Turbulence-Driven Coronal Heating and Improvements to Empirical Forecasting of the Solar Wind
Lauren N. Woolsey, Steven R. Cranmer

TL;DR
This paper introduces two advanced solar wind prediction models that incorporate full magnetic field profiles and Alfvén wave effects, improving accuracy and providing tools for space weather forecasting.
Contribution
The paper presents a new Python-based forecasting code, TEMPEST, built upon the physics-calibrated ZEPHYR model, enhancing solar wind prediction capabilities.
Findings
Both models reproduce the anticorrelation between wind speed and flux tube expansion.
TEMPEST shows less spread in results compared to traditional methods.
The models can validate magnetic field and solar wind relations for improved forecasting.
Abstract
Forecasting models of the solar wind often rely on simple parameterizations of the magnetic field that ignore the effects of the full magnetic field geometry. In this paper, we present the results of two solar wind prediction models that consider the full magnetic field profile and include the effects of Alfv\'en waves on coronal heating and wind acceleration. The one-dimensional MHD code ZEPHYR self-consistently finds solar wind solutions without the need for empirical heating functions. Another 1D code, introduced in this paper (The Efficient Modified-Parker-Equation-Solving Tool, TEMPEST), can act as a smaller, stand-alone code for use in forecasting pipelines. TEMPEST is written in Python and will become a publicly available library of functions that is easy to adapt and expand. We discuss important relations between the magnetic field profile and properties of the solar wind that…
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