Articles | Volume 7, issue 2
https://doi.org/10.5194/wes-7-487-2022
https://doi.org/10.5194/wes-7-487-2022
Research article
 | 
08 Mar 2022
Research article |  | 08 Mar 2022

Can reanalysis products outperform mesoscale numerical weather prediction models in modeling the wind resource in simple terrain?

Vincent Pronk, Nicola Bodini, Mike Optis, Julie K. Lundquist, Patrick Moriarty, Caroline Draxl, Avi Purkayastha, and Ethan Young

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Cited articles

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Archer, C., Simão, H., Kempton, W., Powell, W. B., and Dvorak, M.: The challenge of integrating offshore wind power in the US electric grid. Part I: Wind forecast error, Renew. Energ., 103, 346–360, https://doi.org/10.1016/j.renene.2016.11.047, 2017. a
Babić, K., Bencetić Klaić, Z., and Večenaj, Ž.: Determining a turbulence averaging time scale by Fourier analysis for the nocturnal boundary layer, Geofizika, 29, 35–51, 2012. a
Bloomfield, H., Shaffrey, L., Hodges, K., and Vidale, P.: A critical assessment of the long-term changes in the wintertime surface Arctic Oscillation and Northern Hemisphere storminess in the ERA20C reanalysis, Environ. Res. Lett., 13, 094004, https://doi.org/10.1088/1748-9326/aad5c5, 2018. a
Bodini, N. and Optis, M.: The importance of round-robin validation when assessing machine-learning-based vertical extrapolation of wind speeds, Wind Energ. Sci., 5, 489–501, https://doi.org/10.5194/wes-5-489-2020, 2020. a
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In this paper, we have assessed to which extent mesoscale numerical weather prediction models are more accurate than state-of-the-art reanalysis products in characterizing the wind resource at heights of interest for wind energy. The conclusions of our work will be of primary importance to the wind industry for recommending the best data sources for wind resource modeling.
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