Related papers: A prediction for 25th solar cycle using visibility…
With recent advances in the field of machine learning, the use of deep neural networks for time series forecasting has become more prevalent. The quasi-periodic nature of the solar cycle makes it a good candidate for applying time series…
The dynamic activity of the Sun, governed by its cycle of sunspots -- strongly magnetized regions that are observed on its surface -- modulate our solar system space environment creating space weather. Severe space weather leads to…
Sunspot activity is highly variable and challenging to forecast. Yet forecasts are important, since peak activity has profound effects on major geophysical phenomena including space weather (satellite drag, telecommunications outages) and…
Solar variability occurs over a broad range of spatial and temporal scales, from the Sun's brightening over its lifetime to the fluctuations commonly associated with magnetic activity over minutes to years. The latter activity includes most…
We analysed the combined data of sunspot groups from Greenwich Photoheliographic Results (GPR) during the period 1874-1976 and Debrecen Photoheliographic Data (DPD) during 1977-2017 and determined the monthly mean, annual mean, and 13-month…
We study the sunspot activity in relation to spotless days (SLDs) during the descending phase of solar cycle $11$--$24$ to predict the amplitude of sunspot cycle $25$. For this purpose, in addition to SLD, we also use the geomagnetic…
Sunspot Cycle 25 over 3 years past the cycle minimum of December 2019. At this point, curve-fitting becomes reliable and consistently indicates a maximum sunspot number of 135+/-10 - slightly larger than Cycle 24's maximum of 116.4, but…
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…
The minimum - maximum method, belonging to the precursor class of the solar activity forecasting methods, is based on a linear relationship between relative sunspot number in the minimum and maximum epochs of solar cycles. In the present…
Solar activities have a great impact on modern high-tech systems, such as human aerospace, satellite communication and navigation, deep space exploration, and related scientific research. Therefore, studying the long - term evolution trend…
The Sun's activity cycle governs the radiation, particle and magnetic flux in the heliosphere creating hazardous space weather. Decadal-scale variations define space climate and force the Earth's atmosphere. However, predicting the solar…
Sunspot number (SSN) is an important - albeit nuanced - parameter that can be used as an indirect measure of solar activity. Predictions of upcoming active intervals, including the peak and timing of solar maximum can have important…
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 prediction of solar activity is important for advanced technologies and space activities. The peak sunspot number (SSN), which can represent the solar activity, has declined continuously in the past four solar cycles (21$-$24), and the…
Synoptic magnetograms provide us with knowledge about the evolution of magnetic fields on the solar surface and present important information for forecasting future solar activity. In this work, poloidal and toroidal magnetic field…
Solar activity cycle varies in amplitude. The last Cycle 24 is the weakest in the past century. Sun's activity dominates Earth's space environment. The frequency and intensity of the Sun's activity are accordant with the solar cycle. Hence…
Here we study the prediction of even and odd numbered sunspot cycles separately, thereby taking into account the Hale cyclicity of solar magnetism. We first show that the temporal evolution and shape of all sunspot cycles are extremely well…
Sunspot numbers form a comprehensive, long-duration proxy of solar activity and have been used numerous times to empirically investigate the properties of the solar cycle. A number of correlations have been discovered over the 24 cycles for…
We analyzed the daily sunspot-group data reported by the Greenwich Photoheliographic Results (GPR) during the period 1874-1976 and Debrecen Photoheligraphic Data (DPD) during the period 1977-2017, and the revised Version-2 of ISSN during…
Using neural networks as a prediction method, we attempt to demonstrate that forecasting of the Sun's sunspot time series can be extended to the spatial-temporal case. We employ this machine learning methodology to forecast not only in time…