| Summary: | Historically the low peak current portion of the positive cloud-to-ground lightning
stroke data set is discarded since it is dominated by misclassified strokes. This thesis
presents a self-consistent resolution to the problem of analysing lightning stroke data
sets where misclassified strokes are present, without discarding sections of the data
set. It is shown in this thesis that the misclassification problem is present in all the
data sets, but is most prominent in the positive cloud-to-ground data set. The effect
of truncating the positive cloud-to-ground lightning stroke data set from the South
African Lightning Detection Network is that 43� is discarded by truncating the
data set below 10 kA, and 53� is discarded if the data set is truncated below 15 kA.
The statistical distribution of lightning stroke peak current over southern Africa is
computed with the self-consistent method. A new measure of lightning activity is
established that, in addition to activity, describes the energy of strokes. A previously
undocumented inverse relationship between lightning stroke activity and peak current
is presented in this thesis. The self-consistent method is extended to describe
the diurnal variation of intracloud and cloud-to-ground parameters. The presence
of positive cloud-to-ground lightning at the beginning and end of storms is verified
from lightning detection network measurements. The new measure is applied to single
storm days and single storms, and from this measure the charge distribution of
the lightning producing clouds is inferred. To complement the diurnal and temporal
variations, the unique orographic sensitivity of the various lightning polarity type
combinations is presented over the extensive altitude of the Drakensberg mountain
range. The self-consistent method presented in this thesis has direct application in
meteorology; transmission line design and fault investigations; as well as improving
risk analyses by providing the true distribution of lightning stroke peak currents.
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