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Boundary-Layer Meteorology

, Volume 66, Issue 1–2, pp 19–47 | Cite as

Estimation of sector roughness lengths and the effect on prediction of the vertical wind speed profile

  • R. J. Barthelmie
  • J. P. Palutikof
  • T. D. Davies
Article

Abstract

An estimate of roughness length is required by some atmospheric models and is also used in the logarithmic profile to determine the increase of wind speed with height under neutral conditions. The choice of technique for determining roughness lengths is generally constrained by the available input data. Here, we compare sets of roughness lengths derived by different methods for the same site and evaluate their impact on the prediction of the vertical wind speed profile.

Wind speed and direction data have been collected at four heights over a three-year period at the North Norfolk Wind Monitoring Site. Wind speed profiles were used to generate sector roughness lengths based on the logarithmic profile formula. This is the only direct way of determining roughness lengths. The simplest and cheapest method is to use maps with published tables giving roughness length estimates for different terrain types. Alternatively Wieringa (1976, 1986) and Beljaars (1987) give formulae for determining roughness lengths from wind speed gusts or standard deviations.

The four sets of estimated roughness lengths vary considerably. They were used to estimate 34 m wind speeds from 12.7 m observations. The profile-derived roughnesses are used simply as a check on the prediction of the wind speed profiles. The terrain-derived roughness lengths give reasonable results. Gust-derived and standard deviation roughnesses both predict wind speeds which are lower than the observed ones. The error is greater in the case of standard deviation roughnesses. If stability corrections are applied in the prediction of the vertical wind speed profile, the results are considerably improved.

Keywords

Wind Speed Neutral Condition Roughness Length Monitoring Site Length Estimate 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Kluwer Academic Publishers 1993

Authors and Affiliations

  • R. J. Barthelmie
    • 1
  • J. P. Palutikof
    • 1
  • T. D. Davies
    • 2
  1. 1.Climatic ResearchUniversity of East AngliaNorwichUK
  2. 2.School of Environmental SciencesUniversity of East AngliaNorwichUK

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