Concentrated magnetic structures such as sunspots and starspots play a fundamental role in driving solar and stellar activity. However, as opposed to the sun, observations as well as numerical simulations have shown that stellar spots are usually formed as high-latitude patches extended over wide areas. Using a fully spectral magnetohydrodynamic (MHD) code, we simulate polar starspots produced by self-consistent dynamo action in rapidly rotating convective shells. We carry out high resolution simulations and investigate various properties related to stellar dynamics which lead to starspot formation.

New developments in surface flux transport modeling and theory of flux transport dynamos have given rise to the notion that certain large active regions with anomalous properties (location, tilt angle and/or Hale/non-Hale character) may have a major impact on the course of solar activity in subsequent years, impacting also on the amplitude of the following solar cycles. Here we discuss our current understanding of the role of such “rogue” active regions in cycle-to-cycle variations of solar activity.

The meridional circulation of the Sun is observationally found to vary with the solar cycle, becoming slower during the solar maxima. We explain this by constructing a theoretical model in which the equation of the meridional circulation (the φ component of the vorticity equation) is coupled with the equations of the flux transport dynamo model. We find that the Lorentz force of the dynamo-generated magnetic fields can slow down the meridional circulation during the solar maxima in broad conformity with the observations.

Forecasting the solar activity is of great importance not only for its effect on the climate of the Earth but also on the telecommunications, power lines, space missions and satellite safety. In the present work, machine learning using Artificial Neural Networks (ANNs) called Nonlinear Autoregressive Network (NAR) with Exogenous Inputs (NARX) have been applied for the prediction of future evolution of the present sunspot cycle. NARX network is able to combine the performance of ANN algorithm with nonlinear autoregressive method to handle problems such as finding dependencies among solar indices and prediction of solar cycle evolution.

Predictions of sunspot cycle are important due to their space weather effects. Bhatt et al. (2009) predicted sunspot cycle 24 (Maximum amplitude: 92.8±19.6; Timing:October 2012±4 months) using relative sunspot number (International Sunspot Number), and average geomagnetic activity index aa considering 2008 as the year of sunspot minimum. Owing to the extended solar minimum till 2009, we re-examine our prediction model. Also, the newly calibrated international sunspot number reduces many discrepancies in the old dataset and is available from Solar Influences Data Center (SIDC) website. Considering 2009 as sunspot minimum year and newly calibrated international sunspot number, (i) The annual maximum amplitude of cycle 24 = 118.5±24.4 (observed = 113.3±0.1), (ii) A smoothed monthly mean sunspot number maximum in January 2014±4 months (observed in February 2014). Our prediction method appears to be a reliable indicator for the predictability of cycle 25.

Solar activity is a chaotic process and there are various approximations to forecast its long term and short term variations. But there is no prediction method that predicts the solar activity exactly. In this study, a nonlinear prediction approach was applied to international sunspot numbers and performance of predictions was tested for the last 5 solar cycles. These predictions are in good agreement with observed values of the tested solar cycles. According to these results, end of cycle 24 is expected at February, 2020 with 7.7 smoothed monthly mean sunspot number and maximum of cyle 25 is expected at May, 2024 with 119.6 smoothed monthly mean sunspot number.

Artificial Neural Network based Nonlinear Autoregressive Model is designed to reconstruct and predict Forbush Decrease (FD) Data obtained from Izmiran, Russia. Result indicates that the model seems adequate for short term prediction of the FD data.

Sunspots are active regions on the surface of the Sun having strong magnetic fields. Activity level of the Sun shows long-time scale phenomena known as grand episodes-Grand maxima and Grand minima. Present study examines grand episodes shown by sunspot numbers (1090-2017), using methods of wavelet transform and sinusoidal regression. Time interval analysed includes two grand maxima and four grand minima. Interval in between grand episodes are regular oscillations. Phase changes found from periodicity analysis clearly show the presence of upcoming grand episodes. The forthcoming grand episodes are suggested to be two grand minima which are likely to occur between the years 2100-2160 and 2220-2300.

Here we report our recent prediction of the solar cycle 25 based on a newly developed scheme, which is used to investigate the predictability of the solar cycle over one cycle. The scheme is a combination of the empirical properties of solar cycles and a surface flux transport model to get the possible axial dipole moment evolution at a few years before cycle minimum, by which to get the subsequent cycle strength based on the correlation between the axial dipole moment at cycle minimum and the subsequent cycle strength. We apply this scheme to predict the large-scale field evolution since 2018 onwards. The results show that the northern polar field will keep on increasing, while the southern polar field almost keeps flat by the end of cycle 24. This leads to the cycle 25 strength of 125 ± 32, which is about 10% stronger than cycle 24 according to the mean value.