Extreme wind atlases of South Africa from global reanalysis data (original) (raw)
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Strong winds in South Africa: Part 2 Mapping of updated statistics
Journal of the South African Institution of Civil Engineers
Although wind is the most important environmental action on buildings and structures in South Africa, the last comprehensive strong wind analysis was conducted in 1985. The current wind loading code is still based on the strong wind quantiles forthcoming from that analysis. Wind data available for strong wind analysis has increased about five-fold, due to the employment of automatic weather station (AWS) technology by the South African Weather Service. This makes an updated assessment of strong winds in South Africa imperative. Based on the estimation of strong winds as reported in the accompanying paper (see page 29 in this volume), the spatial interpolation of 50-year characteristic strong wind values to provide updated design wind speed maps is reported in this paper. In addition to taking account of short recording periods and the effects of the mixed strong wind climate, the exposure of the weather stations was considered and correction factors applied. Quantile values were adj...
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Strong wind climatic zones in South Africa
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Copyright: 2010 Techno Press. This is the pre-print version of the work. The definitive version is published in Wind and Structures Journal, Vol 31(1), pp 37-55 In this paper South Africa is divided into strong wind climate zones, which indicate the main sources of annual maximum wind gusts. By the analysis of wind gust data of 94 weather stations, which had continuous climate time series of 10 years or longer, six sources, or strong-wind producing mechanisms, could be identified and zoned accordingly. The two primary causes of strong wind gusts are thunderstorm activity and extratropical low pressure systems, which are associated with the passage of cold fronts over the southern African subcontinent. Over the eastern and central interior of South Africa annual maximum wind gusts are usually caused by thunderstorm gust fronts during summer, while in the western and southern interior extratropical cyclones play the most dominant role. Along the coast and adjacent interior annual extr...
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14th Australasian Wind Enginering Society (AWES) Workshop, August 2010 The South African Weather Service, with the support of the Council for Scientific and Industrial Research (CSIR) and the University of Stellenbosch, is in the process of updating the extreme surface wind statistics for South Africa. A previous assessment was done by the CSIR in 1985 when only a limited number of climate stations over South Africa were available, which had sufficiently long time series of continuously recorded high-resolution wind data. Due to the complexity of the South African strong wind climate, which has also been updated, data produced by the different strong wind producing phenomena should be analyzed separately to improve extreme wind predictions. Previous studies exist, one which e.g. distinguished between four extreme wind-generating mechanisms for Australia. Annual extreme wind speeds are generated by different mechanisms, forthcoming from thunderstorm activity and the passages of extra...
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The complexity of the wind climate of Cape Town and its surroundings can be shown by the measurements of specific wind phenomena by weather stations around Table Mountain. It is shown that there are substantial differences between wind speed characteristics affecting various parts of the city. These differences between the wind at the different locations are further complicated by the main strong wind mechanisms prevailing in the region, i.e. north-westerly winds from passing extratropical cyclones, mainly in the austral winter, and southeasterlies from ridging high-pressure systems, mainly in the summer months. This initial investigation is the precursor of a broader study involving wind tunnel modelling, wind measurements and climate modelling, to provide a comprehensive analysis of the spatial variability of strong winds in the Cape Peninsula.
An updated description of the strong-wind climate of South Africa
2011
Wind constitutes the most critical environmental loading affecting the structural design of the built environment in South Africa. Over the years, several failures of buildings and structures due to wind actions have occurred, some of them resulting in loss of human lives, as well as significant financial losses (Goliger and Retief, 2002). These failures could be attributed to various factors e.g. improper design and/or construction, but also inadequate knowledge of the wind action; more specifically the wind characteristics at low elevations at a regional or local scale affecting the design of specific structures. The need for updating the environmental input into the process of determining wind loads for structural design was emphasised during the process of revising the South African Loading Code (Goliger 2007). The sparse distribution of climate stations, mainly located in large cities for the present wind maps (Milford 1985a & b) and the subsequent addition of several decades o...
Mesoscale modeling for the Wind Atlas of South Africa (WASA) project
2015
This document reports on the methods used to create and the results of the two numerical wind atlases developed for the Wind Atlas for South Africa (WASA) project. The wind atlases were created using the KAMM-WAsP method and from the output of climate-type simulations of the Weather, Research and Forecasting (WRF) model, respectively. The report is divided into three main parts. In the first part, we document the method used to run the mesoscale simulations and to generalize the WRF model wind climatologies, which was used for the first time in a wind atlas project. The second part compares the results from the numerical wind atlases (NWA) produced by the KAMM-WAsP with that produced with the WRF method, and verifies the two wind atlases from the two methods against the observed wind atlas (OWA) generated from wind observations from the 10 WASA masts. The KAMM-WAsP method was found to underestimate the generalized mean wind speeds at the sites (mean bias of-8.2% and mean absolute bias of 9.3%). In the WRF-based method there is, on average, a difference of 4.2% (either positive or negative) between the WRF-based NWA results and the corresponding observed values. The combined average across all the sites is an overestimate of 2.1%. The report also documents the variability of the 62 m AGL wind speed at the 10 sites in the seasonal and diurnal time scale and compares it with the WRF-simulated winds.