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

This paper is dedicated to the new implicit unstructured coronal code COCONUT, which aims at providing fast and accurate inputs for space-weather forecasting as an alternative to empirical models. We use all 20 available magnetic maps of the solar photosphere covering the date of 2019 July 2, which corresponds to a solar eclipse on Earth. We use the same standard preprocessing on all maps, then perform coronal MHD simulations with the same numerical and physical parameters. We conclude by quantifying the performance of 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 of space-weather forecasting and show that the choice of the input magnetic map has a strong impact. We find performances between 24% and 85% for the streamers' edges, 24%-88% for the coronal hole boundaries, and a mean deviation between 4 degrees and 12 degrees for the heliospheric current sheet position. We find that the HMI runs perform better on all indicators, with GONG-ADAPT being the second-best choice. HMI runs perform better for the streamers' edges, and GONG-ADAPT for polar coronal holes, HMI synchronic for equatorial coronal holes, and the streamer belt. We especially illustrate 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.