Spatio-temporal modelling of wind speed variation
Paper in proceedings, 2018

Wind speed variability in the Northern North Atlantic has been success- fully modelled by a spatio-temporal transformed Gaussian field in our previous study. It was shown that this type of model does not describe correctly the extreme wind speeds attributed to tropical storms and hurri- canes. This spatio-temporal model was generalized to include the possi- bility of the occurrence of rare severe storms. In that work, the daily wind speed variability was modelled by the transformed Gaussian field, and then random components were added to model rare events with extreme wind speeds. The model was termed the hybrid model. The transformed Gaussian and the hybrid models are locally stationary and homogeneous random fields with localized dependence structure, which is described by time and space dependent parameters with a natural physical interpreta- tion.

In the present study, these models are used to describe the variability of wind speed in other areas, i.e., the Caribbean sea, the South China Sea and the Arctic area. In most locations, the transformed Gaussian field is a sufficiently accurate model. However, in some regions, e.g. Laptev and the Beaufort Sea at the Arctic, this model severely underestimates the frequencies of extreme winds. In this study, the hybrid model is used to describe the wind variation in these regions. There are also locations, e.g. along the east coast of Greenland, most of the coast areas of the South China Sea, where frequencies of high wind speeds are severely overestimated by the transformed Gaussian model.

In this paper, the models are fitted to ERA-Interim reanalysis wind data and used to find long-term distributions of wind speed, to estimate wind speed return values, e.g. 100-year extreme wind speed, and to compute the expected yearly frequency of events that wind speed exceeds a fixed threshold value.

Extreme wind

South China Sea

Wind speed

Spatio-temporal wind statistics

Gaussian field

Caribbean Sea

Arctic Ocean

Hybrid model

Weibull model


Wengang Mao

Chalmers, Mechanics and Maritime Sciences, Marine Technology

Oscar Ivarsson

Administration DoIT

Igor Rychlik

Chalmers, Mathematical Sciences, Applied Mathematics and Statistics

The Twenty-eighth (2018) International Ocean and Polar Engineering Conference
Sapporo, Japan,

Big data based autonomous navigation system for safe and efficient shipping

Chalmers, 2018-01-01 -- 2019-12-31.

Explore innovative solutions for arctic shipping

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

Modeling environmental loads and structural responses

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

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Meteorology and Atmospheric Sciences

Physical Geography

Probability Theory and Statistics


Basic sciences

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