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dc.contributor.advisorBurger, R.P.
dc.contributor.advisorPiketh, S.J.
dc.contributor.advisorBruyere, C.L.
dc.contributor.authorHavenga, H.
dc.date.accessioned2018-10-08T13:35:03Z
dc.date.available2018-10-08T13:35:03Z
dc.date.issued2018
dc.identifier.urihttps://orcid.org/0000-0002-9238-0295
dc.identifier.urihttp://hdl.handle.net/10394/31246
dc.descriptionMSc (Environmental Sciences), North-West University, Potchefstroom Campus
dc.description.abstractExtreme hail has been among the most costly of natural disasters in South-Africa. Despite the havoc that hail is known to cause, we have limited knowledge about these spatially and temporally rare events and how they are linked from the small scale convective environment to larger scale synoptic circulation. This study attempts to characterize hail events across different atmospheric scales to analyze the trends and cycles of hail favouring conditions. The Weather Research and Forecasting Model (WRF) was used to understand the meso- scale, 2km . 20km, environment by simulating 5 extreme hail events that occurred over the Gauteng province. In the simulations convective parameterization was omitted to simulate convection explicitly, a known improvement to high resolution modeling of severe weather. For each case the simulated thermodynamic and bulk shear ( V ) profile for the major cities in Gauteng was represented at the time the most unstable parcel was simulated. Results indicate that within the simulated thermodynamic profiles CAPE was well represented spatially and temporally across the major cities, while the simulated Showalter index was also within thunderstorm thresholds. The bulk shear was highly variable in the 0 - 1km and 0 -3km layer. The 0 - 6km shear showed the least variability within individual cases, however the 0 - 6km shear between case studies had a high variability compared to CAPE which was well-defined in all the cases. Next the macro- scale synoptic environment, 2000km . 10, 000km, was examined by using a two-step cluster analysis consisting of a k-means and hierarchical cluster analysis. Due to CAPE being well-defined spatiotemporally it was selected as the only convective scale feature for the analysis. Mean sea-level pressure and geoptential at 500 hPa was selected as variables to represent the surface and upper-air at a synoptic scale. The cluster analysis was successful at characterizing synoptic types associated with hail , replicating the annual distribution and patterns of the South-African hail season with reasonable accuracy. Synoptic circulation associated with hail events are accompanied with a surface trough, a ridging anti-cyclone along the east coast and well distributed CAPE over South-Africa. The study concludes by analyzing the trends and cycles of hail related indicators as identified in the meso- and macro- scale. Historical media, weather reports and radiosonde data from the integrated global radiosonde archive (IGRA) was reevaluated in the context of this chapter. Media records indicate a well observed annual variation in reports of hail; peaking in October, November and December while an overall increase in media reports was observed in the last century. The analysis of the seasonal variation for the selected variables from the (IGRA) indicate an increase in atmospheric conditions favouring convective storms during the summer months while an overall increased trend towards a convective favouring atmosphere is observed over the last 40 years. The last analysis examined the relationship between the Southern Oscillation Index (SOI) and hail related synoptic conditions which indicated no synoptic patterns had a statistically significant relationship with the SOI.en_US
dc.language.isoenen_US
dc.publisherNorth-West Universityen_US
dc.subjectHailen_US
dc.subjectweatheren_US
dc.subjectclimateen_US
dc.subjecthighvelden_US
dc.subjectwrfen_US
dc.subjectconvective stormsen_US
dc.titleCharacteristics of hailstorms over the South-African highvelden_US
dc.typeThesisen_US
dc.description.thesistypeMastersen_US
dc.contributor.researchID18002080 - Piketh, Stuart John (Supervisor)
dc.contributor.researchID24062219 - Burger, Roelof Petrus (Supervisor)


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