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

• Published in: Ocean Engineering
• Main Authors: Mao, Wengang, Rychlik, Igor
• Language: unknown
• Published: 2018
• Subjects: Vehicle Engineering; Oceanography; Hydrology; Water Resources; Probability Theory and Statistics; Wind speed; Spatio-temporal wind statistics; Hermite transformation; Exponential transformation; The Arctic; Extreme wind; Poisson hybrid model; Gaussian field; Arctic; Greenland; laptev; North Atlantic
• Online Access: https://doi.org/10.1016/j.oceaneng.2018.07.043; https://research.chalmers.se/en/publication/33cd6e8b-cb37-4920-8244-9943e2ead3fd

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.