planning the construction of new offshore sites. Due to the complex nature of the sea state, its spatial distribution over a large region of ocean should be modeled using a probabilistic model. In this way, uncertainties due to lack of information and/or computing power can be quantified and decisions can be taken based on both what is known and what is not known. We analyze such a spatial probabilistic model in order to assess its ability to predict the significant wave height in the whole north Atlantic based only on measurements on a small line path, i.e., conditional prediction. This work is relevant for several applications, for instance data assimilation, oceanographic forecasting, and routing of ships.

Gaussian random field

Significant wave height

Stochastic partial differential equation

Författare

Anders Hildeman

Chalmers, Rymd-, geo- och miljövetenskap, Mikrovågs- och optisk fjärranalys

Wengang Mao

Chalmers, Mekanik och maritima vetenskaper, Marin teknik

Proceedings of the International Offshore and Polar Engineering Conference

10986189 (ISSN) 15551792 (eISSN)

Vol. 2020-October 2176-2183

30th International Ocean and Polar Engineering Conference
Shanghai, China,

E-Nav - Efficient Electronic Navigation at Sea

VINNOVA, 2019-04-11 -- 2021-10-11.

VINNOVA, 2019-03-01 -- 2021-08-31.

Europeiska kommissionen (EU), 2019-04-11 -- 2021-10-11.

Styrkeområden

Transport

Ämneskategorier

Sannolikhetsteori och statistik

Relaterade dataset

ERA5 [dataset]

URI: https://www.ecmwf.int/en/forecasts/datasets/reanalysis-datasets/era5

Mer information

Senast uppdaterat

2020-11-06