Observation-based modelling of magnetised coronal mass ejections with EUHFORIA

Abstract

Context. Coronal Mass Ejections (CMEs) are the primary source of strong space weather disturbances at Earth. Their geoeffectiveness is largely determined by their dynamic pressure and internal magnetic fields, for which reliable predictions at Earth are not possible with traditional cone CME models. Aims. We study two well-observed Earth-directed CMEs using the EUropean Heliospheric FORecasting Information Asset (EUH-FORIA) model, testing for the first time the predictive capabilities of a linear force-free spheromak CME model initialised using parameters derived from remote-sensing observations. Methods. Using observation-based CME input parameters, we perform magnetohydrodynamic simulations of the events with EUH-FORIA, using the cone and spheromak CME models.Results. Simulations show that spheromak CMEs propagate faster than cone CMEs when initialised with the same kinematic parameters. We interpret these differences as result of different Lorentz forces acting within cone and spheromak CMEs, which lead to different CME expansions in the heliosphere. Such discrepancies can be mitigated by initialising spheromak CMEs with a reduced speed corresponding to the radial speed only. Results at Earth evidence that the spheromak model improves the predictions of B (B z ) up to 12-60 (22-40) percentage points compared to a cone model. Considering virtual spacecraft located within ±10 • around Earth, B (B z ) predictions reach 45-70% (58-78%) of the observed peak values. The spheromak model shows inaccurate predictions of the magnetic field parameters at Earth for CMEs propagating away from the Sun-Earth line.Conclusions. The spheromak model successfully predicts the CME properties and arrival time in the case of strictly Earth-directed events, while modelling CMEs propagating away from the Sun-Earth line requires extra care due to limitations related to the assumed spherical shape. The spatial variability of modelling results and the typical uncertainties in the reconstructed CME direction advocate the need to consider predictions at Earth and at virtual spacecraft located around it.

Multipoint Observations of the June 2012 Interacting Interplanetary Flux Ropes

“…As shown in Figure 1, Earth and Venus were almost radially aligned during the passage of the CMEs; their longitudinal separation was 5.4 • and their latitudinal separation was 0.2 • . The solar and heliospheric characteristics of these CMEs, as well as some of their in-situ signatures (particularly those at Earth), have been investigated in several previous studies (e.g., Kubicka et al, 2016;James et al, 2017James et al, , 2018Palmerio et al, 2017;Srivastava et al, 2018;Pomoell et al, 2019;Scolini et al, 2019;Wang et al, 2019). Here, we focus on comparing interplanetary observations at Venus and Earth.…”

Forecasting the Ambient Solar Wind with Numerical Models. II. An Adaptive Prediction System for Specifying Solar Wind Speed near the Sun

“…Over the last decade, the WSA relation for specifying solar wind conditions near the Sun has been one of the most frequently used modelling approaches for studying the consequences of evolving space weather in the heliosphere. Examples include the prediction of high-speed solar wind streams [33,21,39], the prediction of arrival time and speed of coronal mass ejections [59,69,49,63], the study of the sensitivity of CME events to model parameter settings [60,7], the propagation of coronal mass ejections in the evolving ambient solar wind [24,56], the prediction of solar energetic particles [23,19,66], the understanding of how the evolving ambient solar wind flow interacts with planetary magnetospheres [9], or the study of Forbush decreases in the flux of galactic cosmic rays [68].…”

CME–CME Interactions as Sources of CME Geoeffectiveness: The Formation of the Complex Ejecta and Intense Geomagnetic Storm in 2017 Early September

“…In this work, we initialise spheromak CMEs at 0.1 AU using the following observation-based parameters recovered from the GCS fitting: longitude (θ CME ), latitude (φ CME ), and half width (ω CME /2, average of the values provided in Table 1). Moreover, the speeds of the inserted CMEs are set using the CME radial speed v rad CME derived from the GCS fitting, as discussed in detail by Scolini et al (2019). Due to the more limited observational constraints available, two additional parameters, the CME mass density and temperature, are set to default values (ρ CME = 10 −18 kg m −3 and T CME = 0.8 × 10 6 K, respectively).…”

“…Among all, an initial tilt corresponding to a WSE flux rope type for all three CMEs provides the best B z predictions compared to in situ observations. We set the toroidal magnetic flux ϕ t of each spheromak CME based on the estimated Run number CME1 CME2 CME3 00-00-00 ---01-00-00 spheromak --01-01-00 spheromak spheromak -01-01-01 spheromak spheromak spheromak 00-00-01 --spheromak reconnected flux ϕ r derived from statistical and singleevent studies (Table 2) (using the same methodology as Scolini et al 2019), and under the assumption that the reconnected flux only contributes to the poloidal flux of the flux rope (i.e. ϕ r ≈ ϕ p ; Qiu et al 2007;Möstl et al 2008;Gopalswamy et al 2017).…”

“…Full 3D simulations outputs of the whole heliospheric domain are extracted with a 1hour cadence, while time series of all MHD variables at Earth and selected virtual spacecraft are produced with a 10-minute cadence. Similarly to Scolini et al (2019), we place in our simulation domain an array of virtual spacecraft in the surroundings of Earth, separated by an angular distance of ∆σ = 5 • and ∆σ = 10 • (different combinations of longitudes and latitudes are considered), in order to assess the spatial variability of the results in the vicinity of Earth.…”