Quantifying sources of uncertainty in reanalysis derived wind speed

Stephen Rose, Jay Apt

Research output: Contribution to journalArticlepeer-review

17 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)157-165
Number of pages9
JournalRenewable Energy
Volume94
DOIs
StatePublished - Aug 1 2016

Bibliographical note

Funding Information:
This work was supported by grants to the RenewElec project by the Richard King Mellon Foundation ( 5562 ) and the Doris Duke Charitable Trust . This research was also supported in part by the Climate and Energy Decision Making (CEDM) center, created through a cooperative agreement between the National Science Foundation ( SES-0949710 ) and Carnegie Mellon University . We are grateful to George Young and John Zack for their meteorological insights and advice, and Fallaw Sowell and Stephen Karolyi for their help with mixed-effects models, Bradley Stevens for providing the measured data, and Bob Dattore for providing the reanalysis data.

Publisher Copyright:
© 2016 Elsevier Ltd.

Keywords

  • CFS reanalysis
  • Linear mixed-effect model
  • Wind integration

Fingerprint

Dive into the research topics of 'Quantifying sources of uncertainty in reanalysis derived wind speed'. Together they form a unique fingerprint.

Cite this