Sunspot Number Research Articles

Periodic analysis of forest fire numbers and sunspot numbers in Türkiye

Bu çalışmada Türkiye orman yangın sayıları ile güneş leke sayılarının periyodik yapılarının incelenmesi amaçlanmıştır. Çalışmanın temel hedefi sürekli dalgacık dönüşümleri ve global spektrumlar yöntemini kullanarak Türkiye orman yangın sayıları ile güneş lekesi sayılarının değişkenliğini araştırmaktır. Türkiye son yıllarda orman yangın sayılarının artması üzerinde odaklanılan sorunlardan birisidir. Güneş faaliyetini göstermek için genellikle güneş lekesi sayısı kullanılmaktadır. Dalgacık dönüşümü Fourier dönüşümünün gelişmiş ve iyi planlanmış bir sürümü olup sinyallerin frekans bileşenlerini ortaya çıkarmaktadır. Dalgacık dönüşüm teknikleri yaklaşımı Türkiye’deki orman yangınlarının sayıları ve güneş lekesi sayılarının periyodik analizi için uygulanmıştır. Bu kapsamda Orman Genel Müdürlüğünün (OMG) 1937 yılından 2020 yılı sonuna kadarki güncel yangın sayıları kayıtları ile güneş lekesi sayılarının kayıtları değerlendirilmiştir. Mevcut olan 84 yıllık toplam orman yangın sayıları verileri ve aynı dönemde olan güneş lekeleri sayıları verilerinin periyodik yapısı Sürekli Dalgacık dönüşümü (SDD) ve Global Dalgacık Spektrumu (GDS) tekniğinden yararlanılarak analiz edilmiştir. Orman yangın sayıları için hâkim periyodik bileşen 6.60 yıllık bulunurken güneş leke sayılarında ise 10.67 yıllık periyodik bileşen belirlenmiştir. Her iki değişkenin uzun yıllık diğer periyodik bileşenleri de tespit edilmiştir. Türkiye’deki orman yangın sayıları ile güneş leke sayılarının periyodik yapılarının birbirlerinden çok farklı yapılarda olduğu bu çalışma sonucunda saptanmıştır.

The role of solar and geomagnetic activity in endothelial activation and inflammation in the NAS cohort.

This study investigated the associations between solar and geomagnetic activity and circulating biomarkers of systemic inflammation and endothelial activation in the Normative Aging Study (NAS) cohort. Mixed effects models with moving day averages from day 0 to day 28 were used to study the associations between solar activity (sunspot number (SSN), interplanetary magnetic field (IMF)), geomagnetic activity (planetary K index (Kp index), and various inflammatory and endothelial markers. Biomarkers included intracellular adhesion molecule-1 (sICAM-1), vascular cell adhesion molecule-1 (sVCAM-1), C-reactive protein (CRP), and fibrinogen. After adjusting for demographic and meteorological variables, we observed significant positive associations between sICAM-1 and sVCAM-1 concentrations and solar and geomagnetic activity parameters: IMF, SSN, and Kp. Additionally, a negative association was observed between fibrinogen and Kp index and a positive association was observed for CRP and SSN. These results demonstrate that solar and geomagnetic activity might be upregulating endothelial activation and inflammation.

Evolutionary features of polar and non-polar coronal holes during solar cycle 24 and the rising phase of cycle 25

This paper presents the results of the analysis of the evolution of coronal holes (CHs) on the Sun during the period May 13, 2010 – March 20, 2022, covering Solar Cycle 24. Our study uses images in the extreme-ultraviolet iron line (Fe XII 193 Å) obtained with the Atmospheric Imager Assembly of the Solar Dynamics Observatory (AIA/SDO). To localize CHs and determine their areas, we used the Heliophysics Event Knowledgebase (HEK). We separate the CHs into polar and non-polar and study the evolutionary features of each group. During this period, an asymmetry between the Northern (N) and Southern (S) Hemispheres (N-S or hemispheric asymmetry) is detected both in the solar activity (SA) indices and in the localization of the maximum areas of the polar and non-polar CHs. It is shown that the hemispheric asymmetry of the areas of polar and non-polar CHs varies significantly over time and that the nature of these changes is clearly related to the SA cycle. We find that for most of the period, the polar CHs were predominated generated in the S- hemisphere while the non-polar CHs were dominant in the N- hemisphere. It is found that the maximum and minimum of the hemispheric imbalance in the areas of non-polar CHs are close in time to the maximum and minimum of the asymmetry of the SA indices (the number and areas of sunspots). The maximum hemispheric imbalance of the polar CH areas is observed at the maximum of Cycle 24, and the minimum imbalance is found at the cycle minimum. These results confirm our assumption that these two types of CHs are of a different nature and that the non-polar CHs, like sunspots, are elements of the general magnetic activity.

Forecasting the solar cycle 25 using a multistep Bayesian neural network

ABSTRACTPredicting the solar activity of upcoming cycles is crucial nowadays to anticipate potentially adverse space weather effects on the Earth’s environment produced by coronal transients and traveling interplanetary disturbances. The latest advances in deep learning techniques provide new paradigms to obtain effective prediction models that allow to forecast in detail the evolution of cosmogeophysical time series. Because of the underlying complexity of the dynamo mechanism in the solar interior that is at the origin of the solar cycle phenomenon, the predictions offered by state-of-the-art machine learning algorithms represent valuable tools for our understanding of the cycle progression. As a plus, Bayesian deep learning is particularly compelling thanks to recent advances in the field that provide improvements in both accuracy and uncertainty quantification compared to classical techniques. In this work, a deep learning long short-term memory model is employed to predict the complete profile of Solar Cycle 25, thus forecasting also the advent of the next solar minimum. A rigorous uncertainty estimation of the predicted sunspot number is obtained by applying a Bayesian approach. Two different model validation techniques, namely the Train-Test split and the time series k-fold cross-validation, have been implemented and compared, giving compatible results. The forecasted peak amplitude is lower than that of the preceding cycle. Solar Cycle 25 will last 10.6 ± 0.7 yr, reaching its maximum in the middle of the year 2024. The next solar minimum is predicted in 2030 and will be as deep as the previous one.

A Framework on Analyzing Long-Term Drought Changes and Its Influential Factors Based on the PDSI

Drought is one of the most frequent and most widespread natural disasters worldwide, significantly impacting agricultural production and the ecological environment. An investigation of long-term drought changes and its influencing factors provides not only an understanding of historical droughts but also a scientific basis for the protection of future water resources. This study investigated the temporal characteristics of drought in a study site located in the center of Southwest China (SWC) over a 700-year period (AD 1300–2005) using the Palmer Drought Severity Index (PDSI). The linkage between drought and its influencing factors is discussed. An algorithm based on the random forest (RF) method was proposed to analyze the dynamic influence of the factors on drought. We also examined the linkages between the demise of two dynasties and historical drought events. The results showed that the study site was a drought-prone area in the study period and experienced a non-significant drying trend in all centuries, except for the 17th century; a total of 232 droughts were detected in the study site from AD 1300–2005. The wavelet spectrum of the PDSI series showed the existence of 4-, 8-, 16-, 32-, and 128-year-periods. A strong correlation existed between the sunspot numbers and the PDSI. The correlation of the period between the PDSI and El Niño-Southern Oscillation (ENSO) series in the same frequency domain was weak, while the ENSO exhibited a strong interaction with the PDSI in some time periods. The Pacific Decadal Oscillation (PDO) and PDSI had no resonance period in the low-frequency region, but there was a period of 80–130 years in the high-frequency region. The relative rates of influence of the ENSO, sunspot numbers, and PDO during AD 1700–1996 were 38.40%, 31.81%, and 29.8%, respectively. However, the mechanism of the interaction between droughts and the influential factors is complex, and the dominant factor changed over time. The analysis of long-term drought changes based on the PDSI series may provide clues to understand the development of historical events.

Local database records of solar cycles 22, 23, 24 in Iraq

Recording the activity of solar is an important task especially in space weather because it is one of the predominant features of the 11-year activity, which covers an increasing number of phenomena, its effects on the communications, satellites, radio signals, radio broadcasts, GPS systems and other technologies. During this period, solar activity is analyzed and documented in many places in the world. In the current study sunspot data was monitored and recorded in Baghdad, Iraq, for cycles 22, 23, and 24. The study aims to know the relative number of sunspots for the period started from 1986 using the refracting telescope (diameter = 10 cm, focal length = 120 cm and lens lens = 1.2 cm). The present study confirms that the results of obtained records locally are comparable to those of international studies.

Fire history and its forcing in Northeastern Asia boreal forests

Fire is a principal disturbance agent in Northeastern Asia forests with the highest fire radiative power globally. The limitation of the wide network of sites with long reconstructed fire histories restricts our understanding of factors forcing regional fire dynamics. We aggregated 30 fire scars-based fire chronologies in Northeastern Asia to create a composite region chronology from 1701 to 2010. We assessed the correlation between solar activity, drought (as represented by the Self-Calibrated Palmer Drought Severity Index, scPDSI), Arctic Oscillation (AO), and forest fire. Large fires were associated with drought, positive AO, and lower sunspot numbers, as revealed by Superposed epoch analysis. The pathways of solar activity force drought and AO, hence, driving fire activity indicated by structural equation modeling. As Solar Cycle 25 (starting in December 2019 and lasting until about 2030) is projected to be as weak as the Dalton minimum (1795–1820 AD), we expect fire activity to remain high over this period. Our findings provide insights into the extraterrestrial forcing of forest fire activity and its prediction in Northeastern Asia.

Aligned signed-rank tests of a linear autoregressive model against an exponential autoregressive one

In this work, we have established the aligned signed-rank tests for testing the ordinary dependence against exponential autoregression [] model. The considered tests are asymptotically valid under an arbitrary symmetric innovation density, and locally asymptotically most stringent against the EXPAR alternatives associated with some predetermined symmetric density type. Several simulation was curried out to show the good performance of these tests and an application for modeling the annual sunspots number is provided.

A method to characterize climate, Earth or environmental vector random processes

We propose a general methodology to characterize a non-stationary random process that can be used for simulating random realizations that keep the probabilistic behavior of the original time series. The probability distribution of the process is assumed to be a piecewise function defined by several weighted parametric probability models. The weights are obtained analytically by ensuring that the probability density function is well defined and that it is continuous at the common endpoints. Any number of subintervals and continuous probability models can be chosen. The distribution is assumed to vary periodically in time over a predefined time interval by defining the model parameters and the common endpoints as truncated generalized Fourier series. The coefficients of the expansions are obtained with the maximum likelihood method. Different sets of orthogonal basis functions are tested. The method is applied to three time series with different particularities. Firstly, it is shown its good behavior to capture the high variability of the precipitation projected at a semiarid location of Spain for the present century. Secondly, for the Wolf sunspot number time series, the Schwabe cycle and time variations close to the 7.5 and 17 years are analyzed along a 22-year cycle. Finally, the method is applied to a bivariate time series that contains (1) freshwater discharges at the last regulation point of a dam located in a semiarid zone in Andalucía (Spain) which is influenced not only by the climate variability but also by management decisions and (2) the salinity at the mouth of the river. For this case, the analysis, that was combined with a vectorial autoregressive model, focus on the assessment of the goodness of the methodology to replicate the statistical features of the original series. In particular, it is found that it reproduces the marginal and joint distributions and the duration of sojourns above/below given thresholds.

Maximal growth rate of the ascending phase of a sunspot cycle for predicting its amplitude

Context. Forecasting the solar cycle amplitude is important for a better understanding of the solar dynamo as well as for many space weather applications. Different empirical relations of solar cycle parameters with the peak amplitude of the upcoming solar cycle have been established and used for solar cycle forecasts, as, for instance, the Waldmeier rule relating the cycle rise time with its amplitude, the polar fields at previous minimum, and so on. Recently, a separate consideration of the evolution of the two hemispheres revealed even tighter relations. Aims. We aim to introduce the maximal growth rate of sunspot activity in the ascending phase of a cycle as a new and reliable precursor of a subsequent solar cycle amplitude. We also intend to investigate whether the suggested precursor provides benefits for the prediction of the solar cycle amplitude when using the sunspot indices (sunspot numbers, sunspot areas) derived separately for the two hemispheres compared to the total sunspot indices describing the entire solar disc. Methods. We investigated the relationship between the maximal growth rate of sunspot activity in the ascending phase of a cycle and the subsequent cycle amplitude on the basis of four data sets of solar activity indices: total sunspot numbers, hemispheric sunspot numbers from the new catalogue from 1874 onwards, total sunspot areas, and hemispheric sunspot areas. Results. For all the data sets, a linear regression based on the maximal growth rate precursor shows a significant correlation. Validation of predictions for cycles 1–24 shows high correlations between the true and predicted cycle amplitudes reaching r = 0.93 for the total sunspot numbers. The lead time of the predictions varies from 2 to 49 months, with a mean value of 21 months. Furthermore, we demonstrated that the sum of maximal growth rate indicators determined separately for the north and the south hemispheric sunspot numbers provides more accurate predictions than that using total sunspot numbers. The advantages reach 27% and 11% on average in terms of rms and correlation coefficient, respectively. The superior performance is also confirmed with hemispheric sunspot areas with respect to total sunspot areas. Conclusions. The maximal growth rate of sunspot activity in the ascending phase of a solar cycle serves as a reliable precursor of the subsequent cycle amplitude. Furthermore, our findings provide a strong foundation for supporting regular monitoring, recording, and predictions of solar activity with hemispheric sunspot data, which capture the asymmetric behaviour of the solar activity and solar magnetic field and enhance solar cycle prediction methods.

Unknown effects of daily-scale solar activity on the plant growth: Data from 6-year growth monitoring of Sphagnum riparium.

The influence of solar activity on plant growth has been studied for over 100 years, however, this phenomenon is still poorly understood on a daily scale. The data from extensive monitoring of the growth of peat moss Sphagnum riparium, which we are conducting in the mires of Karelia (Russia), may shed light on this issue. During the 6 years of observation, 161,190 shoots were measured, and 1075 growth rates were obtained. Considering together the growth rates with the sunspot number and involving data on seasonal temperature, we found previously unknown effects of daily-scale solar activity on plant growth. It was found that the sunspot number weakly but significantly inhibits the growth of Sphagnum. The extreme sunspot number in the 4 days before the growth rate values have a stronger influence. The involvement of temperature data showed that inhibition in growth is observed only in the temperature range from 6.7°C to 15.3°C and disappears beyond these limits. In addition, the data obtained showed that the influence of sunspot number on the growth of Sphagnum is progressively increasing along the gradient from the minimum to the maximum of the 11-year solar cycle. The study provides one of the first results on the effect of solar activity on plant growth on a daily scale. The results expand our knowledge of the biological effects of solar activity. Indirectly, they can also be useful to better our understanding of the ozone layer's involvement in this process.

A Machine Learning Approach to Predicting SEP Events Using Properties of Coronal Mass Ejections

AbstractSolar energetic particles (SEPs) can cause severe damage to astronauts and their equipment, and can disrupt communications on Earth. A lack of thorough understanding the eruption processes of solar activities and the subsequent acceleration and transport processes of energetic particles makes it difficult to forecast the occurrence of an SEP event and its intensity using conventional modeling with physics‐based parameters. Therefore, in order to provide an advance warning for astronauts to seek shelter in a timely manner, we apply neural networks to forecast the occurrence of SEP events. We use the properties of coronal mass ejections (CMEs) archived in the Coordinated Data Analysis Workshops catalog based on SOHO Large Angle and Spectrometric Coronagraph Experiment observations. We also derive some features based on these properties associated with the CME, and analyze the contribution of each feature to the overall prediction. Our algorithm achieves an average True Skill Statistic of 0.906, an average F1 score of 0.246, an average probability of detection of 0.920, and an average false alarm rate of 0.882. An analysis of the features shows that sunspot number and a feature based on Type II radio bursts contribute the most, but when grouped together, CME speed‐related features are the most important features.

Solar Cycle 25 Prediction Using Length-to-Amplitude Relations

We propose a simple method for prediction of the 11-year solar cycle maximum that is based on two relations. One of them is well known Waldmeier's rule that binds the amplitude of a cycle and the length of its ascending phase. The second rule relates the length of a given cycle from minimum to minimum and the amplitude of the next one. Using corresponding linear regressions we obtain for the amplitide of cycle 25 in the scale of 13-month smoothed monthly total revised sunspot number ${\rm SN}_{\rm max}(25) = 181\pm46$ and for the moment of the maximum $T_{\rm max}(25) = 2024.2\pm1.0$. Therefore, according to the prediction, cycle 25 will be higher than the previous one (${\rm SN}_{\rm max}(24) = 116$) with probability 0.92.

REGRESSION MAXIMUM AND ITS USE TO HYDROECOLOGICAL RESEARCH

One of the actual problems of hydrochemistry there is influence to solar activity on condition chemical substance in water environment. The goal of the work it is possible estimation relationship some hydrochemical factors and solar activity (Wolf's Numbers W). To this effect the research was given for concentration 〖NH〗_4^+, 〖NO〗_2^- , 〖NO〗_3^- in water large flat river of Ukraine (1991 … 2010 years). The ion concentrations denominated in mg on litre. Were they also analysed given over Wolf's numbers for this period (1991 … 2010 years). Since these dependencies for the best reveals itself at a rate of extreme values that for each value of the Wolf's number selected only maximum to concentrations of the ion. Accordingly building to regressions executed not on all raw data’s but for maximum values concentration only. For this regression execute in two steps: STEP 1: Selected only one (maximum) value concentration of the ion for each unique value of Wolf's number. STEP 2: To prepared by specified way data is used for regression. Experiment has shown that linear regression in this instance to use it is impossible (got the horizontal line). Experiment has also shown adequacy of the use polynomial quantile regression (so it was used this type of regression). It was used median egression (quantile regression for Q=0,5). Median regression demonstrates the observable reduction a concentration of 〖NH〗_4^+, 〖NO〗_2^-,〖 NO〗_3^- when increasing of values Wolf's Numbers. Offered in article “regression for maximus” important for decision of the practical problems of hydroecology. The general trend in behavioyr of Wolf's Numbers can be forecasting on times of the ten years onward. This means as the general trend in behavioyr of 〖NH〗_4^+, 〖NO〗_2^- , 〖NO〗_3^- also can be forecasting on times of ten of years onward (the factor of the purity of natural water). The natural water more pure (the minimum concentration of 〖NH〗_4^+, 〖NO〗_2^- , 〖NO〗_3^-) under high solar activity (high values of Wolf's Numbers).

Relationship between the Sunspot Number and Active Day Fraction: An Application for the Maunder Minimum

Long-term solar activity can be studied using several parameters. Some of the most used are based on the sunspot counting. The active day fraction (ADF) is the simplest index derived from this counting. It is reliable in periods of low solar activity such as the Maunder minimum (MM). In this work, we study the relationship between the ADF and the sunspot number. We have obtained that the optimal fit of that relationship is an exponential function whose exponent is a degree 3 polynomial including all data except those with ADF equal to 100%. Then, we use that fit to estimate the sunspot number during the MM from the ADF calculated from the most recent sunspot group number database. Our estimations of the annual sunspot numbers are below 15, except that for 1656, which is 40.8, whereas our estimations of the triennial sunspot numbers are below 10 from 1648 to 1714. We have found peaks of the solar cycle in the middle of the 1650s, 1670s, 1680s, and 1700s but no clear evidence of solar cycle in the 1660s and 1690s, likely due to the scarcity of the available data. Our results agree with previous works obtaining values significantly higher than those of the group sunspot number derived by Hoyt and Schatten in 1998 but still fully compatible with a grand minimum period.

Coronal mass ejection and solar activity for cycle 23 and 24; a comparative analysis of observational parameters

We analyzed sunspot tracers [sunspot number (SSN) and sunspot area (SSA)] and Coronal Mass Ejection (CME) observed parameters for cycle 23 and 24, comparing sunspot tracers time series with time series for CME parameters [Initial speed and Acceleration] for All and segregated CMEs for possible similarities in their variation pattern. A comparison of CME time series with solar activity (especially SSAT and SSN which seem to reflect the true general activity cycle) shows that fast CMEs follow the solar activity cycle but slow CMEs don’t. Also, CMEs with positive and negative acceleration follow the solar cycle but lags behind b 3monhs for cycle 24. However, the CME acceleration does not follow the solar cycle. CME distribution show that Slow CMEs increases in initial speed from one cycle to the other with a corresponding decrease in acceleration for CMEs with fast speeds. There is an almost insignificant increase in acceleration for CMEs with positive and negative acceleration. These charges are perhaps due to effect of solar wind.

An Early Assessment of the Forecast by the Solar Cycle 25 Prediction Panel

The effects of solar activity on technology have increased the interest in solar forecasting both in the scientific community and in society. With two and a half years of data available, we are approximately halfway to the maximum of Solar Cycle 25. Several research groups around the world published their predictions for this cycle. In this work, we assess the forecast made by the Solar Cycle 25 Prediction Panel. Analyzing the sunspot number data available, we conclude that Solar Cycle 25 is one of the weakest cycles since mid-18th century and, however, it is stronger than the initial forecast of the highest range predicted by the Solar Cycle 25 Prediction Panel for the two first years and a half of its ascending phase.

The quiet chromosphere: Phase relation with the solar cycle

The solar chromosphere was daily observed in the Ca II K line from 1915 August 10 to 1985 July 7, and correspondingly, 938 synoptic maps are measured. The valuable database is utilized to explore phase relationship of long-term evolution of the quiet chromosphere and sunspot numbers. Temporal change of the quiet chromosphere is found to be in anti-phase with the solar cycle on the whole, as the small-scale magnetic elements whose flux is in a span of (2.9-32.0)×1018(Mx) do. The abnormal heating of the quiet chromosphere is accordingly suggested to be related mainly to the magnetic elements, and they may likely function in reducing chromosphere material loss.

Solar and geomagnetic activity reduces pulmonary function and enhances particulate pollution effects

BackgroundIncreased solar and geomagnetic activity (SGA) may alter sympathetic nervous system activity, reduce antioxidant activity, and modulate physiochemical processes that contribute to atmospheric aerosols, all which may reduce pulmonary function. ObjectivesInvestigate associations between forced expiratory volume at 1 s (FEV1) and forced vital capacity (FVC) with SGA, and assess whether SGA enhances adverse effects of particulate pollution, black carbon (BC) and particulate matter ≤2.5 μm in diameter (PM2.5). Methods: We conducted a repeated measures analysis in 726 Normative Aging Study participants (Boston, Massachusetts, USA) between 2000 and 2017, using interplanetary magnetic field (IMF), planetary K index (Kp), and sunspot number (SSN) as SGA measures. Linear mixed effects models were used to assess exposure moving averages up to 28 days for both SGA and pollution. ResultsIncreases in IMF, Kp Index and SSN from the day of the pulmonary function test averaged through day 28 of were associated with a significant decrement in FEV1 and FVC, after adjusting for potential confounders. There were greater effects for longer moving averages and enhanced effects of PM2.5 and BC on FEV1 and FVC with increased SGA. For example, for each inter-quartile increase (4.55 μg/m3) in average PM2.5 28 days before testing, low IMF (10th percentile: 3.2 nT) was associated with a −21.4 ml (95 % CI: −60.8, 18.1) and −7.1 ml (95 % CI: −37.7, 23·4) decrease in FVC and FEV1, respectively; high IMF (90th percentile: 9.0 nT) was associated with a −120.7 ml (95 % CI:-166.5, −74.9) and −78.6 ml (95 % CI: −114.3, −42·8) decrease in FVC and FEV1, respectively. DiscussionIncreased periods of solar and geomagnetic activity may directly contribute to impaired pulmonary function and also enhance effects of PM2.5 and BC. Since exposure to solar activity is ubiquitous, stricter measures in reducing air pollution exposures are warranted, particularly in elderly populations.

Variations of IMF Cone Angle and IMF Clock Angle to Solar Cycles and Geomagnetic Storms

Interplanetary magnetic field (IMF) carried by solar wind and rooted at solar corona. Variations of IMF depend on solar surface activities, e.g. sunspots. Moreover, solar magnetic storms are connected by the IMF. IMF parameters, sunspot number, and geomagnetic index for more than 55 years were downloaded from OMNIWeb, which the data were obtained from several spacecraft in geostasionary and L1 Lagrange orbits. Solar cycles are measured in terms of variations in the number of sunspots and their surface areas. Both magnetic events of IMF and sunspots indicate the relation between solar cycles and IMF orientation which is clock angle and cone angle. IMF also interacts with the Earth’s magnetosphere, and it can cause geomagnetic storms. Cone angle and clock angle are derived from IMF components on X-axis, Y-axis, and Z-axis. Their frequency distribution charts on each solar cycle of 20 to 24 and their maxima phase and minima phase are the key points for the analysis. The plot of clock angle to Dst index as a geomagnetic index for each solar cycle was also derived. Based on the analysis, solar cycles are indicated to give an impact on the magnitude of each IMF component. It makes the peak of the charts shifted from the minima phase to the maxima phase. Other than that, the reconnection in magnetopause mostly occurs when the clock angle is between 1400–2520 or when the clock angle directions are southward. But the 23rd solar cycle shows the northward clock angle can also cause extreme geomagnetic storms.