Assessing methods to extrapolate the vertical wind-speed profile from surface observations in a city centre during strong winds

Kent, C. W., Grimmond, C. S. B. ORCID: https://orcid.org/0000-0002-3166-9415, Gatey, D. and Barlow, J. F. (2018) Assessing methods to extrapolate the vertical wind-speed profile from surface observations in a city centre during strong winds. Journal of Wind Engineering and Industrial Aerodynamics, 173. pp. 100-111. ISSN 0167-6105 doi: 10.1016/j.jweia.2017.09.007

Abstract/Summary

Knowledge of the vertical wind-speed profile in cities is important for the construction and insurance industries, wind energy predictions, and simulations of pollutant and toxic gas release. Here, five methods to estimate the spatially- and temporally- averaged wind-speed profile are compared in London: the logarithmic wind law (LOG); the Deaves and Harris equilibrium (DHe) and non-equilibrium (DHv) models; an adaptation of the power law (PL) and the Gryning et al. (GR) profile. Using measurements at 2.5 times the average building height, a source area model is used to determine aerodynamic roughness parameters using two morphometric methods, which assume homogeneous and variable roughness-element heights, respectively. Hourly-averaged wind speeds are extrapolated to 200 m above the canopy during strong wind conditions, and compared to wind speeds observed with Doppler lidar. Wind speeds are consistently underestimated if roughness-element height variability is not considered during aerodynamic parameter determination. Considering height variability, the resulting estimations with the DHe and GR profiles are marginally more similar to observations than the DHv profile, which is more accurate than the LOG and PL methods. An exception is in directions with more homogeneous fetch and a gradual reduction in upwind roughness, where the LOG and PL profiles are more appropriate.

Item Type	Article
URI	https://reading-clone.eprints-hosting.org/id/eprint/73656
Item Type	Article
Refereed	Yes
Divisions	Science > School of Mathematical, Physical and Computational Sciences > Department of Meteorology
Publisher	Elsevier
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