Heliospheric Modulation of Cosmic Rays and Solar Activity during Solar Cycles 22-24

2018 ◽  
Vol 13 (S340) ◽  
pp. 147-148
Author(s):  
Prithvi Raj Singh ◽  
C. M. Tiwari ◽  
A. K. Saxena
Keyword(s):  
Solar Activity ◽  
Time Lag ◽  
Cosmic Ray ◽  
Radio Flux ◽  
Solar Cycles ◽  
Cosmic Ray Intensity ◽  
Solar Radio Flux ◽  
The Relationship ◽  
Monthly Variations

AbstractWe have studied, the relationship between monthly variations of average counting rates of cosmic ray intensity (CRI) at Moscow super neutron monitoring station with mid cut-off rigidities (~2.42 GV), and the solar radio flux at 10.7cm (F10.7) and sunspot number (SSN) during the solar cycles 22 − 24. The F10.7cm (2800 MHz) and SSN is an excellent indicator of solar activity for the study period. We have investigated the patterns of long-term and mid-term periodicities of SSN and F10.7, using Fast Fourier Transform (FFT) technique. We have observed the time-lag between ascending phase of CRI with F10.7cm and SSN during solar cycles 22 − 24.

scholarly journals Galactic Cosmic Ray Modulation Up to Recent Solar Cycles

2011 ◽  
Vol 48 (4) ◽  
pp. 66-70
Author(s):  
R. Agarwal ◽  
R. Mishra
Keyword(s):  
Solar Cycle ◽  
Solar Radio ◽  
Cosmic Ray ◽  
Radio Flux ◽  
Solar Cycles ◽  
Cosmic Ray Intensity ◽  
Cosmic Ray Modulation ◽  
Solar Radio Flux ◽  
Yearly Average ◽  
Galactic Cosmic Ray

Galactic Cosmic Ray Modulation Up to Recent Solar Cycles Cosmic ray neutron monitor counts obtained by different ground-based detectors have been used to study the galactic cosmic ray modulation during the last four solar activity cycles. Since long, systematic correlative studies have been per-formed to establish a significant relationship between the cosmic ray intensity and different helio-spheric activity parameters, and the study is extended to a recent solar cycle (23). In the present work, the yearly average of 10.7 cm solar radio flux and the interplanetary magnetic field strength (IMF, B) have been used to find correlation of the yearly average cosmic ray intensity derived from different neutron monitors. It is found that for four solar cycles (20-23) the cosmic ray intensity is anti-correlated with the 10.7 cm solar radio flux and the IMF, B value with some discrepancy. However, this is in a good positive correlation with the flux of mentioned wavelength for four different solar cycles. The IMF, B shows a weak correlation with cosmic rays for solar cycle 20, and a good anti-correlation for solar cycles 21-23.

Distribution of the number of strong earthquakes and the intensity of space rays during the solar activity cycle

2020 ◽  
Author(s):  
Valery L. Yanchukovsky ◽  
◽  
Anastasiya Yu. Belinskaya ◽  
Keyword(s):  
Solar Activity ◽  
Cosmic Rays ◽  
Cosmic Ray ◽  
Activity Cycle ◽  
Solar Activity Cycle ◽  
Strong Earthquakes ◽  
Cosmic Ray Intensity ◽  
Relationship Of ◽  
The Relationship

The relationship of Earth's seismicity with solar activity is investigated using the results of continuous long–term observations of cosmic ray intensity, solar activity and the number of strong earthquakes. Modulation of the flux of cosmic rays is used as information on the level of solar activity, processes on the Sun and interplanetary medium. The distribution of the number of sunspots, the intensity of cosmic rays and the number of strong earthquakes in the solar cycle is presented.

scholarly journals Solar activity, 27-day variation and long term modulation of cosmic ray intensity

Solar Physics ◽  
1970 ◽  
Vol 11 (1) ◽  
pp. 151-154 ◽  
Author(s):  
V. K. Balasubrahmanyan ◽  
D. Venkatesan
Keyword(s):  
Solar Activity ◽  
Cosmic Ray ◽  
Cosmic Ray Intensity

scholarly journals Predicting the Daily 10.7-cm Solar Radio Flux Using the Long Short-Term Memory Method

Universe ◽  
2022 ◽  
Vol 8 (1) ◽  
pp. 30
Author(s):  
Wanting Zhang ◽  
Xinhua Zhao ◽  
Xueshang Feng ◽  
Cheng’ao Liu ◽  
Nanbin Xiang ◽  
...  
Keyword(s):  
Solar Radio ◽  
Short Term Memory ◽  
Training Dataset ◽  
Radio Flux ◽  
Solar Cycles ◽  
Short Term ◽  
Term Memory ◽  
Solar Radio Flux ◽  
Long Short Term Memory ◽  
The Relationship

As an important index of solar activity, the 10.7-cm solar radio flux (F10.7) can indicate changes in the solar EUV radiation, which plays an important role in the relationship between the Sun and the Earth. Therefore, it is valuable to study and forecast F10.7. In this study, the long short-term memory (LSTM) method in machine learning is used to predict the daily value of F10.7. The F10.7 series from 1947 to 2019 are used. Among them, the data during 1947–1995 are adopted as the training dataset, and the data during 1996–2019 (solar cycles 23 and 24) are adopted as the test dataset. The fourfold cross validation method is used to group the training set for multiple validations. We find that the root mean square error (RMSE) of the prediction results is only 6.20~6.35 sfu, and the correlation coefficient (R) is as high as 0.9883~0.9889. The overall prediction accuracy of the LSTM method is equivalent to those of the widely used autoregressive (AR) and backpropagation neural network (BP) models. Especially for 2-day and 3-day forecasts, the LSTM model is slightly better. All this demonstrates the potentiality of the LSTM method in the real-time forecasting of F10.7 in future.

scholarly journals On the inclusion of data from solar cycle 19 to estimate F2 layer characteristic long term trends

2015 ◽  
Vol 58 (4) ◽  
Author(s):  
Blas F. de Haro Barbas ◽  
Ana G. Elias
Keyword(s):  
Solar Activity ◽  
Solar Cycle ◽  
Solar Radio ◽  
Data Series ◽  
Radio Flux ◽  
Solar Radio Flux ◽  
Long Term Trends ◽  
Layer Characteristic ◽  
Dominant Influence

<p>The effect of including solar cycle 19 (1954-1964) in ionospheric trend estimation is assessed using experimental foF2 values. The dominant influence on the F2 layer is solar EUV radiation. In fact, around 90% of inter-annual variance of ionospheric parameters, such as foF2, is explained by solar EUV proxies such as the sunspot number, Rz, and solar radio flux at 10.7 cm, F10.7. This makes necessary to filter out solar activity effects prior to long term trends estimation, which is reduced at most to the remaining 10% variance. In general solar activity is filtered assessing the residuals of a linear regression between foF2 and Rz, or between foF2 and F10.7. Solar cycle 19 is a strong cycle during which Rz and F10.7 exceeded the values beyond which the ionosphere does not respond linearly to a further increase in EUV radiation. This effect, called saturation, implies a break down of the linearity between foF2 and EUV, and results in persistent negative residuals during this period. Since solar cycle 19 is at the beginning of the time series, trends result to be positive, or less negative, than trends without considering this period. In this case the filtering process is generating a “spurious” trend in the filtered data series which may lead to erroneous conclusions. hmF2 that do not present a saturation effect is also analyzed.</p><div> </div>

scholarly journals Long-term dynamics of OH * temperatures over central Europe: trends and solar correlations

2016 ◽  
Vol 16 (23) ◽  
pp. 15033-15047 ◽  
Author(s):  
Christoph Kalicinsky ◽  
Peter Knieling ◽  
Ralf Koppmann ◽  
Dirk Offermann ◽  
Wolfgang Steinbrecht ◽  
...  
Keyword(s):  
Time Series ◽  
Solar Activity ◽  
Solar Radio ◽  
Linear Trend ◽  
Temperature Time Series ◽  
Time Interval ◽  
Radio Flux ◽  
Solar Radio Flux ◽  
Temperature Time

Abstract. We present the analysis of annual average OH* temperatures in the mesopause region derived from measurements of the Ground-based Infrared P-branch Spectrometer (GRIPS) at Wuppertal (51° N, 7° E) in the time interval 1988 to 2015. The new study uses a temperature time series which is 7 years longer than that used for the latest analysis regarding the long-term dynamics. This additional observation time leads to a change in characterisation of the observed long-term dynamics. We perform a multiple linear regression using the solar radio flux F10.7 cm (11-year cycle of solar activity) and time to describe the temperature evolution. The analysis leads to a linear trend of (−0.089 ± 0.055) K year−1 and a sensitivity to the solar activity of (4.2 ± 0.9) K (100 SFU)−1 (r2 of fit 0.6). However, one linear trend in combination with the 11-year solar cycle is not sufficient to explain all observed long-term dynamics. In fact, we find a clear trend break in the temperature time series in the middle of 2008. Before this break point there is an explicit negative linear trend of (−0.24 ± 0.07) K year−1, and after 2008 the linear trend turns positive with a value of (0.64 ± 0.33) K year−1. This apparent trend break can also be described using a long periodic oscillation. One possibility is to use the 22-year solar cycle that describes the reversal of the solar magnetic field (Hale cycle). A multiple linear regression using the solar radio flux and the solar polar magnetic field as parameters leads to the regression coefficients Csolar = (5.0 ± 0.7) K (100 SFU)−1 and Chale = (1.8 ±  0.5) K (100 µT)−1 (r2 = 0.71). The second way of describing the OH* temperature time series is to use the solar radio flux and an oscillation. A least-square fit leads to a sensitivity to the solar activity of (4.1 ± 0.8) K (100 SFU)−1, a period P  =  (24.8 ± 3.3) years, and an amplitude Csin  =  (1.95 ± 0.44) K of the oscillation (r2 = 0.78). The most important finding here is that using this description an additional linear trend is no longer needed. Moreover, with the knowledge of this 25-year oscillation the linear trends derived in this and in a former study of the Wuppertal data series can be reproduced by just fitting a line to the corresponding part (time interval) of the oscillation. This actually means that, depending on the analysed time interval, completely different linear trends with respect to magnitude and sign can be observed. This fact is of essential importance for any comparison between different observations and model simulations.

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