Effect of the Initial Shape of Coronal Mass Ejections on 3‐D MHD Simulations and Geoeffectiveness Predictions

Scolini, Camilla; Verbeke, C.; Poedts, Stefaan; Chané, Emmanuel; Pomoell, Jens; Zuccarello, FP

doi:10.1029/2018sw001806

Cited by 48 publications

(55 citation statements)

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“…Indeed, it is well known that the speed and the magnetic field amplitude and orientation of the plasma that impinges on the Earth's magnetosphere are causally related to the onset of geomagnetic storms (Gosling, ). The low‐density magnetized plasma that constitutes the solar wind is well described by magnetohydrodynamics (MHD), and the standard way of forecasting CME propagation adopted by all major Space Weather forecasting providers, is to resolve numerically the MHD equations, with boundary and initial conditions appropriate to mimic an incoming CME (see, e.g., Lee et al, ; Liu et al, ; Parsons et al, ; Scolini et al, ). We note in passing that the problem of determining boundary and initial conditions (which are not completely observable) constitute a core challenge for quantifying the uncertainties associated with numerical simulations (Kay & Gopalswamy, ), and where machine learning techniques can also be successfully employed, especially within the gray‐box paradigm commented in section .…”

Section: Review Of Machine Learning In Space Weathermentioning

confidence: 99%

The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting

2019

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The numerous recent breakthroughs in machine learning make imperative to carefully ponder how the scientific community can benefit from a technology that, although not necessarily new, is today living its golden age. This Grand Challenge review paper is focused on the present and future role of machine learning in Space Weather. The purpose is twofold. On one hand, we will discuss previous works that use machine learning for Space Weather forecasting, focusing in particular on the few areas that have seen most activity: the forecasting of geomagnetic indices, of relativistic electrons at geosynchronous orbits, of solar flares occurrence, of coronal mass ejection propagation time, and of solar wind speed. On the other hand, this paper serves as a gentle introduction to the field of machine learning tailored to the Space Weather community and as a pointer to a number of open challenges that we believe the community should undertake in the next decade. The recurring themes throughout the review are the need to shift our forecasting paradigm to a probabilistic approach focused on the reliable assessment of uncertainties, and the combination of physics-based and machine learning approaches, known as gray box. Plain Language SummaryIn the last decade, machine learning has achieved unforeseen results in industrial applications. In particular, the combination of massive data sets and computing with specialized processors (graphics processing units, or GPUs) can perform as well or better than humans in tasks like image classification and game playing. Space weather is a discipline that lives between academia and industry, given the relevant physical effects on satellites and power grids in a variety of applications, and the field therefore stands to benefit from the advances made in industrial applications. Today, machine learning poses both a challenge and an opportunity for the space weather community. The challenge is that the current data science revolution has not been fully embraced, possibly because space physicists remain skeptical of the gains achievable with machine learning. If the community can master the relevant technical skills, they should be able to appreciate what is possible within a few years time and what is possible within a decade. The clearest opportunity lies in creating space weather forecasting models that can respond in real time and that are built on both physics predictions and on observed data.

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Section: Review Of Machine Learning In Space Weathermentioning

confidence: 99%

The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting

2019

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“…This can be associated to the half-width employed in the Cone model w CME ( • ) via the relation r 0 = 0.1 au · sin w CME . A thorough discussion of the association between the radius and halfwidth is presented in Scolini et al (2018). As in the Cone model implementation in EUHFORIA, the density ρ CME and temperature T CME are taken to be constant inside the CME.…”

Section: Summary Of the Free Parameters In The Lffs Modelmentioning

confidence: 99%

The evolution of coronal mass ejections in the inner heliosphere: Implementing the spheromak model with EUHFORIA

Verbeke

Pomoell

Poedts

2019

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107

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Aims.We introduce a new model for coronal mass ejections (CMEs) that has been implemented in the magnetohydrodynamics (MHD) inner heliosphere model EUHFORIA. Utilising a linear force-free spheromak (LFFS) solution, the model provides an intrinsic magnetic field structure for the CME. As a result, the new model has the potential to predict the magnetic components of CMEs at Earth. In this paper, we present the implementation of the new model and show the capability of the new model. Methods. We present initial validation runs for the new magnetised CME model by considering the same set of events as used in the initial validation run of EUHFORIA that employed the Cone model. In particular, we have focused on modelling the CME that was responsible for creating the largest geomagnetic disturbance (Dst index). Two scenarios are discussed: one where a single magnetised CME is launched and another in which we launch all five Earth-directed CMEs that were observed during the considered time period. Four out of the five CMEs were modelled using the Cone model. Results. In the first run, where the propagation of a single magnetized CME is considered, we find that the magnetic field components at Earth are well reproduced as compared to in-situ spacecraft data. Considering a virtual spacecraft that is separated approximately seven heliographic degrees from the position of Earth, we note that the centre of the magnetic cloud is missing Earth and a considerably larger magnetic field strength can be found when shifting to that location. For the second run, launching four Cone CMEs and one LFFS CME, we notice that the simulated magnetised CME is arriving at the same time as in the corresponding full Cone model run. We find that to achieve this, the speed of the CME needs to be reduced in order to compensate for the expansion of the CME due to the addition of the magnetic field inside the CME. The reduced initial speed of the CME and the added magnetic field structure give rise to a very similar propagation of the CME with approximately the same arrival time at 1 au. In contrast to the Cone model, however, the magnetised CME is able to predict the magnetic field components at Earth. However, due to the interaction between the Cone model CMEs and the magnetised CME, the magnetic field amplitude is significantly lower than for the run using a single magnetised CME. Conclusions. We have presented the LFFS model that is able to simulate and predict the magnetic field components and the propagation of magnetised CMEs in the inner heliosphere and at Earth. We note that shifting towards a virtual spacecraft in the neighbourhood of Earth can give rise to much stronger magnetic field components. This gives the option of adding a grid of virtual spacecrafts to give a range of values for the magnetic field components.

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“…It allows propagation of CMEs through a steady background solar wind in the inner heliosphere from 0.1 AU onwards. We here use EUHFORIA with a cone CME model (e.g., Scolini et al, 2018) that treats CMEs as dense spheres with no internal magnetic field structure, using the input parameters presented in Table 1. The EUHFORIA simulation run for the three CMEs is shown in Supplementary Video 1.…”

Section: Heliospheric Observations and Modelingmentioning

confidence: 99%

Multipoint Observations of the June 2012 Interacting Interplanetary Flux Ropes

Kilpua

Good

Palmerio

et al. 2019

Front. Astron. Space Sci.

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We report a detailed analysis of interplanetary flux ropes observed at Venus and subsequently at Earth's Lagrange L1 point between June 15 and 17, 2012. The observation points were separated by about 0.28 AU in radial distance and 5 • in heliographic longitude at this time. The flux ropes were associated with three coronal mass ejections (CMEs) that erupted from the Sun on June 12-14, 2012 (SOL2012-06-12, SOL2012-06-13, and SOL2012-06-14). We examine the CME-CME interactions using in-situ observations from the almost radially aligned spacecraft at Venus and Earth, as well as using heliospheric modeling and imagery. The June 14 CME reached the June 13 CME near the orbit of Venus and significant interaction occurred before they both reached Earth. The shock driven by the June 14 CME propagated through the June 13 CME and the two CMEs coalesced, creating the signatures of one large, coherent flux rope at L1. We discuss the origin of the strong interplanetary magnetic fields related to this sequence of events, the complexity of interpreting solar wind observations in the case of multiple interacting CMEs, and the coherence of the flux ropes at different observation points.

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Effect of the Initial Shape of Coronal Mass Ejections on 3‐D MHD Simulations and Geoeffectiveness Predictions

Cited by 48 publications

References 30 publications

The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting

The Challenge of Machine Learning in Space Weather: Nowcasting and Forecasting

The evolution of coronal mass ejections in the inner heliosphere: Implementing the spheromak model with EUHFORIA

Multipoint Observations of the June 2012 Interacting Interplanetary Flux Ropes

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