We present a novel global 3D coronal MHD model called COCONUT, polytropic in its first stage and based on a time-implicit backward Euler scheme. Our model boosts run-time performance in comparison with contemporary MHD-solvers based on explicit schemes, which is particularly important when later employed in an operational setting for space-weather forecasting. It is data-driven in the sense that we use synoptic maps as inner boundary inputs for our potential-field initialization as well as an inner boundary condition in the further MHD time evolution. The coronal model is developed as part of the EUropean Heliospheric FORecasting Information Asset (EUHFORIA) and will replace the currently employed, more simplistic, empirical Wang–Sheeley–Arge (WSA) model. At 21.5 R ⊙ where the solar wind is already supersonic, it is coupled to EUHFORIA’s heliospheric model. We validate and benchmark our coronal simulation results with the explicit-scheme Wind-Predict model and find good agreement for idealized limit cases as well as real magnetograms, while obtaining a computational time reduction of up to a factor 3 for simple idealized cases, and up to 35 for realistic configurations, and we demonstrate that the time gained increases with the spatial resolution of the input synoptic map. We also use observations to constrain the model and show that it recovers relevant features such as the position and shape of the streamers (by comparison with eclipse white-light images), the coronal holes (by comparison with EUV images), and the current sheet (by comparison with WSA model at 0.1 au).

Aims This paper presents a H2020 project aimed at developing the world’s most advanced space weather forecasting tool, combining the MagnetoHydroDynamic (MHD) solar wind and Coronal Mass Ejection (CME) evolution modelling with Solar Energetic Particle (SEP) transport and acceleration model(s). The EUHFORIA 2.0 project will address the geoeffectiveness of impacts and mitigation to avoid (part of the) damage, including that of extreme events, related to solar eruptions, solar wind streams, and SEPs, with particular emphasis on its application to forecast Geomagnetically Induced Currents (GICs) and radiation on geospace. Methods We will apply innovative methods and state-of-the-art numerical techniques to extend the recent heliospheric solar wind and CME propagation model EUHFORIA with two integrated key facilities that are crucial for improving its predictive power and reliability, namely 1) data-driven flux-rope CME models, and 2) physics-based, self-consistent SEP models for the acceleration and transport of particles along and across the magnetic field lines. This involves the novel coupling of advanced space weather models. In addition, after validating the upgraded EUHFORIA/SEP model, it will be coupled to existing models for GICs and atmospheric radiation transport models. This will result in a reliable prediction tool for radiation hazards from SEP events, affecting astronauts, passengers and crew in high-flying aircraft, and the impact of space weather events on power grid infrastructure, telecommunication, and navigation satellites. Finally, this innovative tool will be integrated into both the Virtual Space Weather Modeling Centre (VSWMC, ESA) and the space weather forecasting procedures at the ESA SSCC in Uccle (Belgium), so that it will be available to the space weather community and effectively used for improved predictions and forecasts of the evolution of CME magnetic structures and their impact on Earth. Results The results of the first six months of the EU H2020 project are presented here. These concern alternative coronal models, the application of adaptive mesh refinement techniques in the heliospheric part of EUHFORIA, alternative flux-rope CME models, evaluation of data-assimilation based on Karman filtering for the solar wind modelling, and a feasibility study of the integration of SEP models.

Context. Simulating the propagation and predicting the arrival time of coronal mass ejections (CMEs) in the inner heliosphere with a full three-dimensional (3D) magnetohydrodynamic (MHD) propagation model requires a significant amount of computational time. For CME forecasting purposes, multiple runs may be required for different reasons such as ensemble modeling (uncertainty on input parameters) and error propagation. Moreover, higher resolution runs may be necessary, which also requires more CPU time, for example for the prediction of solar energetic particle acceleration and transport or in the framework of more in-depth studies about CME erosion and/or deformation during its evolution. Aims. In this paper we present ICARUS, a new inner heliospheric model for the simulation of a steady background solar wind and the propagation and evolution of superposed CMEs. This novel model has been implemented within the MPI-AMRVAC framework which enables the use of stretched grids and solution adaptive mesh refinement (AMR). The usefulness and efficiency (speed-up) of these advanced features are explored. In particular, we model a typical solar wind with ICARUS and then launch a simple cone CME and follow its evolution. We focus on the effect of radial grid stretching and two specific methods or criteria to trigger solution AMR on this typical simulation run. Methods. For the solar background wind simulation run, we limited the mesh refinement to the area(s) of interest, in this case a co-rotating interaction region (CIR). For the CME evolution run, on the other hand, we apply AMR where the CME is located by the use of a tracing function. As such, the grid is coarsened again after the CME has passed. Results. The implemented AMR is flexible and only refines the mesh in a particular sector of the computational domain, for example around the Earth or a single CIR, and/or for a particular feature such as CIR or CME shocks. Radial grid stretching alone yields speed-ups of up to 4 and more, depending on the resolution. Combined with solution adaptive mesh refinement, the speed-ups can be much larger depending on the complexity of the simulation (e.g., number of CIRs in the background wind, number of CMEs) and on the chosen AMR criteria, thresholds and the number of refinement levels. Conclusions. The ICARUS model implemented in the MPI-AMRVAC framework is a new inner heliospheric 3D MHD model that uses grid stretching as well as AMR techniques. The flexibility in the grid and its resolution allows an optimization of the computational time required for CME propagation simulations for both scientific and forecasting purposes.

Context. Coronal mass ejections (CMEs) are one of the main drivers of disturbances in interplanetary space. Strong CMEs, when directed towards the Earth, cause geomagnetic storms upon interacting with the Earth's magnetic field, and can cause significant damage to our planet and affect everyday life. As such, efficient space weather prediction tools are necessary to forecast the arrival and impact of CME eruptions. Recently, a new heliospheric model called Icarus was developed based on MPI-AMRVAC, which is a 3D ideal magnetohydrodynamics (MHD) model for the solar wind and CME propagation, and it introduces advanced numerical techniques to make the simulations more efficient. In this model the reference frame is chosen to be co-rotating with the Sun, and radial grid stretching together with adaptive mesh refinement (AMR) can be applied to the numerical domain. Aims. Grid stretching and AMR speed up simulation results and performance. Our aim is to combine the advanced techniques available in the Icarus model in order to obtain better results with fewer computational resources than with the equidistant grid. Different AMR strategies are suggested, depending on the purpose of the simulation. Methods. In this study, we model the CME event that occurred on July 12, 2012. A cone model was used to study the CME's evolution through the background solar wind, and its arrival at and impact with the Earth. Grid stretching and AMR were combined in the simulations by using multiple refinement criteria, to assess its influence on the simulations' accuracy and the required computational resources. We compare simulation results to the EUHFORIA model. Results. We applied different refinement criteria to investigate the potential of solution AMR for different applications. As a result, the simulations were sped up by a factor of ∼17 for the most optimal configuration in Icarus. For the cone CME model, we found that limiting the AMR to the region around the CME-driven shock yields the best results. The results modelled by the simulations with radial grid stretching and AMR level 4 are similar to the results provided by the original EUHFORIA and Icarus simulations with the 'standard' resolution and equidistant grids. The simulations with 5 AMR levels yielded better results than the simulations with an equidistant grid and standard resolution. Conclusions. Solution AMR is flexible and provides the user the freedom to modify and locally increase the grid resolution according to the purpose of the simulation. We find that simulations with a combination of grid stretching and AMR can reproduce the simulations performed on equidistant grids significantly faster. The advanced techniques implemented in Icarus can be further used to improve the forecasting procedures, since the reduced simulation time is essential to make physics-based forecasts less computationally expensive.

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° and 12° 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.