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

Journal article
Authors Wengang Mao
Igor Rychlik
Published in Ocean Engineering
Volume 165
Pages 237-251
ISSN 0029-8018
Publication year 2018
Published at Department of Mathematical Sciences
Pages 237-251
Language en
Links https://doi.org/10.1016/j.oceaneng....
Keywords Wind speed, Spatio-temporal wind statistics, The Arctic, Exponential transformation, Hermite transformation, Gaussian field, Poisson hybrid model, Extreme wind
Subject categories Probability Theory and Statistics, Oceanography, Hydrology, Water Resources, Vehicle Engineering

Abstract

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.

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