COCONUT, a novel fast-converging MHD model for solar corona simulations: II. Assessing the impact of the input magnetic map on space-weather forecasting at minimum of activity
Barbara Perri, Blazej Kuzma, Michaela Brchnelova, Tinatin, Baratashvili, Fan Zhang, Peter Leitner, Andrea Lani, Stefaan Poedts

TL;DR
This study evaluates how different input magnetic maps affect the accuracy of the COCONUT MHD model in simulating the solar corona and forecasting space weather, highlighting the importance of polar magnetic data.
Contribution
It introduces the COCONUT coronal code and systematically assesses the impact of various magnetic maps on simulation accuracy for space weather prediction.
Findings
HMI magnetic maps perform best overall.
GONG-ADAPT maps are second-best, especially for polar regions.
Polar magnetic field data significantly influence forecast accuracy.
Abstract
This paper is dedicated to the new implicit unstructured coronal code COCONUT, which aims at providing fast and accurate inputs for space weather forecast as an alternative to empirical models. We use all 20 available magnetic maps of the solar photosphere covering the date of the 2nd of July 2019 which corresponds to a solar eclipse on Earth. We use the same standard pre-processing on all maps, then perform coronal MHD simulations with the same numerical and physical parameters. In the end, we quantify the performance for each map using three indicators from remote-sensing observations: white-light total solar eclipse images for the streamers' edges, EUV synoptic maps for coronal holes and white-light coronagraph images for the heliospheric current sheet. We discuss the performance for space weather forecasts and we show that the choice of the input magnetic map has a strong impact. We…
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Taxonomy
TopicsSolar and Space Plasma Dynamics · Ionosphere and magnetosphere dynamics · Geophysics and Gravity Measurements
