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Scaling anisotropy of the power in parallel and perpendicular components of the solar wind magnetic field
Author(s) -
M. A. Forman,
R. T. Wicks,
T. S. Horbury,
S. Oughton
Publication year - 2013
Publication title -
aip conference proceedings
Language(s) - English
Resource type - Conference proceedings
SCImago Journal Rank - 0.177
H-Index - 75
eISSN - 1551-7616
pISSN - 0094-243X
DOI - 10.1063/1.4811014
Subject(s) - physics , solar wind , magnetohydrodynamics , magnetic field , computational physics , scaling , anisotropy , astrophysics , nuclear magnetic resonance , geometry , optics , mathematics , quantum mechanics
Power spectra of the components of the magnetic field parallel (Pzz) and perpendicular (Pzz+Pyy) to the local mean magnetic field direction were determined by wavelet methods from Ulysses’ MAG instrument data during eighteen 10-day segments of its first North Polar pass at high latitude at solar minimum in 1995. The power depends on frequency f and the angle θ between the solar wind direction and the local mean field, and with distance from the Sun. This data includes the solar wind whose total power (Pxx + Pyy + Pzz) in magnetic fluctuations we previously reported depends on f and the angle θ nearly as predicted by the GS95 critical balance model of strong incompressible MHD turbulence. Results at much wider range of frequencies during six evenly-spaced 10-day periods are presented here to illustrate the variability and evolution with distance from the Sun. Here we investigate the aniso tropic scaling of Pzz(f,θ) in particular because it is a reduced form of the Poloidal (pseudo-Alfvenic) component of the (incompressible) fluctuations. We also report the much larger Pxx(f,θ)+Pyy(f,θ) which is (mostly) reduced from the Toroidal (Alfvenic, i.e., perpendicular to both B and k) fluctuations, and comprises most of the total power. These different components of the total power evolve and scale differently in the inertial range. We compare these elements of the magnetic power spectral tensor with “critical balance” model predictions

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