Abstract
Retrospective analysis of severe convective storms and associated lightning is useful for forecasting these extreme events. However, until recently it has been difficult to obtain sufficient datasets for long-term analysis. The use of lightning as a proxy for severe convection has become feasible in the past decade as global and regional lightning detection datasets have become of sufficient longevity for climatological investigation. A twelve-year (2001-2012) climatological analysis of positive and negative cloud-to-ground discharge (+/-CGD) lightning in New Zealand was completed using data from a network of sensors maintained by the New Zealand Meteorological Service. A spatio-temporal lightning analysis in relation to diurnal and seasonal scales, Kidson’s synoptic types and variability introduced by SAM and ENSO was carried out. Results show that -CGD are largely dominant, with clear inter-annual and seasonal variability and topography shaping their spatial variability. Western areas, especially the West Coast of the South Island, experience the highest +/-CGD. They primarily occur under trough situations in response to orographic triggers and can occur at any time of the day or year, although more frequently during spring and autumn months (Sep-Dec, Mar-Jun). Eastern areas are most likely to experience lightning activity during summer (Nov-Feb), have a strong diurnal pattern and are linked to interactions between post-frontal unstable southwesterly flow regimes and smaller scale sea breeze convergence mechanisms. The central North Island also has a strong diurnal pattern, with lightning most likely to occur during the afternoon in the summer months (Dec-Jan). These primarily occur during blocking synoptic conditions, where slack air gradients and strong daytime heating produce small-scale wind interactions, local convection and the production of severe convective storm cells. These results have assisted in the construction of a lightning climatology, while subsequent investigation of the atmospheric processes involved is underway using a meso-scale modeling system (WRF-ARW).