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The Sun exhibits episodic surges of magnetic activity across a range of temporal and spatial scales, the most prominent of which is the 11-th year modulation of sunspot production. Beside the ∼170 (min to max) decadal variation in sunspot production there is a less-explored quasi-annual variation in the range of 25-50 sunspots/year in magnitude. In addition, there is there is a slower, ∼80 year period, 10-50 variation in the sunspot number, that is commonly referred to as the "Gleissberg Cycle." Using a suite of contemporary and historical observations we will illustrate these elements of our star's episodic behavior and present a hypothesis that may provide a consistent physical link between the observed "climatic," "decadal," and "seasonal" magnetic variation of our star. [ABSTRACT FROM AUTHOR]

Effective predicting sunspot numbers (SSN) is the complex task of studying space weather, solar activity, satellite communication, and Earth's climate. Developing a reliable SSN forecasting model is difficult because SSN time series exhibit complex patterns, nonlinearity, and nonstationarity characteristics. The state-of-the-art shows that deep-learning models often need help capturing SSN data's intricate dynamics and long-term dependencies. The SSN time series' decomposed trend and seasonal and residual characteristics may provide better information on long-term dependencies and associated dynamics for effective learning. In this research, the vipin-deep-decomposed-recomposed rolling-window (vD2R2w) models have been proposed with a combination of time-series decomposition, deep-learning models, and a rolling-window method to predict the SSN accurately. The proposed vD2R2w models have been evaluated over four datasets and consistently outperform traditional deep-learning models. The model improves the performance in terms of RMSE, MAPE, and R 2 over the datasets as SSN_Daily: 84.18% (RMSE), 10.38% (MAPE), and 3.504% ( R 2 ); SSN_Monthly: 39.5% (RMSE), 26.06% (MAPE), and 7.258% ( R 2 ); SSN_MonthlyMean: 178.32% (RMSE), 54.83% (MAPE), and 1.56% ( R 2 ); and SSN_Yearly: 6.06% (RMSE), 10.36% (MAPE), and 1.366% ( R 2 ). Further, the superiority of the vD2R2w models is validated through AIC & BIC, Diebold Mariano test, and Friedman ranking statistical tests. Additionally, the vD2R2w model has forecasted the peak value of Solar Cycles (SC) and time, i.e., SC25: 127.16 (± 6.83) in 2025 and SC26: 191.71 (± 43.37) in 2035. The analysis of proposed model performances and statistical validation over various measures with four SSNs have concluded that the vD2R2w model outperforms the traditional models and is a reliable framework for SSN time series forecasting. Implementing the proposed model may benefit domains such as space-weather monitoring, satellite communication planning, and solar energy forecasting that rely on accurate SSN predictions. [ABSTRACT FROM AUTHOR]

In this research paper we investigated the halo coronal mass ejections and sunspot number during the period December 2019 to December 2023 for the rising phase of solar cycle 25. The SOHO/LASCO instrument observed 145 HCMEs during this period. We selected 145 halo CMEs and the associated flares, SSN and Dst. The HCME is found to be significantly faster and more energetic than the other CME. During solar maximum, HCME occur more frequently. Here we have considered all the frontside disk halos, frontside limb halos and the backside halos of the sun. These halos are classified according to their source location. Mostly the frontside disk halos are more geoeffective. For 70% (7/10) of the halo CMEs associated with X class flares have a speed greater than 1000 km/s. The daily sunspot number (SSN) and the yearly mean total sunspot number are taken into consideration for our study. The study of sunspot numbers is essential for planning space-related activities, particularly for low Earth orbiting spacecraft. Geoeffectiveness is the capacity of HCMEs to induce geomagnetic storms. The "Dst (Disturbance storm time) index" is one geomagnetic metric that is used to measure this. We categorized geoeffective halos into three groups: strongly geoeffective (Strong-GE, Dst < −100 nT), moderately geoeffective (Moderate-GE, −100 < Dst ≤ −50 nT), and weakly geoeffective (Weak-GE, Dst > −50 nT). Only 3.44% of halo CMEs were strongly geoeffective, and all of these were disk halos. The NW quadrant has the highest number of events throughout the research period of December 2019–December 2023. In this paper, we illustrate how the parameters of halo coronal mass ejections, sunspots and Dst can be used for space weather effects, evaluating geoeffectiveness, and auroras. [ABSTRACT FROM AUTHOR]

Interplanetary (IP) shocks are perturbations observed in the solar wind. IP shocks correlate well with solar activity, being more numerous during times of high sunspot numbers. Earth‐bound IP shocks cause many space weather effects that are promptly observed in geospace and on the ground. Such effects can pose considerable threats to human assets in space and on the ground, including satellites in the upper atmosphere and power infrastructure. Thus, it is of great interest to the space weather community to (a) keep an accurate catalog of shocks observed near Earth, and (b) be able to forecast shock occurrence as a function of the solar cycle (SC). In this work, we use a supervised machine learning regression model to predict the number of shocks expected in SC25 using three previously published sunspot predictions for the same cycle. We predict shock counts to be around 275 ± 10, which is ∼47% higher than the shock occurrence in SC24 (187 ± 8), but still smaller than the shock occurrence in SC23 (343 ± 12). With the perspective of having more IP shocks on the horizon for SC25, we briefly discuss many opportunities in space weather research for the remainder years of SC25. The next decade or so will bring unprecedented opportunities for research and forecasting effects in the solar wind, magnetosphere, ionosphere, and on the ground. As a result, we predict SC25 will offer excellent opportunities for shock occurrences and data availability for conducting space weather research and forecasting. Plain Language Summary: Solar activity is quite correlated with sunspot numbers. Alternating periods between solar minima and minima, termed solar cycle, usually occur every ∼11 years. As a result, researchers often attempt to predict sunspot occurrences for the following solar cycle. Solar perturbations occur more frequently during periods of high solar activity, and Earth‐bound perturbations can disturb the Earth's magnetic field in geospace and on the ground, affecting satellites and power infrastructure. In this work, we use an artificial intelligence supervised model to predict the number of shock occurrences in the ongoing solar cycle (beginning December 2019) by training the model with observations of sunspots and solar perturbations in the previous two solar cycles (August 1996–December 2019). Then, sunspot number predictions for the ongoing solar cycle are applied to the model, and predictions for the solar perturbations are obtained. We find that the number of predicted solar perturbations is ∼50% higher than their occurrence number in the previous solar cycle (December 2008–December 2019). Finally, we discuss how this relatively higher number of predicted solar perturbations can impact space weather research given the unprecedented number of data sets available in geospace and on the ground in the upcoming years. Key Points: SSN and shock count data in SC23‐24 are used with SSN predictions for SC25 in a supervised regression model to estimate shock counts in SC25We predict SC25 (275) will have ∼48% more shocks in comparison to SC24 (187), but it will have fewer shocks in comparison to SC23 (343)SC25 will offer unprecedented opportunities for space weather research given the availability of many data sets from space to the ground [ABSTRACT FROM AUTHOR]

High-resolution thermospheric mass density low Earth orbit (LEO)-based measurements are valuable for accurately estimating short-term atmospheric abrupt disturbances triggered by solar flux forcing. To investigate the enhancing status of solar cycle 25 between August 29 and December 31, 2020, we processed Gravity Recovery and Climate Experiment Follow-on (GRACE-FO) 10-s accelerometer-derived thermospheric mass density (TMD) measurements normalized at 500 km altitude by the NRLMSIS-2.0 empirical model. These 4-month enhancing disturbance observations suggest a shift from relative quiescence to a much more active solar phase, revealing unexpected dependencies on temporal and spatial characteristics. The results indicated that the dominant driver is solar extreme ultraviolet radiation (EUV) during this ascending phase. Density enhancement was symmetric in both hemispheres around the autumn equinox. After the equinox, the neutral density enhancement intensity in the Southern Hemisphere surpasses that in the Northern Hemisphere. Density maxima occurred from high to low latitudes, accompanied by a 2–3 h time lag. The Wygant function was applied to model the response to solar wind geomagnetic field changes and quantify the impact of geomagnetic activities on upper atmosphere density, verifying the time lag of density disturbances. All these findings could potentially improve our understanding of the solar cycle and LEO orbital drag. [ABSTRACT FROM AUTHOR]

Abstract Interplanetary (IP) shocks are perturbations observed in the solar wind. IP shocks correlate well with solar activity, being more numerous during times of high sunspot numbers. Earth‐bound IP shocks cause many space weather effects that are promptly observed in geospace and on the ground. Such effects can pose considerable threats to human assets in space and on the ground, including satellites in the upper atmosphere and power infrastructure. Thus, it is of great interest to the space weather community to (a) keep an accurate catalog of shocks observed near Earth, and (b) be able to forecast shock occurrence as a function of the solar cycle (SC). In this work, we use a supervised machine learning regression model to predict the number of shocks expected in SC25 using three previously published sunspot predictions for the same cycle. We predict shock counts to be around 275 ± 10, which is ∼47% higher than the shock occurrence in SC24 (187 ± 8), but still smaller than the shock occurrence in SC23 (343 ± 12). With the perspective of having more IP shocks on the horizon for SC25, we briefly discuss many opportunities in space weather research for the remainder years of SC25. The next decade or so will bring unprecedented opportunities for research and forecasting effects in the solar wind, magnetosphere, ionosphere, and on the ground. As a result, we predict SC25 will offer excellent opportunities for shock occurrences and data availability for conducting space weather research and forecasting.

• Hemispheric prediction of solar cycle 25 has been done using sunspot number data. • Deep Learning algorithm has been used for the purpose of prediction. • Method of Expanding widow has been used for cross-validation. • Validation of model performance has been done by predicting past solar cycles 20–24. • Developed model achieves a high degree of accuracy in hemispheric prediction of cycle 25. In the current work, single-and double-layered Long Short-Term Memory (LSTM) model have been developed and compared to find the most suitable one for hemispheric prediction of solar cycle 25. The smoothed Hemispheric Sunspot Number (HSN) reconstructed by Veronig et al. (2021) has been used to achieve the objective. The deep learning models are evaluated over different pairs of train and test set for both the hemispheres using the 5-fold cross-validation scheme, to determine the best effective model for hemispheric prediction of cycle 25. Our analysis reveals that the single-layered LSTM model achieves lower RMSE when predicting the test dataset. To prove the reliability of our suggested LSTM models, we have predicted the hemispheric profiles of solar cycles 21–24. From our analysis, we found that the single-layered LSTM model performed better in comparison to the double-layered LSTM model in predicting past solar cycles 21–24. Hence, hemispheric prediction of solar cycle 25 is carried out utilizing the single-layered LSTM model due to better performance capability during experimental analysis. This work founds that the northern hemisphere will peak with an amplitude of 56 ± 5 during July 2023 + 3 months while the southern hemisphere will have a maximum amplitude of 54 ± 4 during June 2023 + 2 months. So, it can be said that the southern HSN will peak before the northern HSN and that the peak of northern HSN will be slightly higher than the southern one. [ABSTRACT FROM AUTHOR]

In this paper, an optimized long short-term memory (LSTM) model was proposed to deal with the monthly sunspot area (SSA) data, aiming to predict the peak amplitude of SSA and the occurring time for Solar Cycle 25, and obtain the forecasting maximum value of sunspot number (SSN) and the reaching time to maximum. The "re-prediction" process in LSTM + was employed with the latest prediction results obtained from the previous prediction as the input for the next prediction calculation. The accuracy and reliability of LSTM + were performed by the validation of the predicted peak amplitude and occurring time of Solar Cycles 21 to 24. The prediction error between the predicted and the observed peak amplitude for cycles 21 to 24 were 8.85 %, 4.49 %, 2.88 %, and 4.57 %, respectively, and the error for the occurring time for 4 cycles was all within 6 months. The predicted peak amplitude of SSA for Solar Cycle 25 was obtained as 2562.5 with LSTM+, and the maximum value of SSN was calculated as 213 based on the relation between SSA and SSN, which would be stronger than that of Solar Cycle 24. And according to the prediction result, Solar Cycle 25 would reach the peak around January 2025. [ABSTRACT FROM AUTHOR]

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A deep-learning Vanilla, or single layer, Long Short-Term Memory model is proposed for improving the prediction of Solar Cycle 25. WDC-SILSO the Royal Observatory of Belgium, Brussels provides the 13-month smoothed sunspot-number data that were used to make this prediction. The root mean square error (RMSE) obtained by the proposed model, which is improved in comparison to the existing stacked LSTM model, lies within the range of 1.65 – 4.92, according to analysis on a number of temporal intervals taken into consideration in this study. The model performance has been validated by forecasting the peak amplitude of Solar Cycles 21 – 24. It is shown that for Cycles 21 and 22, the prediction error in estimating the peak is 1.159% and 0.423%, while the RMSE is estimated to be 4.149 and 3.274, respectively. For Cycle 23, the relative error and RMSE are 1.054% and 2.985, respectively, whereas for Cycle 24 they are 1.117% and 3.406, respectively. The current proposed model has exactly predicted the timing when the SSN reached its maximum for Cycle 23. While for Cycle 21, the prediction has a 1-month delay from the actual timing. For Cycles 22 and 24, the year during which the SSN reached maximum coincides with the observed year, although their month of peak occurrence showed a difference of three months and one month, respectively. The current proposed model suggests that the Cycle 25 will peak in April 2023 with an amplitude value of 136.9, which will be approximately 17.68% stronger compared with Cycle 24. [ABSTRACT FROM AUTHOR]

The sunspot number is the most used solar-activity index to study the behavior of solar activity. In this work, we reproduce the methodology of Coban, Raheem, and Cavus (Solar Phys. 296, 156, 2021) using a long short-term memory model with daily data from the American Association of Variable Star Observers (AAVSO) to predict the maximum amplitude of Solar Cycle 25. We have also used that same methodology with daily values from the official sunspot number (Version 2) of the Sunspot Index and Long-term Solar Observations (SILSO). The objective of this work is to analyze if the predictions obtained from that methodology agree with the observed values available for the current Solar Cycle 25. Thus, we conclude that the predictions are not reproducing well the behavior of the Solar Cycle 25 in its rising phase. Moreover, contrary to the previous prediction, no minor peak occurred in February 2022, and we also conclude that it seems unlikely that the combination of the solar-activity level of Solar Cycle 24 and 25 constitutes a new Dalton-type Minimum, such as Coban, Raheem, and Cavus (2021) proposed. [ABSTRACT FROM AUTHOR]

In this paper, an optimized long short-term memory (LSTM) model is proposed to deal with the smoothed monthly F 10.7 data, aiming to predict the peak amplitude of F 10.7 and the occurring time for Solar Cycle 25 (SC-25) to obtain the maximum amplitude of sunspot number (SSN) and the reaching time. The "re-prediction" process in the model uses the latest prediction results obtained from the previous prediction as the input for the next prediction calculation. The prediction errors between the predicted and observed peak amplitude of F 10.7 for SC-23 and SC-24 are 2.87% and 1.09%, respectively. The predicted peak amplitude of F 10.7 for SC-25 is 156.3, and the maximum value of SSN is calculated as 147.9, which implies that SC-25 will be stronger than SC-24. SC-25 will reach its peak in July 2025. [ABSTRACT FROM AUTHOR]

In this research, the plasma parameters of the ionospheric F-layer have been calculated during daytime at 12 PM condition for four different months (January, April, July and October) over Iraqi capital “ Baghdad” (44.3° E, 33.3° N) during the year 2019 that represents the beginning of the solar cycle 25. Depending on the impact of the solar activity that was represented by the number of sunspot (SSN) and solar flux (F10.7 cm) on the variation and behavior of the electron density (ne) and the electron temperature (Te) which have been conducted using the international reference ionosphere (IRI 2016) model which was considered as one of the recommended international models for different heights (150 500) km with 50 km increment. It has been noticed that the seasonal variations of electron density (ne) and frequency (fpe) around the equinoxes are higher than those in summer and winter for solar minimum (2019).While λDe around the equinoxes (April and October) is lower than that of summer and winter for solar minimum (2019). It has been explained as a positive correlation between the thermal velocity of electron (υte) and (Te) in all seasonal variation of ionosphere for the selected year. In this paper, the weakness of the electron coupling parameter in the ionosphere was proved in all seasons of the year 2019. [ABSTRACT FROM AUTHOR]

• Peak & timing of solar cycle 25 has been predicted using sunspot number data. • The concept of Deep Learning algorithm has been used for forecasting. • The proposed model showed good performance on various time intervals with low RMSE value. • Validation of model performance has been done by predicting past solar cycles 20–24. • The model captured non-linearity and cyclic trend within the data with good RMSE. In the current work we have used the deep learning based long short-term memory model to predict the strength and peak time of solar cycle 25 by employing the monthly smoothed sunspot number data obtained from WDC-SILSO, Royal Observatory of Belgium, Brussels. We have used the stacked LSTM forecasting model to predict the upcoming cycle 25. From our analysis it has been shown that our proposed model is capable of capturing long term dependencies as well as trend within the data. For cycle 20 and 21 the error difference between predicted as well as observed peak value is 2.3 and 0.7 respectively while the peak prediction error is 1.47% and 0.30%. The RMSE of the model for cycle 20 and 21 is 3.97 and 4.34 respectively. For cycle 22, the AE and RE is 4.6 and 2.16% while the RMSE of the model for this case is 4.50. The predicted peak amplitude of solar cycle 23 and 24 from our proposed model has a relative error of 1.75% and 1.99% respectively from the observed value while the RMSE is 3.4 for cycle 23 and 4.2 for cycle 24. Our projected prediction of cycle 25 using the proposed LSTM model, says that it will be stronger than cycle 24 and weaker than cycle 23. The solar cycle 25 will peak with an amplitude of sunspot number at 171.9 ± 3.4 and will be 47 % stronger than cycle 24. The solar cycle 25 will reach its peak in August 2023 ± 2 months. [ABSTRACT FROM AUTHOR]

Abstract Using a time series of geomagnetic storm events between 1957 and 2019, obtained by selecting storms where Dst

Using a time series of geomagnetic storm events between 1957 and 2019, obtained by selecting storms where Dst<−50 nT, we have analyzed the probability of occurrence of moderate, intense, and severe events. Considering that geomagnetic storms can be modeled as stochastic processes with a log‐normal probability distribution over their minimum Dst index, the dataset was separated according to solar cycle (SC) and SC phases, and the distributions of events were fitted through maximum likelihood method in order to characterize the occurrence of storms in each cycle and phase, and then compare those occurrences to the SC24. Our results show that there is a strong dependence between the occurrence of intense storms, with Dst< −100 nT, and the strength of the SC measured by the sunspot numbers. In particular, SC24 is very similar to SC20. However, when comparing the occurrence of storms by SC phases, events tend to show similar activity toward the minimum phase and have significant differences in the maximum phases. By looking at the σ value—the fit log‐normal distribution "width" parameter—characteristic of the occurrence rate of storms, we have found that the σdes (the sigma value in the descending phase of one cycle) shows the highest correlation (r=−0.76) with σmax (the sigma value in the maximum phase of the next cycle) which allows us to estimate the occurrence rate of storms for SC25 to be similar to those of SC21 and SC22, suggesting a more intense cycle than the one that just ended. Plain Language Summary: Geomagnetic storms are a common occurrence on Earth, and they can have significant impact on our lives. The occurrence of geomagnetic storms depends on the strength of the 11 yr solar cycle (SC), and the different phases in it. Since we have been recording sunspot numbers (which roughly indicate the activity of the sun) for centuries, and the storm index Dst (a measurement of geomagnetic activity on Earth) for decades, we study in this manuscript the connection between the two dataset, this is, how sunspot number (and therefore SC) relates to the occurrence of geomagnetic storms. We found that the latest SC behaved in a way, that is, more characteristic of the phases of low activity (minimum phase) of the previous cycles. We also found that in general, the declining phase of a cycle tends to be connected to the maximum phase of the next cycle, which indicates that a prediction of the next cycle can be attempted. In that regard, our results suggest that the SC that just started should be stronger than the current cycle, but no the strongest of the past five cycles. Key Points: Geomagnetic storms are characterized by solar cycle (SC) and phases using a log‐normal distribution fitted using maximum likelihood methodSC24 behaved similarly to the minimum phase of the past five SCs in terms of the intensity of the storms that occurredDescending phase characteristic lognormal coefficient correlates with the following maximum phase and may predict strength of SC25 [ABSTRACT FROM AUTHOR]

This article deals with the prediction of the upcoming solar activity cycle, Solar Cycle 25. We propose that astronomical ephemeris, specifically taken from the catalogs of aphelia of the four Jovian planets, could be drivers of variations in solar activity, represented by the series of sunspot numbers (SSN) from 1749 to 2020. We use singular spectrum analysis (SSA) to associate components with similar periods in the ephemeris and SSN. We determine the transfer function between the two data sets. We improve the match in successive steps: first with Jupiter only, then with the four Jovian planets and finally including commensurable periods of pairs and pairs of pairs of the Jovian planets (following Mörth and Schlamminger in Planetary Motion, Sunspots and Climate, Solar-Terrestrial Influences on Weather and Climate, 193, 1979). The transfer function can be applied to the ephemeris to predict future cycles. We test this with success using the “hindcast prediction” of Solar Cycles 21 to 24, using only data preceding these cycles, and by analyzing separately two 130 and 140 year-long halves of the original series. We conclude with a prediction of Solar Cycle 25 that can be compared to a dozen predictions by other authors: the maximum would occur in 2026.2 (± 1 yr) and reach an amplitude of 97.6 (± 7.8), similar to that of Solar Cycle 24, therefore sketching a new “Modern minimum”, following the Dalton and Gleissberg minima. [ABSTRACT FROM AUTHOR]

We present the observing program proposed by Paris and Côte d'Azur Observatories for monitoring solar activity during the upcoming cycle 25 and providing near real time images and movies of the chromosphere for space-weather research and applications. Two optical instruments are fully dedicated to this task and we summarize their capabilities. Short-term and fast-cadence observations of the chromosphere will be performed automatically at Calern observatory (Côte d'Azur), where dynamic events, as flare development, Moreton waves, filament instabilities and Coronal Mass Ejections onset, will be tracked. This new set of telescopes will operate in 2021 with narrow bandpass filters selecting Hα and CaII K lines. We present the instrumental design and a simulation of future images. At Meudon, the Spectroheliograph is well adapted to the long-term and low-cadence survey of chromospheric activity by recently improved and optimized spectroscopic means. Surface scans deliver daily (x, y, λ) datacubes of Hα, CaII K and CaII H line profiles. We describe the nature of available data and emphasize the new calibration method of spectra. [ABSTRACT FROM AUTHOR]