Geomagnetic Storms over the Last Solar Cycle : A Superposed Epoch Analysis

This thesis investigates energy transfer between solar wind (SW)-magnetosphere-ionosphere systems during geomagnetic storms that could pose a significant threat to terrestrial technology. A superposed epoch analysis (SEA) of 143 storms from the last solar cycle (1997-2008) was completed. The average geomagnetic storm was investigated via SW data and the global SYM-H index. A new dual trend was observed when comparing storm size to main phase duration which reduced for storms with SYM-H minima <-150 nT, opposite to the findings of Yokoyama and Kamide [1997]. This suggests ring current enhancement dominates recovery, meaning intense storms can occur on the same timescales as weak; important for space weather prediction. One of the first global SEA studies of storm time ionospheric convection using HF SuperDARN radars and map potential technique was completed. Latitude-Time-Velocity plots were developed to best observe the average convection response to storm driving compared to quiet periods and Gillies et al. [2011]. A case study was presented comparing the SEA results to two recent storms, showing remarkable agreement, suggesting the SEA average convection could be used in future predictions. An SEA of global UV auroral images from the IMAGE and Polar spacecraft produced expected auroral responses to geomagnetic storm driving (e.g. Milan et al. [2009]). Open-closed field line boundaries, identified using the method of Boakes et al. [2008], were compared to convection reversal boundaries derived from the SuperDARN analysis. Consistent statistical boundary o_sets suggested a small 'viscous-like' interaction between the SW and magnetosphere was present, estimated to produce an additional ∽4-11 kV potential during quiet and storm periods; an important, small addition to the usual reconnection driven convection. These studies increase our understanding of storm time SW-magnetosphere-ionosphere coupling, raising interesting questions for future work. The combination of datasets makes this one of the largest statistical studies of storms