A probabilistic approach to heterogeneity in space plasmas: the case of magnetic field intensity in solar wind

Consolini, Giuseppe; Bavassano, B.; Michelis, Paola De

doi:10.5194/npg-16-265-2009

Cited by 5 publications

(8 citation statements)

References 40 publications

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“…In other words, depending on the values of κ (we show P κ and P L κ for κ = 3, 10, 20), the kappa and log‐kappa distributions are capable to reproduce the PDF tails which are fatter than the normal or lognormal PDFs. Although other types of parametric PDFs [e.g., Consolini et al , ] or their combinations could also work well, we provide evidence that the considered PDFs are sufficient to model substantial parts of the observed magnetic fluctuations in the variable solar wind in 2008.…”

Section: Probability Distribution Functions For Space Plasmasmentioning

confidence: 78%

“…Although other types of parametric PDFs (e.g. [Consolini et al, 2009]) or their combinations could also work well, we provide evidence that the considered PDFs are sufficient to model substantial part of the observed magnetic fluctuations in the variable solar wind in 2008.…”

Section: Parametric Pdfs For the Magnetic Fieldmentioning

confidence: 89%

“…From these models the normal and lognormal distributions can be obtained by taking the limit

κ \to \infty

. Although other types of model PDFs or their combinations could also describe the peaked and skewed histograms well [ Consolini et al , ], the kappa and logarithmic kappa models allow to study the multiscale changes of histograms in a simple way. Anyhow, for a systematic study of the statistical variability in the solar wind, model PDFs have to be chosen.…”

Section: Discussionmentioning

confidence: 99%

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Probability density functions for the variable solar wind near the solar cycle minimum

et al. 2015

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Unconditional and conditional statistics are used for studying the histograms of magnetic field multiscale fluctuations in the solar wind near the solar cycle minimum in 2008. The unconditional statistics involves the magnetic data during the whole year in 2008. The conditional statistics involves the magnetic field time series split into concatenated subsets of data according to a threshold in dynamic pressure. The threshold separates fast‐stream leading edge compressional and trailing edge uncompressional fluctuations. The histograms obtained from these data sets are associated with both multiscale (B) and small‐scale (δB) magnetic fluctuations, the latter corresponding to time‐delayed differences. It is shown here that, by keeping flexibility but avoiding the unnecessary redundancy in modeling, the histograms can be effectively described by a limited set of theoretical probability distribution functions (PDFs), such as the normal, lognormal, kappa, and log‐kappa functions. In a statistical sense the model PDFs correspond to additive and multiplicative processes exhibiting correlations. It is demonstrated here that the skewed small‐scale histograms inherent in turbulent cascades are better described by the skewed log‐kappa than by the symmetric kappa model. Nevertheless, the observed skewness is rather small, resulting in potential difficulties of estimation of the third‐order moments. This paper also investigates the dependence of the statistical convergence of PDF model parameters, goodness of fit, and skewness on the data sample size. It is shown that the minimum lengths of data intervals required for the robust estimation of parameters is scale, process, and model dependent.

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Section: Probability Distribution Functions For Space Plasmasmentioning

confidence: 78%

Section: Parametric Pdfs For the Magnetic Fieldmentioning

confidence: 89%

“…From these models the normal and lognormal distributions can be obtained by taking the limit

κ \to \infty

Section: Discussionmentioning

confidence: 99%

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Probability density functions for the variable solar wind near the solar cycle minimum

et al. 2015

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“…The presence of a power law with exponent

(Yaglom’s law) for solar wind proton density and temperature is investigated by using two periods of different solar wind conditions: (i) a period of six months from 01 January 1996 to 30 June 1996 (labeled as

), when the Ulysses satellite was out of the solar equatorial plane (HGI Lat

) at a distance of ∼3.6 A.U., still observing high-speed solar wind (

km/s); (ii) an already investigated long-standing period of nearly constant slow solar wind conditions (

km/s) from 01 October 1997 to 31 March 1998 (labeled as

), when the Ulysses satellite was on the heliospheric equatorial plane (HGI Lat

) at a distance of ∼5 A.U. (see, e.g., [ 19 , 20 ]). …”

Section: Data Descriptionmentioning

confidence: 99%

“…(ii) an already investigated long-standing period of nearly constant slow solar wind conditions (

km/s) from 01 October 1997 to 31 March 1998 (labeled as

), when the Ulysses satellite was on the heliospheric equatorial plane (HGI Lat

) at a distance of ∼5 A.U. (see, e.g., [ 19 , 20 ]).…”

Section: Data Descriptionmentioning

confidence: 99%

On Yaglom’s Law for the Interplanetary Proton Density and Temperature Fluctuations in Solar Wind Turbulence

Consolini

Alberti

Carbone

2020

Entropy

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In the past decades, there has been an increasing literature on the presence of an inertial energy cascade in interplanetary space plasma, being interpreted as the signature of Magnetohydrodynamic turbulence (MHD) for both fields and passive scalars. Here, we investigate the passive scalar nature of the solar wind proton density and temperature by looking for scaling features in the mixed-scalar third-order structure functions using measurements on-board the Ulysses spacecraft during two different periods, i.e., an equatorial slow solar wind and a high-latitude fast solar wind, respectively. We find a linear scaling of the mixed third-order structure function as predicted by Yaglom’s law for passive scalars in the case of slow solar wind, while the results for fast solar wind suggest that the mixed fourth-order structure function displays a linear scaling. A simple empirical explanation of the observed difference is proposed and discussed.

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Bimodal distribution of the solar wind at 1 AU

Larrodera

Cid

2020

A&A

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Aims. Here we aim to separate the two main contributions of slow and fast solar wind that appear at 1 AU. Methods. The Bi-Gaussian function is proposed as the probability distribution function of the two main components of the solar wind. The positions of the peaks of every simple Gaussian curve are associated with the typical values of every contribution to solar wind. We used the entire data set from the Advanced Composition Explorer (ACE) mission in an analysis of the data set as a whole and as yearly series. Solar cycle dependence is considered to provide more accurate results for the typical values of the different parameters.Results. The distribution of the solar wind at 1 AU is clearly bimodal, not only for velocity, but also for proton density, temperature and magnetic field. New typical values for the main parameters of the slow and fast components of the solar wind at 1 AU are proposed.

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A probabilistic approach to heterogeneity in space plasmas: the case of magnetic field intensity in solar wind

Cited by 5 publications

References 40 publications

Probability density functions for the variable solar wind near the solar cycle minimum

Probability density functions for the variable solar wind near the solar cycle minimum

On Yaglom’s Law for the Interplanetary Proton Density and Temperature Fluctuations in Solar Wind Turbulence

Bimodal distribution of the solar wind at 1 AU

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