Articles | Volume 6, issue 6
Wind Energ. Sci., 6, 1379–1400, 2021
https://doi.org/10.5194/wes-6-1379-2021
Wind Energ. Sci., 6, 1379–1400, 2021
https://doi.org/10.5194/wes-6-1379-2021

Research article 04 Nov 2021

Research article | 04 Nov 2021

Satellite-based estimation of roughness lengths and displacement heights for wind resource modelling

Rogier Floors et al.

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Cited articles

Badger, J., Hahmann, A., Larsen, X. G., Badger, M., Kelly, M., Davis, N., Olsen, B. T., and Mortensen, N. G.: The Global Wind Atlas, Tech. rep., DTU Wind Energy, Roskilde, Denmark, available at: https://energiforskning.dk/sites/energiforskning.dk/files/slutrapporter/gwa_64011-0347_finalreport.pdf​​​​​​​ (last access: 26 October 2021), 2015. a, b
Bergström, H., Alfredsson, H., Arnqvist, J., Carlén, I., Dellwik, E., Fransson, J., Ganander, H., Mohr, M., Segalini, A., Söderberg, S., Bergström, H., Alfredsson, H., Carlén, J., Dellwik, I., Ganander, J., and Mohr, H.: Wind power in forests: Winds and effects on loads, Tech. rep., Uppsala University, Stockholm, Sweden, available at: https://orbit.dtu.dk/en/publications/wind-power-in-forests-winds-and-effects-on-loads​​​​​​​ (last access: 26 October 2021), 2013. a
Blackadar, A. K. and Tennekes, H.: Asymptotic Similarity in Neutral Barotropic Planetary Boundary Layers, J. Atmos. Sci., 25, 1015–1020, https://doi.org/10.1175/1520-0469(1968)025<1015:ASINBP>2.0.CO;2, 1968. a
Boser, B. E., Guyon, I. M., and Vapnik, V. N.: A Training Algorithm for Optimal Margin Classifiers, in: Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, 27–29 July​​​​​​​ 1992, Pittsburg, USA, ACM Press, 144–152, 1992. a
Bottema, M., Klaasen, W., and Hopwood, W.: Landscape Roughness Parameters for Sherwood Forest – Validation of Aggregation Models, Bound.-Lay. Meteorol., 89, 317–347, https://doi.org/10.1023/A:1001795509379, 1998. a
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Short summary
Wind turbines are frequently placed in forests. We investigate the potential of using satellites to characterize the land surface for wind flow modelling. Maps of forest properties are generated from satellite data and converted to flow modelling maps. Validation is carried out at 10 sites. Using the novel satellite-based maps leads to lower errors of the power density than land cover databases, which demonstrates the value of using satellite-based land cover maps for flow modelling.