Optimal application of climate data to the development of design wind speeds

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Thursday, 6 February 2014: 8:30 AM
Room C101 (The Georgia World Congress Center )
Andries C. Kruger, South African Weather Service, Pretoria, South Africa; and X. G. Larsén, A. Goliger, and J. V. Retief
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Accurate extreme wind statistics are important for the design of a safe and economic built environment. The recent revision of the South African Wind Loading Code for engineers (SANS 10160-3:2011) will also include a reassessment of design wind speed statistics. In addition, the Wind Atlas of South Africa (WASA project) focuses, amongst others, on the development of a Regional Extreme Wind Climate (REWC) for South Africa. Wind farms are planned for areas with relatively strong and sustained winds, with wind turbines classed according to their suitability for different wind conditions. The REWC statistics are used during the construction and design phase to make assumptions about the local strong wind climate that the wind turbines will be exposed to, with the local environment and topography as additional input.

The simultaneous development of the REWC and revision of the extreme wind statistics of South Africa created an opportunity to bring together a range of expertise that could contribute to the optimal development of design wind speed information. These include the knowledge of the statistical extraction of extreme wind observations from reanalysis and model data, the quality control and extreme value analysis of measured wind data, the reliability basis of statistical results, and the principles of wind action on structures and its standardization.

Methods with reanalysis and model data

In the case of the reanalysis data, the extreme wind quantiles are estimated using the Annual Maximum Method (AMM), with the Gumbel distribution (Gumbel 1958). The annual maximum winds of the standard conditions are derived using two approaches, namely the application of the geostrophic wind (Larsén and Mann 2009), as well as the surface wind and a generalization procedure (Badger et al. 2010 and Larsén et al. 2012a). Spectral correction is then applied (Larsén et al. 2012b) in order to augment the wind variability in a certain range of frequency, to the modeled time series. The variability is missed out by the smoothing effect embedded in the numerical modeling.

Analysis of measured data

South Africa is, in some regions, dominated by a mixed strong wind climate (Kruger et al. 2010). The methodologies using reanalysis of the data can become inappropriate for shorter time scales, as these were developed for strong wind climates dominated by synoptic-scale systems. Therefore, the extreme value analysis of the measured data serves as the major input in the development of extreme wind statistics. Here, various factors are considered which influence the appropriate analysis of the data, including record lengths, relevant strong wind mechanisms, and types of instrumentation and measuring environments (Kruger 2010).

Reliability analysis

The probability models applied in the reliability analysis should represent both the natural variability of strong winds and the uncertainties resulting from the phenomenological and statistical bases of the models. In converting the probability models into operational design procedures, provision needs to be made in terms of regional characteristics on the one hand and a range of design conditions and situations.

Based on standardized structural design procedures strong wind, at a given location, is typically defined in terms of a 50-year return period quantile. Regional distribution is obtained by mapping the results from the network of observations with sufficient resolution to capture geographical trends, but also sufficiently simplified to be used for operational design (Retief et al 2013). Normalized probability models per strong wind sub-region are used to derive design values from the characteristic value wind map.


Investigations so far addressed the unique limitations of both the estimation of the REWC and design wind speeds with measured or reanalysis/model. For the measured data these mainly refer to the quality of the data, the density of the observation network and the significant spatial and regional tendencies of the strong wind climate. For the reanalysis/model approach in South Africa, these refer to the different strong wind mechanisms, prevalence of thunderstorms as a source of strong winds, and the dominance of mixed strong wind climate conditions in some parts of the country. The optimal consideration of these associated strengths and limitations is required in the integration of the results of different approaches.

Eventually, strong wind probability models for the optimal design of the built environment are developed. These take into consideration the inherent uncertainty associated with the occurrence of strong winds, as well as the simplification required to characteristic and design values for use by practitioners in the built environment on the basis of reliability analysis. The process of integrating REWC probability models into the wind engineering and reliability calibration has been initiated as part of the comprehensive approach taken.


Badger J. et al. 2010: A universal mesoscale to microscale modelling interface tool, European Wind Energy Conference, Warsaw, Poland, 20 – 23 April. Gumbel E.J. 1958. Statistics of Extremes. Columbia University Press, 51 pages. Kruger A.C. (2010) Wind climatology of South Africa relevant to the design of the built environment. PhD dissertation Stellenbosch University http://hdl.handle.net/10019.1/6847 . Kruger A.C., Goliger A., Retief J. and S. Sekele (2010), Strong wind climate zones in South Africa. Wind and Structures, Vol. 13, No 1. Larsén X. G., & Mann, J. 2009. Extreme winds from the NCEP/NCAR reanalysis data. Wind Energy 12, 556 – 573, Doi 10.1002/we.318. Larsén X. G., Badger J., Hahmann A. N. & Mortensen N. G. 2012a. The selective dynamical downscaling method for extreme-wind atlases. Wind Energy, published online, Doi 10.1002/we.1544. Larsén X. G., Ott S., Badger J., Hahmann A. N. & Mann J. 2012b. Recipes for correcting the impact of effective mesoscale resolution on the estimation of extreme winds. J. Appl. Meteorol. Climat., 51: 521 – 533, Doi 10.1175/JAMC-D-11-090.1. Retief J.V., Barnardo-Viljoen C. & Holicky M. 2013. Probabilistic models for design of structures against wind loads. SEMC2013, Cape Town, South Africa. SANS 10160-3:2011. South African National Standard. Basis of structural design and actions for buildings and industrial structures. SABS Standard Division, Pretoria.