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Webinar: HAO Colloqium: ‘Data analytics’ approaches to space weather

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Wednesday, 04 September 2019, 4:00

Wednesday, September 4, 2019. 4:00PM Eastern Time. Webinar: HAO Colloqium: ‘Data analytics’ approaches to space weather. Sandra Chapman, University Of Warwick. Sponsored by NCAR|UCAR. More information here.


The plasma and magnetic field of earth’s near-space environment is highly dynamic, with its own space weather. 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.


Where there are multiple observations such as the SuperMAG collated 100+ magnetometer stations in the auroral region we can test 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’ substorm current system.


Where there are single point, long term observations we can quantify the space climate effects of the variability between each unique solar cycle.  Over the last five solar cycles we have parameters characterizing solar wind driving and magnetospheric response. These vary with the different activity levels of each solar cycle but we found certain properties of the statistical distribution are reproducible from one solar maximum to the next. Observations of the past heliospheric climate may assist prediction of that of the next solar cycle. Over the last 14 cycles we have less well resolved geomagnetic indices and we can attempt to use these to quantify how the likelihood of super-storms depends on solar cycle modulated activity and to set the Carrington event in context.


Quantifying how the frequency and intensity of large, rare events is changing in time is a generic challenge. A topical question is whether heatwaves are becoming more intense and more frequent. The potential for addressing this question solely from the data will be discussed using a new analysis of earth surface temperature timeseries.

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