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

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 2 nd 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 find performances between 24% to 85% for the streamers' edges, 24% to 88% for the coronal hole boundaries and a mean deviation between 4 to 12 degrees for the heliospheric current sheet position. We find that the HMI runs are globally performing better on all indicators, with the GONG-ADAPT being the second-best choice. HMI runs perform better for the streamers' edges, GONG-ADAPT for polar coronal holes, HMI synchronic for equatorial coronal holes and for the streamer belt. We especially showcase the importance of the filling of the poles. This demonstrates that the solar poles have to be taken into account even for ecliptic plane previsions.