Predicting the geoeffective properties of coronal mass ejections: current status, open issues and path forward

Abstract

Much progress has been made in the study of coronal mass ejections (CMEs), the main drivers of terrestrial space weather thanks to the deployment of several missions in the last decade. The flow of energy required to power solar eruptions is beginning to be understood. The initiation of CMEs is routinely observed with cadences of tens of seconds with arc-second resolution. Their inner heliospheric evolution can now be imaged and followed routinely. Yet relatively little progress has been made in predicting the geoeffectiveness of a particular CME. Why is that? What are the issues holding back progress in medium-term forecasting of space weather? To answer these questions, we review, here, the measurements, status and open issues on the main CME geoeffective parameters; namely, their entrained magnetic field strength and configuration, their Earth arrival time and speed, and their mass (momentum). We offer strategies for improving the accuracy of the measurements and their forecasting in the near and mid-term future. To spark further discussion, we incorporate our suggestions into a top-level draft action plan that includes suggestions for sensor deployment, technology development and modelling/theory improvements. This article is part of the theme issue 'Solar eruptions and their space weather impact'.

Keywords: coronal mass ejections; forecasting; space weather.

Figure 1.

(a) CME travel time to Earth against the halo expansion speed V_exp for 80 unique CME-shock correlations. The travel time T_tr is defined by the CME's first appearance in the LASCO/C2 images and the shock arrival at 1 AU. The solid line is a least-squares fit to the 80 data points, the fit function being T_tr = 203 − 20.77 × lnV _exp. The standard deviation from the fit curve is 14 h. The two dotted lines denote a 95% certainty margin of two standard deviations. The thin dashed line marks the calculated travel time for a constant radial propagation speed, V_rad, (kinematic approach) inferred from the observed V_exp, via V _rad = 0.88 × V _exp. The green dots denote CMEs without shock signatures, that is magnetic clouds (M) and plasma blobs (B). These points were not used for the fit. Reproduced with permission from Schwenn et al. [7]. (b) CME interplanetary propagation speeds, V_IPo, towards the in situ observatory, versus CME transit times, TT. The V_IPo from three geometrical models (fixed-Φ (red), harmonic mean (blue) and self-similar expansion (green)) is plotted. The details of power-law fits (solid lines) to each model are shown. The black dashed line is the mean transit time for the employed CME sample, 〈TT〉 = 71.9 h. Reproduced with permission from Möstl et al. [11]. Copyright © AAS. (Online version in colour.)

Figure 2.

Variation in Δt as a function of year for all 32 different model predictions. The line in the centre of the box gives the median of the data, while the tops and the bottoms of the box give the lower and upper quartiles. The ends of the vertical lines give the minimum and maximum values of the data (provided that there are no outliers), while any circles give the values of outliers (more then 1.5 times above/below the upper/lower quartiles). Reproduced with permission from Riley et al. [25]. Copyright © 2018, John Wiley and Sons.

Figure 3.

Mean absolute error in the time-of-arrival of CMEs versus event sample size from the studies compiled in table 1. The error bars represent the uncertainty in the mean value ($\sigma /\sqrt{N}$, where N is the sample size). The star symbols and thicker error bars denote studies with projected CME parameters (or a mixture of projected/unprojected inputs). The dashed line is the overall unweighted average (9.8 h).

Figure 4.

Plasma β (colour scale) and magnetic flux function (black contours) are displayed for three times during an MHD simulation. The edge of the closed field lines for the magnetic flux rope are displayed as a thick line (green). The black crosses denote manually selected positions through the flux rope, and the dashed line is an arc of a circle that is optimally chosen from the crosses. The middle panel identifies the uniformly distributed positions along the arc that is later used in our analysis (red squares). Reproduced with permission from [52]. Copyright © AAS. (Online version in colour.)

Figure 5.

Predicted ICME durations at 1 AU from geometrical modelling of CME STEREO observations in the corona and the inner heliosphere and associated MC durations from in situ observations. Reproduced with permission from Wood et al. [13]. Copyright © AAS. (Online version in colour.)

Figure 6.

Screenshot of the Bz4Cast model technique being implemented for the 26 August 2015 CME event. The Earth trajectory is created by using solar imagery to deduce the CME location. The Bphi component (fourth panel from top in the times series) correctly increases with time. (Online version in colour.)

Figure 7.

Improving the forecast accuracy of CME geoeffective properties requires a multi-pronged attack plan. The draft outline here demonstrates the required scope of such a plan that should extend from theory to research to instrumentation to incubation of new technologies across the full breath of Heliophysics. (Online version in colour.)