# Contextual Predictions for Parker Solar Probe II: Turbulence Properties and Taylor Hypothesis

## TL;DR

This paper uses global MHD simulations to predict turbulence properties along the Parker Solar Probe trajectory and assesses the validity of the Taylor hypothesis in these conditions, aiding future data interpretation.

## Contribution

It introduces a method to evaluate turbulence conditions and the Taylor hypothesis validity using self-consistent MHD simulations for PSP's upcoming orbits.

## Key findings

- Simulations predict large variations in turbulence properties near the Sun.
- The approach helps anticipate conditions for better data analysis.
- Assessment of Taylor hypothesis accuracy along PSP trajectory.

## Abstract

The Parker Solar Probe (PSP) primary mission extends seven years and consists of 24 orbits of the Sun with descending perihelia culminating in a closest approach of ($\sim 9.8~R_\odot$). In the course of these orbits PSP will pass through widely varying conditions, including anticipated large variations of turbulence properties such as energy density, correlation scales and cross helicities. Here we employ global magnetohydrodynamics simulations with self-consistent turbulence transport and heating \citep{usmanov2018} to preview likely conditions that will be encountered by PSP, by assuming suitable boundary conditions at the coronal base. The code evolves large-scale parameters -- such as velocity, magnetic field, and temperature -- as well as turbulent energy density, cross helicity, and correlation scale. These computed quantities provide the basis for evaluating additional useful parameters that are derivable from the primary model outputs. Here we illustrate one such possibility in which computed turbulence and large-scale parameters are used to evaluate the accuracy of the Taylor "frozen-in" hypothesis along the PSP trajectory. Apart from the immediate purpose of anticipating turbulence conditions that PSP will encounter, as experience is gained in comparisons of observations with simulated data, this approach will be increasingly useful for planning and interpretation of subsequent observations.