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2021-10-12, 15:00: Prof. Sandra Chapman (University of Warwick)

Prof. Sandra Chapman

University of Warwick, UK

Quantifying space climate as the statistics of space weather: networks and burst distributions


Space weather and solar terrestrial physics observations are increasingly becoming a data analytics challenge and there are common approaches with other fields such as earth climate observations. Whilst focussing on specific applications, this talk aims to present generic methodology for inhomogeneous ‘real world’ data.

Multiple observations of spatially and temporally varying fields: spatially irregular observations such as the SuperMAG collated 100+ ground based magnetometer stations in the auroral region can be tested for spatio-temporal patterns of correlation using dynamical networks. Whilst networks are in widespread use in the data analytics of societal and commercial data, there are additional challenges in their application to physical time series. We are able to construct dynamical networks direct from SuperMAG.   The transient dynamics of the auroral current system is captured by the spatio-temporal patterns of correlation between the magnetometer time-series and can be quantified by (time dependent) network parameters. Cross-correlation lags can be used to construct directed networks which give directions and timescales for propagation. This offers the possibility of characterizing detailed spatio-temporal pattern by a few parameters, so that many events can then be compared with each other and with differing theoretical predictions of the ‘typical’ geomagnetic substorm current system.

Single long-term observations over multiple solar cycles: Each solar cycle, and its effect on space weather, is unique in amplitude and duration. High fidelity space age observations of solar wind parameters and geomagnetic indices based on ground magnetometer stations are available for the last 4-5 cycles. The statistical distribution of these data are found to track the cycle of activity such that the underlying functional form of the distribution tail repeats from one cycle maximum to the next. Distribution quantiles tend to track sunspot number. Space weather events can be identified in these timeseries as bursts (time above a threshold). We will discuss how the ‘repeatability’ in the statistics of the observations may constrain burst statistics through crossing theory. Over the last 14 cycles we have geomagnetic indices such as the aa index which are poorly resolved in amplitude but nevertheless contain information on the likelihood of occurrence of extreme space weather events, and we discuss how this can be quantified, setting the Carrington event in the context of extreme events that have occurred over the last 150 years.