Comparing SSN Index to X-Ray Flare and Coronal Mass Ejection Rates from Solar Cycles 22 – 24

Winter, L. M.; Pernak, Rick; Balasubramaniam, Krishnan

doi:10.1007/s11207-016-0901-6

Cited by 8 publications

(6 citation statements)

References 20 publications

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“…where B denotes the azimuthally averaged radial magnetic field. This formula assumes the use of the Schmidt quasi-normalization in the definition of the spherical harmonics, widely used in solar physics and geomagnetism (see, e.g., Winch et al 2005). For direct comparison of the amplitudes of harmonics of different degree, a full normalization is sometimes preferred (e.g., in DeRosa et al 2012): this results in a normalized dipole coefficientD ¼ ð4p=3Þ 1=2 D. While (12) or even (13) are often loosely referred to as the ''solar dipole moment'', it should also be kept in mind that the magnetic [dipole] moment, as normally defined in physics, is related to D as ð2pR 3 =l 0 ÞD where R is the solar radius and l 0 is the vacuum permeability.…”

Section: Polar Magnetic Field Datamentioning

confidence: 99%

Solar cycle prediction

Petrovay

2020

Living Rev Sol Phys

200

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A review of solar cycle prediction methods and their performance is given, including early forecasts for Cycle 25. The review focuses on those aspects of the solar cycle prediction problem that have a bearing on dynamo theory. The scope of the review is further restricted to the issue of predicting the amplitude (and optionally the epoch) of an upcoming solar maximum no later than right after the start of the given cycle. Prediction methods form three main groups. Precursor methods rely on the value of some measure of solar activity or magnetism at a specified time to predict the amplitude of the following solar maximum. The choice of a good precursor often implies considerable physical insight: indeed, it has become increasingly clear that the transition from purely empirical precursors to model-based methods is continuous. Model-based approaches can be further divided into two groups: predictions based on surface flux transport models and on consistent dynamo models. The implicit assumption of precursor methods is that each numbered solar cycle is a consistent unit in itself, while solar activity seems to consist of a series of much less tightly intercorrelated individual cycles. Extrapolation methods, in contrast, are based on the premise that the physical process giving rise to the sunspot number record is statistically homogeneous, i.e., the mathematical regularities underlying its variations are the same at any point of time, and therefore it lends itself to analysis and forecasting by time series methods. In their overall performance during the course of the last few solar cycles, precursor methods have clearly been superior to extrapolation methods. One method that has This article is a revised version of

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Section: Polar Magnetic Field Datamentioning

confidence: 99%

Solar cycle prediction

Petrovay

2020

Living Rev Sol Phys

200

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“…According to previous research, the number of the CMEs during SC24 is larger than SC23 (Gopalswamy et al 2015a;Winter et al 2016;Compagnino et al 2017; Bilenko 2020), the CMEs during SC24 have lower velocities (Lamy Table 1. The values of the correlation coefficients between CMEs, F10.7, and CBI, during the whole time, SC23, and SC24, respectively.…”

Section: Correlation Analysis Between Cme Occurrence Rate F107 and Cbimentioning

confidence: 77%

“…Lamy et al (2019) analyzed the difference of the CME occurrence rate between the northern and southern hemisphere and confirmed that there exists asymmetry between the two hemispheres. Gopalswamy et al (2015a); Winter et al (2016); Compagnino et al (2017); Bilenko (2020) found that the number of CMEs during Solar Cycle 24 (SC24) is larger than SC23. Bilenko (2020) noted that the high-latitude CMEs contributes more to the increase of the number of CMEs during SC24.…”

Section: Introductionmentioning

confidence: 99%

The Temporal and Spatial Behaviors of CME Occurrence Rate at Different Latitudes

Lin,

Wang,

Deng

et al. 2022

Preprint

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The statistical study of the Coronal Mass Ejections (CMEs) is a hot topic in solar physics. To further reveal the temporal and spatial behaviors of the CMEs at different latitudes and heights, we analyzed the correlation and phase relationships between the occurrence rate of CMEs, the Coronal Brightness Index (CBI), and the 10.7-cm solar radio flux (F10.7). We found that the occurrence rate of the CMEs correlates with CBI relatively stronger at high latitudes (≥ 60 • ) than low latitudes (≤ 50 • ). At low latitudes, the occurrence rate of the CMEs correlates relatively weaker with CBI than F10.7. There is a relatively stronger correlation relationship between CMEs, F10.7, and CBI during Solar Cycle 24 (SC24) than Solar Cycle 23 (SC23). During SC23, the high-latitude CME occurrence rate lags behind F10.7 by three months, and during SC24, the low-latitude CME occurrence rate leads the low-latitude CBI by one month. The correlation coefficient values turn out to be larger when the very faint CMEs are removed from the samples of the CDAW catalog. Based on our results, we may speculate that the source regions of the high/low-latitude CMEs may vary in height, and the process of magnetic energy accumulation and dissipation is from the lower to the upper atmosphere of the Sun. The temporal offsets between different indicators could help us better understand the physical processes responsible for the solar-terrestrial interactions.

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“…X-class flares are rare, varying from 0 to 20 per year, and those that cause measurable density perturbations, (X-class > X9), even more so. With an occurrence rate of~2 × 10 À3 per day (Winter et al, 2016), we expect only a handful (~8) of X9+ events per solar cycle. This may be a reason for the relative scarcity of studies on the thermospheric response to flares.…”

Section: 1002/2017sw001725mentioning

confidence: 97%

EUV Irradiance Inputs to Thermospheric Density Models: Open Issues and Path Forward

Vourlidas

Bruinsma²

2018

Space Weather

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One of the objectives of the NASA Living With a Star Institute on “Nowcasting of Atmospheric Drag for low Earth orbit (LEO) Spacecraft” was to investigate whether and how to increase the accuracy of atmospheric drag models by improving the quality of the solar forcing inputs, namely, extreme ultraviolet (EUV) irradiance information. In this focused review, we examine the status of and issues with EUV measurements and proxies, discuss recent promising developments, and suggest a number of ways to improve the reliability, availability, and forecast accuracy of EUV measurements in the next solar cycle.

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Comparing SSN Index to X-Ray Flare and Coronal Mass Ejection Rates from Solar Cycles 22 – 24

Cited by 8 publications

References 20 publications

Solar cycle prediction

Solar cycle prediction

The Temporal and Spatial Behaviors of CME Occurrence Rate at Different Latitudes

EUV Irradiance Inputs to Thermospheric Density Models: Open Issues and Path Forward

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