Stochastic spatio-temporal model for wind speed variation in the Arctic
Artikel i vetenskaplig tidskrift, 2018

A spatio-temporal transformed Gaussian field has been proposed to model wind variability in the northern North Atlantic, but it does not accurately describe the extreme wind speeds attributed to tropical storms and hurricanes. In Rychlik and Mao (2018), this model was generalized by adding certain number of random components to model rare events with extreme wind speeds or severe storms, and was named the hybrid model.

In this study, these models are further developed and validated to properly describe the variation of wind speeds in the Arctic area. In most locations, the transformed Gaussian field is a sufficiently accurate model. However, in some regions, e.g., the Laptev and Beaufort Seas, this model severely underestimates the frequencies of extreme wind speeds. Therefore, the hybrid model is further improved to add Poisson distributed random storm events to describe the wind variation in these regions, and is named as the Poisson hybrid model. There are also locations, e.g., along the east coast of Greenland, where the frequencies of high wind speeds are severely overestimated by the transformed Gaussian model. It is shown that this model can be used to estimate the long-term distribution of wind speeds, predict extreme wind speeds and simulate the spatio-temporal wind fields for practical applications.

Wind speed

Spatio-temporal wind statistics

Hermite transformation

Exponential transformation

The Arctic

Extreme wind

Poisson hybrid model

Gaussian field

Författare

Wengang Mao

Chalmers, Mekanik och maritima vetenskaper, Marin teknik

Igor Rychlik

Chalmers, Matematiska vetenskaper, Tillämpad matematik och statistik

Ocean Engineering

0029-8018 (ISSN)

Vol. 165 1 237-251

Stokastiska modeller för vind- och våglaster

Vetenskapsrådet (VR), 2012-01-01 -- 2015-12-31.

Utforska innovativa lösningar för arktisk sjöfart

STINT, 2017-05-01 -- 2020-06-30.

Styrkeområden

Informations- och kommunikationsteknik

Transport

Energi

Drivkrafter

Hållbar utveckling

Fundament

Grundläggande vetenskaper

Ämneskategorier

Farkostteknik

Oceanografi, hydrologi, vattenresurser

Sannolikhetsteori och statistik

DOI

10.1016/j.oceaneng.2018.07.043

Mer information

Senast uppdaterat

2018-12-10