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Journal article

Extrapolating Satellite Winds to Turbine Operating Heights

From

Department of Wind Energy, Technical University of Denmark1

Meteorology & Remote Sensing, Department of Wind Energy, Technical University of Denmark2

Resource Assessment Modelling, Department of Wind Energy, Technical University of Denmark3

Laboratoire d’Études en Géophysique et Océanographie Spatiales4

Ocean wind retrievals from satellite sensors are typically performed for the standard level of 10 m. This restricts their full exploitation for wind energy planning, which requires wind information at much higher levels where wind turbines operate. A new method is presented for the vertical extrapolation of satellitebased wind maps.

Winds near the sea surface are obtained from satellite data and used together with an adaptation of the Monin–Obukhov similarity theory to estimate the wind speed at higher levels. The thermal stratification of the atmosphere is taken into account through a long-term stability correction that is based on numerical weather prediction (NWP) model outputs.

The effect of the long-term stability correction on the wind profile is significant. The method is applied to Envisat Advanced Synthetic Aperture Radar scenes acquired over the south Baltic Sea. This leads to maps of the long-term stability correction and wind speed at a height of 100 m with a spatial resolution of 0.028.

Calculations of the corresponding wind power density and Weibull parameters are shown. Comparisons with mast observations reveal that NWP model outputs can correct successfully for long-term stability effects and also, to some extent, for the limited number of satellite samples. The satellite-based and NWP-simulated wind profiles are almost equally accurate with respect to those from the mast.

However, the satellite-based maps have a higher spatial resolution, which is particularly important in nearshore areas where most offshore wind farms are built.

Language: English
Publisher: American Meteorological Society
Year: 2016
Pages: 975-991
ISSN: 15588432 and 15588424
Types: Journal article
DOI: 10.1175/JAMC-D-15-0197.1
ORCIDs: Badger, Merete , Pena Diaz, Alfredo , Hahmann, Andrea N. and Hasager, Charlotte Bay

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