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Quantifying sources of uncertainty in reanalysis derived wind speed

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  • Rose, Stephen
  • Apt, Jay

Abstract

Reanalysis data are attractive for wind-power studies because they can offer wind speed data for large areas and long time periods and in locations where historical data are not available. However, reanalysis-predicted wind speeds can have significant uncertainties and biases relative to measured wind speeds. In this work we develop a model of the bias and uncertainty of CFS reanalysis wind speed than can be used to correct the data and identify sources of error. We find the CFS reanalysis data underestimate wind speeds at high elevations, at high measurement heights, and in unstable atmospheric conditions. For example, at a site with an elevation of 500 m and hub height of 80 m, a CFS reanalysis wind speed of 8 m/s is 0.2 m/s higher to 1.3 m/s lower than the measured wind speed. We also find a seasonal bias that correlates with surface roughness length used by the reanalysis model during the spring season. The corrections we propose reduce the average bias of reanalysis wind speed extrapolated to hub height to nearly zero, an improvement of 0.3–0.9 m/s. These corrections also reduce the RMS error by 0.1–0.4 m/s, a small improvement compared to the uncorrected RMS errors of 1.5–2.4 m/s.

Suggested Citation

  • Rose, Stephen & Apt, Jay, 2016. "Quantifying sources of uncertainty in reanalysis derived wind speed," Renewable Energy, Elsevier, vol. 94(C), pages 157-165.
    • Handle: RePEc:eee:renene:v:94:y:2016:i:c:p:157-165
    DOI: 10.1016/j.renene.2016.03.028
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    References listed on IDEAS

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    2. José V. P. Miguel & Eliane A. Fadigas & Ildo L. Sauer, 2019. "The Influence of the Wind Measurement Campaign Duration on a Measure-Correlate-Predict (MCP)-Based Wind Resource Assessment," Energies, MDPI, vol. 12(19), pages 1-15, September.
    3. Penalba, Markel & Guo, Chao & Zarketa-Astigarraga, Ander & Cervelli, Giulia & Giorgi, Giuseppe & Robertson, Bryson, 2023. "Bias correction techniques for uncertainty reduction of long-term metocean data for ocean renewable energy systems," Renewable Energy, Elsevier, vol. 219(P1).
    4. Katikas, Loukas & Dimitriadis, Panayiotis & Koutsoyiannis, Demetris & Kontos, Themistoklis & Kyriakidis, Phaedon, 2021. "A stochastic simulation scheme for the long-term persistence, heavy-tailed and double periodic behavior of observational and reanalysis wind time-series," Applied Energy, Elsevier, vol. 295(C).
    5. Handschy, Mark A. & Rose, Stephen & Apt, Jay, 2017. "Is it always windy somewhere? Occurrence of low-wind-power events over large areas," Renewable Energy, Elsevier, vol. 101(C), pages 1124-1130.

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