2.1 An Observational Climatology of Wind and Wind Extremes at the North Sea for Load Assessment Studies

Monday, 23 January 2017: 1:30 PM
606 (Washington State Convention Center )
Peter Kalverla, Wageningen University, Wageningen, Netherlands; and G. J. Steeneveld, R. J. Ronda, and A. A. M. Holtslag

The current emergence of large offshore wind farms calls for improved understanding of the offshore wind climate. Detailed knowledge of wind variability is required for turbine load computations, but traditional methods fail to represent the full suite of offshore wind conditions. This research uses high quality observations up to 300m to characterize the wind climate at the North Sea. These data lead the way to estimate the uncertainty due to extrapolation methods and address the question "how to improve the representation of wind extremes in load assessment models?"
Several archetypes of anomalous wind events (AWEs) are defined to represent wind conditions that cannot be adequately described by the traditional logarithmic wind profile. Low-level jets are an illustrative example, but profiles with strong speed- or directional shear, strong turbulence and gusts, and wind ramps are also highlighted. These AWEs are then described in terms of their spatial and temporal characteristics, and their relation to other (meteorological) variables. This methodology is applied to four years of observations from the IJmuiden meteomast located 85 km off the Dutch Coast, occasionally complemented with observations from other locations.
The results indicate that at least 20% of the 10-min averaged wind profiles should be classified as AWEs. Offshore low-level jets occur up to 10% of the total time, with more jets in stable conditions during spring and early summer. Most wind shear also occurs under stable conditions, while strong turbulence and gusts are observed in unstable conditions and at larger distance from the coast.
The methodology can easily be applied to other datasets, and the resulting climatology can be used to complement the meteorological input for turbine load assessment models. Furthermore, the methodology can serve as a framework for model evaluation, and as a basis for in-depth physical analysis.
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