Forecasting the Structure and Orientation of Earthbound Coronal Mass Ejections

Kilpua, Emilia; Lugaz, Noé; Mays, M. L.; Temmer, Manuela

doi:10.1029/2018sw001944

Cited by 84 publications

(61 citation statements)

References 280 publications

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“…CMEs are violent eruptions of magnetized plasma that leave the surface of the Sun with speed as large as 1,000 km/s. Predicting the evolution of a CME as it expands away from the Sun and travels toward Earth is one of the major challenges of Space Weather forecasting (Kilpua et al, ). 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, ).…”

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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“…The CMEs erupting from the Sun evolve as they travel through the interplanetary medium to become the ICME seen at 1 AU (see review by Manchester et al, ; Kilpua et al, ). The key parameters for assessing how an ICME will interact with the Earth's magnetosphere are the solar wind speed, v , and the strength of the southward component, Bs , of the interplanetary magnetic field (IMF).…”

Section: Interplanetary Conditionsmentioning

confidence: 99%

A 21st Century View of the March 1989 Magnetic Storm

2019

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On 13 March 1989, the largest magnetic storm of the last century caused widespread effects on power systems including a blackout of the Hydro-Québec system. Since then this event has become the archetypal disturbance for examining the geomagnetic hazard to power systems. However, even 30 years on from 1989, the story of exactly what happened in March 1989 is far from complete. This paper reexamines the information available about the March 1989 event and uses this to construct a timeline and description of the space weather phenomena and how they caused the power system effects. The evidence shows that the disturbance was caused by two coronal mass ejections (CMEs): the first associated with a X4.5 flare on 10 March and the second linked to a M7.3 flare on 12 March. The arrival of the interplanetary CME shock fronts caused storm sudden commencements at 01.27 and 07.43 UT on 13 March. The transit time and speed of the first (second) interplanetary CME shock are 54.5 hr (31.5 hr) and 760 km/s (1,320 km/s). Empirical relations are used to estimate solar wind speed and southward interplanetary magnetic field, Bs, and give values of v = 980 km/s, Bs = 40 to 60 nT at the peak of the storm. Key findings are that the second storm sudden commencement occurred at the same time as the substorm that impacted the Hydro-Québec system and indicates that external triggering of the substorm may have contributed to a faster substorm onset than might otherwise have occurred. This caused the production of larger geomagnetically induced currents that caused the Hydro-Québec blackout. The March 1989 storm had the largest recorded value of the Dst index representing the size of the magnetic storm main phase, but the Hydro-Québec blackout occurred early in the storm when the Dst value was less disturbed. Only later in the storm did Dst reach its peak value. At this time an expansion of the auroral oval brought disturbances to lower latitudes where they caused power system problems in the United States, United Kingdom, and Sweden.

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“…These structures are thought to characterize a current-carrying (sheared or twisted) magnetic field system possessing significant magnetic helicity (Pevtsov et al 2014). CMEs are the key drivers of adverse space weather events, mostly because they can provide sustained periods of strongly southward magnetic field, allowing efficient transportation of solar wind energy, plasma, and momentum into the Earth's magnetosphere (Kilpua et al 2019).…”

Section: Introductionmentioning

confidence: 99%

The Relationship between Chirality, Sense of Rotation, and Hemispheric Preference of Solar Eruptive Filaments

Zhou

Cheng

et al. 2020

ApJ

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The orientation, chirality, and dynamics of solar eruptive filaments is a key to understanding the magnetic field of coronal mass ejections (CMEs) and therefore to predicting the geoeffectiveness of CMEs arriving at Earth. However, confusion and contention remain over the relationship between the filament chirality, magnetic helicity, and sense of rotation during eruption. To resolve the ambiguity in observations, in this paper, we used stereoscopic observations to determine the rotation direction of filament apex and the method proposed by Chen et al. (2014) to determine the filament chirality. Our sample of 12 eruptive active-region filaments establishes a strong one-to-one relationship, i.e., during the eruption, sinistral/dextral filaments (located in the southern/northern hemisphere) rotate clockwise/counterclockwise when viewed from above, and corroborates a weak hemispheric preference, i.e., a filament and related sigmoid both exhibit a forward (reverse) S shape in the southern (northern) hemisphere, which suggests that the sigmoidal filament is associated with a low-lying magnetic flux rope with its axis dipped in the middle. As a result of rotation, the projected S shape of a filament is anticipated to be reversed during eruption.

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Forecasting the Structure and Orientation of Earthbound Coronal Mass Ejections

Cited by 84 publications

References 280 publications

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

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

A 21st Century View of the March 1989 Magnetic Storm

The Relationship between Chirality, Sense of Rotation, and Hemispheric Preference of Solar Eruptive Filaments

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