Articles | Volume 13, issue 10
https://doi.org/10.5194/gmd-13-5079-2020
https://doi.org/10.5194/gmd-13-5079-2020
Model experiment description paper
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27 Oct 2020
Model experiment description paper | Highlight paper |  | 27 Oct 2020

The Making of the New European Wind Atlas – Part 2: Production and evaluation

Martin Dörenkämper, Bjarke T. Olsen, Björn Witha, Andrea N. Hahmann, Neil N. Davis, Jordi Barcons, Yasemin Ezber, Elena García-Bustamante, J. Fidel González-Rouco, Jorge Navarro, Mariano Sastre-Marugán, Tija Sīle, Wilke Trei, Mark Žagar, Jake Badger, Julia Gottschall, Javier Sanz Rodrigo, and Jakob Mann

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Cited articles

Anderson, J. R., Hardy, E. E., Roach, J. T., and Witmer, R. E.: A land use and land cover classification system for use with remote sensor data, Tech. rep., United States Geological Service, available at: https://pubs.usgs.gov/pp/0964/report.pdf (last access: 20 October 2020), 1976. a
Badger, J., Frank, H., Hahmann, A. N., and Giebel, G.: Wind-climate estimation based on mesoscale and microscale modeling: Statistical-dynamical downscaling for wind energy applications, J. Appl. Meteorol. Clim., 53, 1901–1919, https://doi.org/10.1175/JAMC-D-13-0147.1, 2014. a, b
Badger, J., Sempreviva, A., Söderberg, S., Costa, P., Simoes, T., Estanqueiro, A., Gottschall, J., Dörenkämper, M., Callies, D., Navarro Montesinos, J., González Rouco, J., Garcia Bustamante, E., and Bauwens, I.: Report on Link to Global Wind Atlas and National Wind Atlases – Deliverable D4.7, Technical Report, 37 pages 4.7, Technical University of Denmark, https://doi.org/10.5281/zenodo.3243193, 2018. a
Barcons, J., Avila, M., and Folch, A.: Diurnal cycle RANS simulations applied to wind resource assessment, Wind Energy, 22, 269–282, https://doi.org/10.1002/we.2283, 2019. a
Copernicus Land Monitoring Service: CORINE Land Cover, available at: https://land.copernicus.eu/pan-european/corine-land-cover, last access: 22 October 2019. a, b
Short summary
This is the second of two papers that document the creation of the New European Wind Atlas (NEWA). The paper includes a detailed description of the technical and practical aspects that went into running the mesoscale simulations and the microscale downscaling for generating the climatology. A comprehensive evaluation of each component of the NEWA model chain is presented using observations from a large set of tall masts located all over Europe.
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