Analysis of global geomagnetic variability

Anh, Vo; Yu, Zu-Guo; Wanliss, J. A.

doi:10.5194/npg-14-701-2007

Cited by 23 publications

(14 citation statements)

References 25 publications

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“…The origin of such an intermittent and multifractal character has been interpreted and modeled in terms of dynamical changes and turbulent dynamics (Wanliss et al, 2005;Anh et al, 2007;Yu et al, 2010;Sharma and Veeramani, 2011).…”

Section: Introductionmentioning

confidence: 99%

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Latitudinal variation of stochastic properties of the geomagnetic field

Wanliss

Shiokawa

Yumoto

2014

Nonlin. Processes Geophys.

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Abstract. We explore the stochastic fractal qualities of the geomagnetic field from 210 mm ground-based magnetometers during quiet and active magnetospheric conditions. We search through 10 yr of these data to find events that qualify as quiet intervals, defined by Kp ≤ 1 for 1440 consecutive minutes. Similarly, active intervals require Kp ≥ 4 for 1440 consecutive minutes. The total for quiet intervals is ∼ 4.3 × 10 6 and 2 × 10 8 min for active data points. With this large number of data we characterize changes in the nonlinear statistics of the geomagnetic field via measurements of a fractal scaling. A clear difference in statistical behavior during quiet and active intervals is implied through analysis of the scaling exponents; active intervals generally have larger values of scaling exponents. This suggests that although 210 mm data appear monofractal on shorter timescales, the scaling changes, with overall variability are more likely described as a multifractional Brownian motion. We also find that low latitudes have scaling exponents that are consistently larger than for high latitudes.

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Section: Introductionmentioning

confidence: 99%

“…This is especially important since temporal fluctuations of the geomagnetic field and, more importantly, their nonlinear fractal or multifractal statistical fluctuations depend on geographic location and time (Anh et al, 2007;Yu et al, 2010).…”

Section: Introductionmentioning

confidence: 99%

Latitudinal variation of stochastic properties of the geomagnetic field

Wanliss

Shiokawa

Yumoto

2014

Nonlin. Processes Geophys.

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“…The Special Issue is introduced by the Guest Editors -B. Malamud, H. W. Rust and P. Yiou -and contains 14 research papers on the theory of extreme events and its applications to many phenomena in the geosciences. Theoretical developments are covered by Abaimov et al (2007); ; Blender et al (2008); Schölzel and Friederichs (2008) and Serinaldi (2009), while the applications cover the fields of meteorology and climate dynamics (Vannitsem and Naveau, 2007;Vrac et al, 2007a;Ghil et al, 2008b;Taricco et al, 2008;Yiou et al, 2008), seismology (Soloviev, 2008;Narteau et al, 2008) and geomagnetism (Anh et al, 2007). Many other peer-reviewed papers were published over the duration of the project and data sets and other unrefereed products can be found on the web sites of the participating institutions.…”

Section: Introductionmentioning

confidence: 99%

Extreme events: dynamics, statistics and prediction

Ghil

Yiou

Hallegatte

et al. 2011

Nonlin. Processes Geophys.

228

167

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Abstract. We review work on extreme events, their causes and consequences, by a group of European and American researchers involved in a three-year project on these topics. The review covers theoretical aspects of time series analysis and of extreme value theory, as well as of the deterministic modeling of extreme events, via continuous and discrete dynamic models. The applications include climatic, seismic and socio-economic events, along with their prediction.Two important results refer to (i) the complementarity of spectral analysis of a time series in terms of the continuous and the discrete part of its power spectrum; and (ii) the need for coupled modeling of natural and socio-economic systems. Both these results have implications for the study and prediction of natural hazards and their human impacts.

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“…[e.g., Schertzer and Lovejoy, 1987;Schmitt et al, 1992;Tessier et al, 1993Tessier et al, , 1996Olsson and Niemzcynowicz, 1996;Harris et al, 1996;Lovejoy et al, 1996Lovejoy et al, , 2008Deidda, 2000;Lilley et al, 2006;Kantelhardt et al, 2006;Veneziano et al, 2006;Venugopal et al, 2006;Lovejoy and Schertzer, 2006, 2010a, 2010bGarcia-Marin et al, 2008;Serinaldi, 2010;. In particular, Lui [2002] Anh et al [2007Anh et al [ , 2008 and Yu et al [2010Yu et al [ , 2012 used MFA and fractional stochastic differential equations to study the AE and geomagnetic field data. This paper aims to identify significant similarities in the scaling properties of geomagnetic and solar indices.…”

Section: Journal Of Geophysical Research: Space Physicsmentioning

confidence: 99%

Underlying scaling relationships between solar activity and geomagnetic activity revealed by multifractal analyses

Anh

Eastes

2014

JGR Space Physics

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This paper identifies some scaling relationships between solar activity and geomagnetic activity. We examine the scaling properties of hourly data for two geomagnetic indices (a p and AE), two solar indices (solar X-rays X l and solar flux F10.7), and two inner heliospheric indices (ion density Ni and flow speed Vs) over the period 1995-2001 by the universal multifractal approach and the traditional multifractal analysis. We found that the universal multifractal model (UMM) provides a good fit to the empirical K(q) and (q) curves of these time series. The estimated values of the Lévy index in the UMM indicate that multifractality exists in the time series for a p , AE, X l , and Ni, while those for F10.7 and Vs are monofractal.The estimated values of the nonconservation parameter H of this model confirm that these time series are conservative which indicate that the mean value of the process is constant for varying resolution. Additionally, the multifractal K(q) and (q) curves, and the estimated values of the sparseness parameter C 1 of the UMM indicate that there are three pairs of indices displaying similar scaling properties, namely a p and X l , AE and Ni, and F10.7 and Vs. The similarity in the scaling properties of pairs (a p , X l ) and (AE, Ni) suggests that a p and X l , AE and Ni are better correlated-in terms of scaling-than previous thought, respectively. But our results still cannot be used to advance forecasting of a p and AE by X l and Ni, respectively, due to some reasons.

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Analysis of global geomagnetic variability

Cited by 23 publications

References 25 publications

Latitudinal variation of stochastic properties of the geomagnetic field

Latitudinal variation of stochastic properties of the geomagnetic field

Extreme events: dynamics, statistics and prediction

Underlying scaling relationships between solar activity and geomagnetic activity revealed by multifractal analyses

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