Properties of Jupiter's magnetospheric turbulence observed by the Galileo spacecraft

Abstract

In collisionless plasmas, turbulence is thought to play an important role in mass transport and energy dissipation. Magnetic fluctuations in the Jovian magnetosphere are essential in a turbulent state. Previous studies of that turbulence have focused on the large scales using low time resolution of magnetic field data. Here we extend those studies to cover a wider range of scales by combining both low and high-time-resolution data of Galileo magnetometer. We use particle data from the plasma instrument and include energetic particle contributions to estimate the local plasma parameters. We obtain 11 power spectra of magnetic field in the frequency range of 10 À4 -1 Hz, which covers both magnetohydrodynamics and ion kinetic scales. The frequencies of the evidenced spectral breaks are found to be relatively well correlated with the characteristic scales of heavy ion. The spectral indices below and above the spectral breaks are found to be broad and cover the ranges of 0.6-1.9 and 1.7-2.5, respectively. An analysis of higher-order statistics shows an intermittent feature of the turbulence, found to be more prominent in the plasma sheet than in the lobe. Furthermore, a statistical survey of the power of the fluctuations using low-time-resolution data suggests a radially varying dawn-dusk asymmetry: the total power is larger in the duskside (dawnside) at <50 R J (>80 R J ), which would reflect flow shear and global magnetospheric activity.

Local time dependence of turbulent magnetic fields in Saturn's magnetodisc

“…The perturbation of the magnetic field is then δ B ( t ) = B ( t ) − B 0 , and perpendicular fluctuations of the magnetic field are given by δ B ( t ) ⊥ = δ B ( t ) − δ B ( t ) ∥ , where δ B ( t ) ∥ is the component of the fluctuation of the magnetic field parallel to B 0 . The power spectrum of vector components of δ B ( t ) ⊥ is then estimated as in Tao et al []: where W i ( t i , f ) is a Morlet wavelet with the period of (1.03 f ) 1 [ Farge , ; Torrence and Compo , ]. The total power spectrum of the perpendicular fluctuation is calculated as the square root of the sum of the squares of the components.…”

Characterization of turbulence in the Mars plasma environment with MAVEN observations

“…During this time interval, the spacecraft traversed from the magnetic pileup region to the magnetosheath and finally into the upstream region (see Figure d). Performing a wavelet transform on the magnetic field time series as in Tao et al [], we compute power spectral densities (PSDs) for the magnetic field fluctuations (see Figure c): where W x , W y , and W z are the wavelet transforms of the x , y , and z MSO components of the magnetic field. Here Δ t is the inverse sampling rate of the magnetometer.…”

“…Turbulence in these plasma environments shows both similarities and differences with that of the solar wind turbulence. In particular, similar to the solar wind, magnetic field fluctuations in these plasma environments display different spectral indices in different frequency ranges [ Zimbardo et al , ; Hadid et al , ; Tao et al , ]. However, the spectral index in a given frequency range has different values in different regions of a given plasma environment [ Zimbardo et al , ].…”

“…For example, Hadid et al [] observed that, in the magnetosheath of Saturn, the spectral index changes from a value of approximately −1.2 at low frequencies to a value of approximately −2.5 at high frequencies (frequencies higher than the proton gyrofrequency) without forming the intermediate inertial range that has the Kolmogorov scaling value, unlike in the solar wind. The absence of the inertial range has also been observed at the Earth's magnetosheath and Jupiter's magnetosphere [ Zimbardo et al , ; Tao et al , ]. However, the absence of the frequency range with the Kolmogorov scaling value is not a universal feature in planetary plasma environments.…”

Identifying Active Kelvin–Helmholtz Vortices on Saturn's Magnetopause Boundary

“…These velocities can be determined using an empirically fitted radial profile (Thomsen et al, ). For a given 1‐s‐averaged MAG time series, the one‐dimensional power spectrum of is calculated with a Morlet wavelet transform (Tao et al, ). Similarly to section , is integrated with a factor , as in equation .…”