Differences between the CME fronts tracked by an expert, an automated algorithm, and the Solar Stormwatch project

Barnard, Luke; Scott, Chris J.; Owens, M. J.; Lockwood, Mike; Crothers, S. R.; Davies, J. A.; Harrison, R. A.

doi:10.1002/2015sw001280

Cited by 17 publications

(21 citation statements)

References 31 publications

(53 reference statements)

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“…It is possible that experts may outperform the citizen science approach for complex events. However, we note that in an earlier version of Solar Stormwatch, which characterized CMEs in J-maps, there was no evidence that the citizen science approach was inferior to expert identification at characterizing complex or faint events [Barnard et al, 2014[Barnard et al, , 2015a.…”

Section: Development Of the Solar Stormwatch "Storm Front" Elongationmentioning

confidence: 99%

“…Setting this parameter to zero replicates the FP geometry while setting it to 90 ∘ replicates the HM geometry; intermediate values approximate the CME cross section as a self-similar expanding circle of constant half width (as discussed by Davies et al [2012]). As a result, some studies have set the half width to a value that represents a compromise between the two extremes; Möstl et al [2014] used 45 ∘ , Rollett et al [2016] used 35 ∘ , while Barnard et al [2015a] and Mishra et al [2014] used 30 ∘ . Such techniques can be applied to CMEs observed from a single spacecraft or to simultaneous observations from two spacecraft with distinct viewpoints [Lugaz, 2010;Liu et al, 2010Liu et al, , 2013.…”

Section: 1002/2017sw001609mentioning

confidence: 99%

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Testing the current paradigm for space weather prediction with heliospheric imagers

et al. 2017

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Predictions of the arrival of four coronal mass ejections (CMEs) in geospace are produced through use of three CME geometric models combined with CME drag modeling, constraining these models with the available Coronagraph and Heliospheric Imager data. The efficacy of these predications is assessed by comparison with the Space Weather Prediction Center (SWPC) numerical MHD forecasts of these same events. It is found that such a prediction technique cannot outperform the standard SWPC forecast at a statistically meaningful level. We test the Harmonic Mean, Self‐Similar Expansion, and Ellipse Evolution geometric models, and find that, for these events at least, the differences between the models are smaller than the observational errors. We present a new method of characterizing CME fronts in the Heliospheric Imager field of view, utilizing the analysis of citizen scientists working with the Solar Stormwatch project, and we demonstrate that this provides a more accurate representation of the CME front than is obtained by experts analyzing elongation time maps for the studied events. Comparison of the CME kinematics estimated independently from the STEREO‐A and STEREO‐B Heliospheric Imager data reveals inconsistencies that cannot be explained within the observational errors and model assumptions. We argue that these observations imply that the assumptions of the CME geometric models are routinely invalidated and question their utility in a space weather forecasting context. These results argue for the continuing development of more advanced techniques to better exploit the Heliospheric Imager observations for space weather forecasting.

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Section: Development Of the Solar Stormwatch "Storm Front" Elongationmentioning

confidence: 99%

Section: 1002/2017sw001609mentioning

confidence: 99%

Testing the current paradigm for space weather prediction with heliospheric imagers

et al. 2017

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“…These kinematic profiles have been used to study the physics of CME evolution (Mishra et al., 2012; Mishra et al., 2014; Harrison et al., 2012; Rollett et al., 2012), and also as a component of models to forecast CME arrival at Earth, for example ELEvoHI (Rollett et al., 2016; Amerstorfer et al., 2018). However, there are significant uncertainties associated with this CME geometric modeling framework, relating to both the processing of the HI observations (Williams et al., 2009; Barnard et al., 2015), and assumptions of the models (Barnard et al., 2017).…”

Section: Introductionmentioning

confidence: 99%

Quantifying the Uncertainty in CME Kinematics Derived From Geometric Modeling of Heliospheric Imager Data

et al. 2022

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Geometric modeling of Coronal Mass Ejections (CMEs) is a widely used tool for assessing their kinematic evolution. Furthermore, techniques based on geometric modeling, such as ELEvoHI, are being developed into forecast tools for space weather prediction. These models assume that solar wind structure does not affect the evolution of the CME, which is an unquantified source of uncertainty. We use a large number of Cone CME simulations with the HUXt solar wind model to quantify the scale of uncertainty introduced into geometric modeling and the ELEvoHI CME arrival times by solar wind structure. We produce a database of simulations, representing an average, a fast, and an extreme CME scenario, each independently propagating through 100 different ambient solar wind environments. Synthetic heliospheric imager observations of these simulations are then used with a range of geometric models to estimate the CME kinematics. The errors of geometric modeling depend on the location of the observer, but do not seem to depend on the CME scenario. In general, geometric models are biased towards predicting CME apex distances that are larger than the true value. For these CME scenarios, geometric modeling errors are minimised for an observer in the L5 region. Furthermore, geometric modeling errors increase with the level of solar wind structure in the path of the CME. The ELEvoHI arrival time errors are minimised for an observer in the L5 region, with mean absolute arrival time errors of 8.2 ± 1.2 h, 8.3 ± 1.0 h, and 5.8 ± 0.9 h for the average, fast, and extreme CME scenarios.

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“…This method allows the quantitative estimate of the uncertainty in computed CME properties, which can be found from the distributions of estimates provided by the great number of volunteers taking part. Data from the Solar Stormwatch project have already been used to create a catalogue of CMEs from the SECCHI HI imagers on the STEREO spacecraft [ Barnard et al , , ]. The Solar Stormwatch approach is described in section 2.1.…”

Section: Introductionmentioning

confidence: 99%

Tracking CMEs using data from the Solar Stormwatch project; observing deflections and other properties

et al. 2017

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With increasing technological dependence, society is becoming ever more affected by changes in the near‐Earth space environment caused by space weather. The primary driver of these hazards are coronal mass ejections (CMEs). Solar Stormwatch is a citizen science project in which volunteers participated in several activities which characterized CMEs in the remote sensing images from the Sun Earth Connection Coronal and Heliospheric Investigation (SECCHI) instrument package on the twin STEREO spacecraft. Here we analyze the results of the “Track‐it‐back” activity, in which CMEs were tracked back through the COR1, COR2, and EUVI images. Analysis of the COR1, COR2, and EUVI data together allows CMEs to be studied consistently throughout the whole field of view spanned by these instruments (out to 15 RS). A total of 4783 volunteers took part in this activity, creating a data set containing 23,801 estimates of CME timing, location, and size. We used these data to produce a catalogue of 41 CMEs, which is the first to consistently track CMEs through each of these instruments. We assess how the CME speeds, propagation directions, and widths vary as the CMEs propagate through the fields of view of the different imagers. In particular, we compare the observed CME deflections between the COR1 and COR2 fields of view to the separation between the CME source region and the heliospheric current sheet (HCS), demonstrating that in general, these CMEs appear to deflect toward the HCS, consistent with other modeling studies of CME propagation.

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Differences between the CME fronts tracked by an expert, an automated algorithm, and the Solar Stormwatch project

Cited by 17 publications

References 31 publications

Testing the current paradigm for space weather prediction with heliospheric imagers

Testing the current paradigm for space weather prediction with heliospheric imagers

Quantifying the Uncertainty in CME Kinematics Derived From Geometric Modeling of Heliospheric Imager Data

Tracking CMEs using data from the Solar Stormwatch project; observing deflections and other properties

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