Results 21 to 30 of about 164,385 (278)
Predicting Solar Flares with Machine Learning: Investigating Solar Cycle Dependence [PDF]
The Astrophysical Journal, 2020 Abstract A deep learning network, long short-term memory (LSTM), is used to predict whether an active region (AR) will produce a flare of class Γ in the next 24 hr. We consider Γ to be ≥M (strong flare), ≥C (medium flare), and ≥A (any flare) class.
Xiantong Wang +9 more
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Prediction of solar activity on the basis of spectral characteristics of sunspot number [PDF]
Annales Geophysicae, 2004 Prediction of solar activity strength for solar cycles 23 and 24 is performed on the basis of extrapolation of sunspot number spectral components. Sunspot number data during 1933-1996 periods (solar cycles 17-22) are searched for periodicities by ...
E. Echer +4 more
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Predictability of the Solar Cycle Over One Cycle
The Astrophysical Journal, 2018 Abstract The prediction of the strength of future solar cycles is of interest because of its practical significance for space weather and as a test of our theoretical understanding of the solar cycle. The Babcock–Leighton mechanism allows predictions by assimilating the observed magnetic field on the surface.
Jie Jiang +3 more
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Research on the Effect of Data Assimilation for Three‐Dimensional MHD Simulation of Solar Wind
Space Weather, 2023 As an important part of space weather forecasting, the prediction of solar wind parameters in the near‐Earth space is particularly significant. The introduction of data assimilation (DA) method can improve the reliability of numerical prediction. In this
Hanke Zhang, Fang Shen, Yi Yang
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Neural network prediction of solar cycle 24 [PDF]
Research in Astronomy and Astrophysics, 2011 The ability to predict the future behavior of solar activity has become of extreme importance due to its effect on the near Earth environment. Predictions of both the amplitude and timing of the next solar cycle will assist in estimating the various consequences of Space Weather. The level of solar activity is usually expressed by international sunspot
Ajabshirizadeh, A. +2 more
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Prediction Methods in Solar Sunspots Cycles [PDF]
Scientific Reports, 2016 AbstractAn understanding of the Ohl’s Precursor Method, which is used to predict the upcoming sunspots activity, is presented by employing a simplified movable divided-blocks diagram. Using a new approach, the total number of sunspots in a solar cycle and the maximum averaged monthly sunspots number Rz(max) are both shown to be statistically related to
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Fractal Dimension and Maximum Sunspot Number in Solar Cycle [PDF]
Journal of Astronomy and Space Sciences, 2006 The fractal dimension is a quantitative parameter describing the characteristics of irregular time series. In this study, we use this parameter to analyze the irregular aspects of solar activity and to predict the maximum sunspot number in the following ...
R.-S. Kim +4 more
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Physical Models for Solar Cycle Predictions
Space Science Reviews, 2023 AbstractThe dynamic activity of stars such as the Sun influences (exo)planetary space environments through modulation of stellar radiation, plasma wind, particle and magnetic fluxes. Energetic solar-stellar phenomena such as flares and coronal mass ejections act as transient perturbations giving rise to hazardous space weather.
Prantika Bhowmik +4 more
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Impact of nonlinear surface inflows into activity belts on the solar dynamo
Journal of Space Weather and Space Climate, 2020 We examine the impact of surface inflows into activity belts on the operation of solar cycle models based on the Babcock–Leighton mechanism of poloidal field regeneration.
Nagy Melinda +2 more
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On solar cycle predictions and reconstructions [PDF]
Astronomy & Astrophysics, 2009 Generally, there are two procedures for solar cycle predictions: The empirical methods -- statistical methods based on extrapolations and precursor methods -- and methods based on dynamo models. The goal of the present analysis is to forecast the strength and epochs of the next solar cycle, to investigate proxies for grand solar minima and to ...
Brajsa, R. +7 more
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