Substorm onset identification using neural networks and Pi2 pulsations

Sutcliffe, P.

doi:10.1007/s00585-997-1257-x

Cited by 21 publications

(12 citation statements)

References 12 publications

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“…Previous work to automatically and routinely identify substorm onset has largely concentrated on ground‐based magnetometer observations of the impulsive Pi1 and Pi2 ultralow frequency (ULF) magnetic waves observed at substorm onset [ Jacobs et al ., ] that are highly correlated with auroral intensity [ Rae et al ., ] and may be used as a proxy for auroral substorm onset [ Sakurai and Saito , ]. Sutcliffe [] trained a neural network to determine substorm onsets by identifying Pi2 pulsations at low‐latitude ground‐based magnetometer stations. Similarly, Nose et al .…”

Section: Introductionmentioning

confidence: 99%

“…[] used a Meyer wavelet to automatically identify Pi2 pulsations at low‐latitude ground‐based magnetometer stations. While both Sutcliffe [] and Nose et al . [] were able to routinely identify Pi2 pulsations, the delay between auroral zone ULF waves and low‐latitude ULF waves can be on the order of minutes, introducing additional timing uncertainties when observing ULF waves away from the onset location.…”

Section: Introductionmentioning

confidence: 99%

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Automated determination of auroral breakup during the substorm expansion phase using all‐sky imager data

et al. 2014

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This paper describes a novel method for quantitatively and routinely identifying auroral breakup following substorm onset using the Time History of Events and Macroscale Interactions during Substorms all-sky imagers. Substorm onset is characterized by a brightening of the aurora that is followed by auroral poleward expansion and auroral breakup. This breakup can be identified by a sharp increase in the auroral intensity i(t) and the time derivative of auroral intensity i(t). Utilizing both i(t) and i'(t), we have developed an algorithm for identifying the time interval and spatial location of auroral breakup during the substorm expansion phase based solely on quantifiable characteristics of the optical auroral emissions. We compare the time interval determined by the algorithm to independently identified auroral onset times from three previously published studies. In each case the time interval determined by the algorithm is within error of the onset independently identified by the prior studies. We further show the utility of the algorithm by comparing the breakup intervals determined using the automated algorithm to an independent list of substorm onset times. We demonstrate that 50% of the breakup intervals characterized by the algorithm are within the uncertainty of the times identified in the list. The quantitative description and routine identification of an interval of auroral brightening during the substorm expansion phase provides a foundation for unbiased statistical analysis of the aurora and to probe the physics of the auroral substorm and identify the processes leading to auroral substorm onset.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Automated determination of auroral breakup during the substorm expansion phase using all‐sky imager data

et al. 2014

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“…For example, Pi2 pulsations recorded at low latitudes are regarded to be one of the clearest indicators of magnetospheric substorm onsets and intensifications [ Saito et al , ]. Consequently, a number of researchers have developed automated methods of identifying substorm onsets using Pi2 pulsations [ Sutcliffe , ; Nosé et al , ].…”

Section: Geomagnetic Pulsationsmentioning

confidence: 99%

The role of SANSA's geomagnetic observation network in space weather monitoring: A review

2015

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Geomagnetic observations play a crucial role in the monitoring of space weather events. In a modern society relying on the efficient functioning of its technology network such observations are important in order to determine the potential hazard for activities and infrastructure. Until recently, it was the perception that geomagnetic storms had no or very little adverse effect on radio communication and electric power infrastructure at middle-and low-latitude regions like southern Africa. The 2003 Halloween storm changed this perception. In this paper we discuss the role of the geomagnetic observation network operated by the South African National Space Agency (SANSA) in space weather monitoring. The primary objective is to describe the geomagnetic data sets available to characterize and monitor the various types of solar-driven disturbances, with the aim to better understand the physics of these processes in the near-Earth space environment and to provide relevant space weather monitoring and prediction.

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“…Development of a procedure to determine Pi2 onset automatically and accurately would be desirable. Automatic determination of Pi2 onset has been carried out by employing various data adaptive filters such as wavelet analysis [ Nose' et al , 1998; Tsunezawa et al , 1999] and an artificial neural network (ANN) [ Sutcliffe , 1997]. Although these methods are useful for selecting Pi2 onsets, the precision of the determined onset time does not meet our requirement, for example.…”

Section: Introductionmentioning

confidence: 99%

“…In addition, the threshold for selecting the Pi2 pulsation tends to be arbitrary, which affects the estimated onset time. The approach using an ANN mitigates this problem of a reasonable choice of thresholds but still suffers from insufficient resolution in estimating the onset time because the lower‐frequency components of the amplitude spectrum are used as input data in the ANN [ Sutcliffe , 1997].…”

Section: Introductionmentioning

confidence: 99%

Pi2 onset time determination with information criterion

et al. 2002

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[1] The present study proposes a new method to identify ground Pi2 onsets. The Pi2 wave is usually examined by applying a linear band-pass filter or something equivalent, such as wavelet convolution, to ground magnetometer data, and its onset is determined based on the amplitude of the extracted wave component. The proposed method is distinguished from such conventional methods in terms of the following points. First the proposed method does not filter or smooth data, rather it analyzes the original data. Second the proposed method determines the Pi2 onset so that the intervals before and after the onset can be optimally described by different time series models. The optimal partition is determined by minimizing the Akaike information criterion (AIC). The time series models adopted are general enough to apply to any nonstationary time series irrespective of its waveform, frequency, amplitude, and signal-to-noise ratio. As a result, the proposed procedure is free from the choice of a threshold and is therefore objective. The procedure is applied to simulation data, and it is confirmed that the procedure can determine the onset with the precision of the data's time resolution. An application to magnetic field data from 210°magnetic meridian (MM) stations suggests that our procedure can be used for examining the latitudinal and longitudinal dependence of the onset time. The proposed procedure is expected to be useful for examining issues that have been difficult to address with conventional methods because of the precision required for determining Pi2 onset.

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Substorm onset identification using neural networks and Pi2 pulsations

Cited by 21 publications

References 12 publications

Automated determination of auroral breakup during the substorm expansion phase using all‐sky imager data

Automated determination of auroral breakup during the substorm expansion phase using all‐sky imager data

The role of SANSA's geomagnetic observation network in space weather monitoring: A review

Pi2 onset time determination with information criterion

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