2.4 Locations, widths, geometry

The latitude distribution of the central position angles of CMEs tends to cluster about the equator around solar minimum but broadens over all latitudes near solar maximum. Hundhausen (1993Jump To The Next Citation Point) first noted that this CME latitude variation more closely parallels that of streamers and prominences than of active regions or sunspots. This pattern also is closely linked to the variation of the global solar magnetic field, as exemplified by the tilt angle of the heliospheric current sheet (HCS) when the Sun makes its transition from solar minimum to maximum. This pattern including the match between CMEs, prominence eruptions and the HCS has been confirmed with the LASCO data (Figure 11View ImageGopalswamy, 2004Jump To The Next Citation Point; Gopalswamy et al., 2010aJump To The Next Citation Point). On this figure also note the sharp decrease in the rate of CMEs and prominence eruptions in ∼ 2006 when the HCS became flatter below 30° solar latitude.
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Figure 11: Latitudes of LASCO CMEs (filled circles) with known solar surface associations (identified from microwave prominence eruptions) plotted vs time, by Carrington Rotation number. The dotted and dashed curves represent the tilt angle of the heliospheric current sheet in the northern and southern hemispheres, respectively; the solid curve is the average of the two. The up and down arrows denote the times when the polarity in the north and south solar poles, resp., reversed. Note that the high latitude CMEs and PEs are confined to the solar maximum phase and their occurrence is asymmetric in the northern and southern hemispheres. PEs at latitudes below 40° may arise from active regions or quiescent filament regions, but those at higher latitudes are always from the latter. Image adapted from Gopalswamy (2004Jump To The Next Citation Point); Gopalswamy et al. (2010aJump To The Next Citation Point), updated by S. Yashiro (2011).

In pre-SOHO coronagraph observations the angular size distribution of CMEs seemed to vary little over the cycle, maintaining an average width of about 45° (SMM – Hundhausen, 1993Jump To The Next Citation Point; Solwind – Howard et al., 1985Jump To The Next Citation Point). However, the CME size distribution observed by LASCO and the CORs is affected by their increased detection of very wide CMEs, especially halos. Including halo CMEs from January 1996 – June 1998, St Cyr et al. (2000Jump To The Next Citation Point) found the average (median) width of LASCO CMEs was 72° (50°). Including all measured LASCO CMEs of 20 – 120° in width through 2002, Yashiro et al. (2004Jump To The Next Citation Point) found the average widths to vary, from 47° at minimum to 61° at maximum (1999), then declining again. Figure 12View Image from Gopalswamy et al. (2010aJump To The Next Citation Point) gives the updated distributions of LASCO CME speeds and widths. The average width of 41° corresponds to non-halo (width ≤ 120°) CMEs, whereas inclusion of all CMEs yields an average width of 60°. On the bottom are the speed and width distributions of all LASCO CMEs with widths > 30°. That the CACTus automatic catalog contains many more narrow CMEs is illustrated in Figure 13View Image from Robbrecht et al. (2009b). Shown on a log-log scale are the CACTus and CDAW width distributions for each year from 1997 – 2006; CACTus does not measure structures with widths below 10°.

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Figure 12: Speed and width distributions of all CMEs (top) and wider CMEs (W ≥ 30°; bottom). The average width of wider CMEs is calculated using only those CMEs with W ≥ 30°. Image reproduced with permission from Gopalswamy et al. (2010a), copyright by Springer.
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Figure 13: Apparent CME width distributions, displayed per year in log-log scale. The CACTus distribution corresponds to the red curve; the CDAW distribution is represented by the light blue curve. The distributions are not corrected for observing time. Image reproduced with permission from Robbrecht et al. (2009aJump To The Next Citation Point), copyright by IOP.

Along with their white light imaging capabilities, the benefits of polarized images have also been demonstrated with some instruments. A polarizing strip across a fixed radial was part of the C/P instrument on board SMM and polarizing capabilities were part of the Skylab and Solwind coronagraphs as well (Sheeley Jr et al., 1980; Crifo et al., 1983). Polaroid filters can help determine distances of CME material along the line of sight and, therefore, give an idea of its three-dimensional structure. This is because the Thomson scattered light that enables us to observe CMEs has a polarization degree that is dependent on the direction of observation (Billings, 1966Jump To The Next Citation Point; Howard and Tappin, 2009Jump To The Next Citation Point). In what has become two of only a few studies making use of the SOHO/LASCO polarizing capabilities, Moran and Davila (2004) and Dere et al. (2005) presented analyses of LASCO C2 polarized CME observations and showed loop arcades and filamentary structure in six CMEs. The STEREO coronagraphs provide a constant stream of polarized images enabling for the first time their regular utility for 3-D property extraction. Publications making use of this ability include Mierla et al. (2009Jump To The Next Citation Point), Moran et al. (2010), and de Koning and Pizzo (2011).

The STEREO instruments allow us to attempt to remove the projection effects using geometry, that is to use geometric triangulation on features commonly observed between observers. An early attempt to do this using LASCO and COR2 data was performed by Howard and Tappin (2008). They measured two events observed as southwest limb CMEs in LASCO observed in November 2007 when the STEREO spacecraft were each ∼ 20° from the Sun-Earth line (and ∼ 40° from each other). Figure 14View Image shows the results from a geometric localization technique, also using LASCO and COR2 data, devised by de Koning et al. (2009Jump To The Next Citation Point). Rather than attempt to perform 3-D triangulation on a series of points comprising the CME, they confine the CME to within a polygon bound by the limits of the CME’s extent. While this does not provide as much information as one may assume can be obtained with 3-D triangulation, it is actually a powerful technique, as the optical thinness of CMEs makes it nearly impossible to identify the same point in 3-D space when observing from different perspectives.

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Figure 14: The 3-D spatial location of the CME on 17 October 2008 at 14:08 UT as calculated using geometric localization. The CME appeared as a west-limb event in STEREO-A, as an east-limb event in STEREO-B, and as a halo CME by LASCO. The absence of space weather disturbances at Earth associated with the CME suggests that it was a backside halo event. The green quadrilaterals indicate the bounding volume of the CME as a whole; the blue quadrilaterals indicate the bounding volume of the leading-edge shell. The hash marks on the plots indicate the scale used; the distance between each mark is 1 R⊙. The viewing latitudes and longitudes on the plots refer to the observer’s position in HEEQ coordinates. The left plot is for an observer hovering over the west limb of the Sun; Earth is on the left-hand side of the plot. The center plot is for an observer at Earth. The right plot is for an observer looking down onto the north pole of the Sun; Earth is toward the bottom of the plot. Image reproduced with permission from de Koning et al. (2009Jump To The Next Citation Point), copyright by Springer.

Many workers have now devised geometrical techniques for determining 3-D information on CMEs, including forward modeling (e.g., Thernisien et al., 2006; Wood et al., 2009), tie-pointing (e.g., Mierla et al., 2009), and inverse reconstruction (Antunes et al., 2009). Other triangulation efforts have also been made by (for example) de Koning et al. (2009Jump To The Next Citation Point), Liewer et al. (2009), and Temmer et al. (2009). The review by Mierla et al. (2010) discusses many of these new and emerging techniques. Attempts to identify the 3-D structure using triangulation has proven to be difficult, and techniques that place the CME within a volume bound by a polygon (e.g., de Koning et al., 2009; Byrne et al., 2010; Feng et al., 2012) may have greater success.

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