Stochastic spatio-temporal model for wind speed variation in the Arctic
Journal article, 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


Wengang Mao

Chalmers, Mechanics and Maritime Sciences (M2), Marine Technology

Igor Rychlik

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

Ocean Engineering

0029-8018 (ISSN)

Vol. 165 1 237-251

Modeling environmental loads and structural responses

Swedish Research Council (VR) (2012-6004), 2012-01-01 -- 2015-12-31.

Explore innovative solutions for arctic shipping

The Swedish Foundation for International Cooperation in Research and Higher Education (STINT) (Dnr:CH2016-6673), 2017-05-01 -- 2020-06-30.

Areas of Advance

Information and Communication Technology



Driving Forces

Sustainable development


Basic sciences

Subject Categories

Vehicle Engineering

Oceanography, Hydrology, Water Resources

Probability Theory and Statistics



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