Constraining Solar Wind Transport Model Parameters Using Bayesian Analysis

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Abstract We apply nested-sampling Bayesian analysis to a model for the transport of magnetohydrodynamic-scale solar wind fluctuations. The dual objectives are to obtain improved constraints on parameters present in the turbulence transport model (TTM) and to support quantitative comparisons of the quality of distinct versions of the transport model. The TTMs analyzed are essentially the 1D steady-state ones presented in Breech et al. that describe the radial evolution of the energy, correlation length, and normalized cross helicity of the fluctuations, together with the proton temperature, in prescribed background solar wind fields. Modeled effects present in the TTM include nonlinear turbulence interactions, shear driving, and energy injection associated with pickup-ions. Each of these modeled effects involves adjustable parameters that we seek to constrain using Bayesian analysis. We find that, given the TTMs and observational data sets analyzed, the most appropriate TTM to recommend corresponds to 2D fluctuations and has von Kármán–Howarth parameters of α ≈ 0.16 and β ≈ 0.10, along with reasonably standard values for the other adjustable parameters. The analysis also indicates that it is advantageous to include pickup ion effects in the lengthscale evolution equation by assuming Z 2β/α λ is locally conserved. Such Bayesian analysis is readily extended to more sophisticated solar wind models, space weather models, and might lead to improved predictions of, for example, solar flare and coronal mass ejection interactions with the Earth.