Articles | Volume 13, issue 10
https://doi.org/10.5194/gmd-13-5053-2020
https://doi.org/10.5194/gmd-13-5053-2020
Methods for assessment of models
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27 Oct 2020
Methods for assessment of models | Highlight paper |  | 27 Oct 2020

The making of the New European Wind Atlas – Part 1: Model sensitivity

Andrea N. Hahmann, Tija Sīle, Björn Witha, Neil N. Davis, Martin Dörenkämper, Yasemin Ezber, Elena García-Bustamante, J. Fidel González-Rouco, Jorge Navarro, Bjarke T. Olsen, and Stefan Söderberg

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Revised manuscript not accepted
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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, availabl e at: https://pubs.usgs.gov/pp/0964/report.pdf (last access: 18 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
Benjamin, S. G., Grell, G. A., Brown, J. M., and Smirnova, T. G.: Mesoscale weather prediction with the RUC hybrid isentropic-terrain-following coordinate model, Mon. Weather Rev., 132, 473–494, https://doi.org/10.1175/1520-0493(2004)132<0473:MWPWTR>2.0.CO;2, 2004. a
Bosveld, F. C.: Cabauw In-situ Observational Program 2000 – Now: Instruments, Calibrations and Set-up, Tech. rep., KNMI, available at: http://projects.knmi.nl/cabauw/insitu/observations/documentation/Cabauw_TR/Cabauw_TR.pdf (last access: 28 June 2018), 2019. a
Chávez-Arroyo, R., Lozano-Galiana, S., Sanz-Rodrigo, J., and Probst, O.: Statistical-dynamical downscaling of wind fields using self-organizing maps, Appl. Therm. Eng., 75, 1201–1209, https://doi.org/10.1016/j.applthermaleng.2014.03.002, 2015. a
Short summary
Wind energy resource assessment routinely uses numerical weather prediction model output. We describe the evaluation procedures used for picking the suitable blend of model setup and parameterizations for simulating European wind climatology with the WRF model. We assess the simulated winds against tall mast measurements using a suite of metrics, including the Earth Mover's Distance, which diagnoses the performance of each ensemble member using the full wind speed and direction distribution.
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