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dc.contributor.advisorPegram, G. G. S.
dc.contributor.authorWesson, Stephen Malcolm
dc.date.accessioned2011-06-23T10:27:36Z
dc.date.available2011-06-23T10:27:36Z
dc.date.created2005
dc.date.issued2005
dc.identifier.urihttp://hdl.handle.net/10413/3069
dc.descriptionThesis (M.Sc.Eng.)-University of KwaZulu-Natal, Durban, 2005.en_US
dc.description.abstractWeather radar provides a detailed spatial representation of rainfall over a large area and in a real time basis. It has proven to be a valuable tool for hydrologists, agriculturalists and organisations that require accurate and real time information of rainfall and overcomes many of the disadvantages associated with the traditional raingauge estimate. However one of the shortcomings of the rainfall estimates supplied by weather radars is that there are quality problems associated with radar rainfall images that include ground clutter, beam blocking and anomalous propagation to name a few. To obtain the best rainfall estimate possible, techniques for removing ground clutter (non-meteorological echoes that influence radar data quality) on two-dimensional (20) and three-dimensional (3D) radar rainfall image data sets were developed in this study. The chosen method for estimating the "true" values behind the contaminated data was Kriging, which is considered to be the optimal technique for the spatial prediction of Gaussian data. Kriging has various advantages and disadvantages, which need to be taken into consideration in this type of application. For the radar rainfall images to be repaired in real time a computationally fast and efficient method of estimating the missing contaminated data was needed. This is achieved by exploiting the various properties associated with Kriging. In South Africa, radar volume scan data are currently only available on one-kilometre horizontal grids at one-kilometre intervals above the earth's surface. This may not be an accurate representation of the rainfall that actually reaches ground level. To provide an estimate of the "true" rainfall reaching the earth's surface, an algorithm has been developed that extrapolates the radar data down to ground level. The extrapolation is carried out using a combination of 3D Universal and Ordinary Kriging which is preceded by a rainfall classification algorithm developed and calibrated in this study. The techniques proposed for ground clutter infilling and the extrapolation of radar data to ground level have been tested for their effectiveness and efficiency on a wide range of selected rainfall events and indicate that the methodology is practically useful. The South African Weather Service (SAWS) has recently installed the software to "cleanse" the radar data as it is received in real time.en_US
dc.language.isoenen_US
dc.subjectTheses--Civil engineering.en_US
dc.subjectRadar meteorology.
dc.subjectKriging.
dc.titleRadar reflectivity infilling techniques.en_US
dc.typeThesisen_US


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