Equatorial coronal holes, solar wind high-speed streams, and their geoeffectiveness

Verbanac, Giuliana; Vršnak, Bojan; Veronig, Astrid; Temmer, Manuela

doi:10.1051/0004-6361/201014617

Cited by 63 publications

(51 citation statements)

References 38 publications

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“…The distribution of the solar wind is also decisive for deriving the propagation behavior of CMEs . The relationship between the size and location of CHs in the corona and the solar wind parameters and geomagnetic effects measured at 1 AU has been investigated by several groups (Nolte et al, 1976;Robbins, Henney, and Harvey, 2006;Vršnak, Temmer, and Veronig, 2007a,b;Luo et al, 2008;Abramenko, Yurchyshyn, and Watanabe, 2009;Obridko et al, 2009;de Toma, 2011;Verbanac et al, 2011;Lowder et al, 2014). In the early phases of coronal holes identification, CHs were visually tracked by experienced observers (Harvey and Recely, 2002;McIntosh, 2003).…”

Section: Introductionmentioning

confidence: 99%

Real-Time Solar Wind Prediction Based on SDO/AIA Coronal Hole Data

et al. 2015

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We present an empirical model based on the visible area covered by coronal holes close to the central meridian in order to predict the solar wind speed at 1 AU with a lead time up to four days in advance with a 1-h time resolution. Linear prediction functions are used to relate coronal hole areas to solar wind speed. The function parameters are automatically adapted by using the information from the previous 3 Carrington Rotations. Thus the algorithm automatically reacts on the changes of the solar wind speed during different phases of the solar cycle. The adaptive algorithm has been applied to and tested on SDO/AIA-193Å observations and ACE measurements during the years 2011 − 2013, covering 41 Carrington Rotations. The solar wind speed arrival time is delayed and needs on average 4.02 ± 0.5 days to reach Earth. The algorithm produces good predictions for the 156 solar wind high speed streams peak amplitudes with correlation coefficients of cc ≈ 0.60. For 80% of the peaks, the predicted arrival matches within a time window of 0.5 days of the ACE in situ measurements. The same algorithm, using linear predictions, was also applied to predict the magnetic field strength from coronal hole areas but did not give reliable predictions (cc ≈ 0.2).

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Section: Introductionmentioning

confidence: 99%

Real-Time Solar Wind Prediction Based on SDO/AIA Coronal Hole Data

et al. 2015

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“…ac.jp/dstdir/, and http://wdc.kugi.kyoto-u.ac.jp/ dstae/index.html, respectively. For more detailed information about the indices, we refer to Prolss (2004) and Verbanac et al (2011aVerbanac et al ( , 2010. Among the available geomagnetic indices, these three indices have been chosen for the evaluation of the geomagnetic activity since their variations can be physically interpreted and related to the specific current system, for example, the ring current and polar electrojet.…”

Section: Data Sets and Methodsmentioning

confidence: 99%

“…The geoeffectiveness of ICMEs and HSS/CIRs has been studied by many researchers (e.g., Schwenn et al 2005;Huttunen et al 2005;Georgieva et al 2006;Lavraud et al 2006;Alves et al 2006;Richardson et al 2006;Yermolaev & Yermolaev 2006;Vršnak et al 2007;Zhang et al 2007b;Echer et al 2008;Verbanac et al 2011a;Richardson & Cane 2010Verbanac et al 2011b, and references therein). In most of these studies, the level of the geomagnetic activity is commonly quantified with the storm-time disturbance index Dst, which is a relatively good measure of the ring current strength Wang et al 2003;Kane 2005;Alves et al 2006;Bakare & Chukwuma 2010).…”

Section: Introductionmentioning

confidence: 99%

“…Moreover, the observed Dst has contributions not only from the ring current, but also from magnetopause currents, tail currents, induced currents in the solid Earth, and other sources not yet fully quantified (e.g., Gonzalez et al 1994;Verbanac et al 2007). Verbanac et al (2011a and b), hereafter Paper I and Paper II, studied the geoeffectiveness of HSS/CIRs during the declining phase of the solar cycle 23, by employing various geomagnetic indices. In this paper we expand the analysis focusing on a detailed comparison of the geomagnetic responses to ICMEs and HSS/CIRs.…”

Section: Introductionmentioning

confidence: 99%

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Comparison of geoeffectiveness of coronal mass ejections and corotating interaction regions

Verbanac

Živković

Vršnak

et al. 2013

A&A

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Context. A detailed comparison of the geomagnetic responses to interplanetary coronal mass ejection (ICMEs) and corotating interaction regions (CIRs) during solar cycle 23 was performed using geomagnetic indices Dst, Ap, and AE. Aims. We aim to find out if there are relative differences in the response of various magnetospheric current systems to the impact of ICMEs and CIRs. In addition, we are exploring the possibility of forecasting geomagnetic activity using the coronagraphic observations of the ICME take-off. Methods. The peak values of the plasma characteristics of ICMEs and CIRs (velocity V, magnetic field B, and BV related to the electric field), and geomagnetic indices were investigated by applying the linear and power-law cross correlation analysis. The influence of the time-resolution on the results was performed for two time resolutions obtained by one-hour (three-hour for Ap) and six-hour data averaging. Results. For ICMEs the power-law fits are found to be important only for the relationships between BV and geomagnetic indices. For Ap and Dst, there is no difference between the one-hour (three-hour for Ap) and six-hour option. For AE, the one-hour data distribution shows more clearly the non-linear dependence on BV. Our data set shows that below BV ∼ 5 mV m −1 ICMEs have practically no geomagnetic effect at low and mid latitudes, but at high latitudes at least some geomagnetic activity will be triggered. For all HSS/CIRs dependencies, a power law is found to better describe the data than the linear fit. The data distributions show that BV has to reach ∼4 mV m −1 in order to drive at least some geomagnetic activity at all latitudes. We observed that there are fast CMEs that have almost no geomagnetic effect at low and mid latitudes. On the other hand, at high latitudes, fast CMEs always trigger some geomagnetic activity. This might be have implications for space weather forecasting. Conclusions. Magnetospheric response to both solar drivers (ICMEs and CIRs) is different at various latitudes, thus results in different development of various current systems within the Earth's magnetosphere and ionosphere. Furthermore, we show that ICMEs and CIRs cause different geomagnetic activity. In the case of ICMEs equatorial current system responses in a linear manner, while the response of the polar-current system is likely to be non-linear. For HSS/CIRs, apparently all current systems respond in a non-linear way, especially the polar current system.

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“…A second key idea of SVM as presented by Vapnick in his original formulation (Vapnick 1998) is to map the feature vectors in a nonlinear way to a high (possibly infinite) dimensional space and then utilize linear classifiers in this new space. This is done through the use of a kernel function.…”

Section: Support Vector Machinementioning

confidence: 99%

Improvements on coronal hole detection in SDO/AIA images using supervised classification

Reiss

Hofmeister

Visscher

et al. 2015

J. Space Weather Space Clim.

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We demonstrate the use of machine learning algorithms in combination with segmentation techniques in order to distinguish coronal holes and filaments in SDO/AIA EUV images of the Sun. Based on two coronal hole detection techniques (intensity-based thresholding, SPoCA), we prepared datasets of manually labeled coronal hole and filament channel regions present on the Sun during the time range 2011-2013. By mapping the extracted regions from EUV observations onto HMI line-of-sight magnetograms we also include their magnetic characteristics. We computed shape measures from the segmented binary maps as well as first order and second order texture statistics from the segmented regions in the EUV images and magnetograms. These attributes were used for data mining investigations to identify the most performant rule to differentiate between coronal holes and filament channels. We applied several classifiers, namely Support Vector Machine (SVM), Linear Support Vector Machine, Decision Tree, and Random Forest, and found that all classification rules achieve good results in general, with linear SVM providing the best performances (with a true skill statistic of % 0.90). Additional information from magnetic field data systematically improves the performance across all four classifiers for the SPoCA detection. Since the calculation is inexpensive in computing time, this approach is well suited for applications on real-time data. This study demonstrates how a machine learning approach may help improve upon an unsupervised feature extraction method.

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Equatorial coronal holes, solar wind high-speed streams, and their geoeffectiveness

Cited by 63 publications

References 38 publications

Real-Time Solar Wind Prediction Based on SDO/AIA Coronal Hole Data

Real-Time Solar Wind Prediction Based on SDO/AIA Coronal Hole Data

Comparison of geoeffectiveness of coronal mass ejections and corotating interaction regions

Improvements on coronal hole detection in SDO/AIA images using supervised classification

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