Time Series Analysis Applied to Geomagnetic Indices
Abstract:
Geomagnetic indices, based on magnetic field observations at the Earth’s surface, provide almost continuous monitoring of Earth’s magnetospheric and ionospheric activity. AE and DST are two such indices designed to measure the evolution and intensity of the auroral electrojets and the ring current, respectively. The SuperMAG collaboration have made new versions of these indices available. The new indices are based on a larger number of magnetometer stations than the original indices and are produced with higher cadence observations than DST.
Time series analysis techniques are applied to decades of geomagnetic index observations. Comparison of empirical distributions demonstrates that important differences exist between the original indices and their SuperMAG counterparts. Burst statistics are inferred from empirical distributions of the indices and are seen to exhibit solar cycle ordering. Extreme value analysis is used to estimate the recurrence likelihood of extreme geomagnetic storms. Space weather storms can cause technological problems including electrical grid damage and satellite system disruption. These results may combine to offer important constraints in the quantification of overall space weather risk and highlight the effect of improved spatial resolution in geomagnetic indices.
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Last Updated: 17th October 2022 by Sophie Murray
2022-06-21 Aisling Bergin (University of Warwick)
Time Series Analysis Applied to Geomagnetic Indices
Abstract:
Geomagnetic indices, based on magnetic field observations at the Earth’s surface, provide almost continuous monitoring of Earth’s magnetospheric and ionospheric activity. AE and DST are two such indices designed to measure the evolution and intensity of the auroral electrojets and the ring current, respectively. The SuperMAG collaboration have made new versions of these indices available. The new indices are based on a larger number of magnetometer stations than the original indices and are produced with higher cadence observations than DST.
Time series analysis techniques are applied to decades of geomagnetic index observations. Comparison of empirical distributions demonstrates that important differences exist between the original indices and their SuperMAG counterparts. Burst statistics are inferred from empirical distributions of the indices and are seen to exhibit solar cycle ordering. Extreme value analysis is used to estimate the recurrence likelihood of extreme geomagnetic storms. Space weather storms can cause technological problems including electrical grid damage and satellite system disruption. These results may combine to offer important constraints in the quantification of overall space weather risk and highlight the effect of improved spatial resolution in geomagnetic indices.
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