We study the relation between monthly average counting rates of the cosmic ray intensity (CRI) observed at Moscow Neutron Monitoring Station, solar flare index (SFI) and coronal index during the solar cycles 22 and 23...We study the relation between monthly average counting rates of the cosmic ray intensity (CRI) observed at Moscow Neutron Monitoring Station, solar flare index (SFI) and coronal index during the solar cycles 22 and 23, for the period 1986-2008. The long-term behaviour of various solar activity parameters: sunspot numbers (SSN), solar flare index (Hα flare index), coronal index (CI) in relation to the duration of solar cycles 22 and 23 is examined. We find that the correlation coefficient of CRI with the coronal index as well as Hα flare index is relatively large anti-correlation during solar cycle 22. However, the monthly mean values of sunspot number, Hα flare index, and coronal index are well positively correlated with each other. We have analyzed the statistical analysis of the above parameters using of linear model and second order polynomial fits model.展开更多
Periodicity is common in natural processes, however, extraction tools are typically difficult and cumbersome to use. Here we report a computational method developed in MATLAB through a function called Periods with the...Periodicity is common in natural processes, however, extraction tools are typically difficult and cumbersome to use. Here we report a computational method developed in MATLAB through a function called Periods with the aim to find the main harmonic components of time series data. This function is designed to obtain the period, amplitude and lag phase of the main harmonic components in a time series (Periods and lag phase components can be related to climate, social or economic events). It is based on methods of periodic regression with cyclic descent and includes statistical significance testing. The proposed method is very easy to use. Furthermore, it does not require full understanding of time series theory, nor require many inputs from the user. However, it is sufficiently flexible to undertake more complex tasks such as forecasting. Additionally, based on previous knowledge, specific periods can be included or excluded easily. The output results are organized into two groups. One contains the parameters of the adjusted model and their F statistics. The other consists of the harmonic parameters that best fit the original series according to their importance and the summarized statistics of the comparisons between successive models in the cyclic descent process. Periods is tested with both, simulated and actual sunspot and Multivariate ENSO Index data to show its performance and accuracy.展开更多
文摘We study the relation between monthly average counting rates of the cosmic ray intensity (CRI) observed at Moscow Neutron Monitoring Station, solar flare index (SFI) and coronal index during the solar cycles 22 and 23, for the period 1986-2008. The long-term behaviour of various solar activity parameters: sunspot numbers (SSN), solar flare index (Hα flare index), coronal index (CI) in relation to the duration of solar cycles 22 and 23 is examined. We find that the correlation coefficient of CRI with the coronal index as well as Hα flare index is relatively large anti-correlation during solar cycle 22. However, the monthly mean values of sunspot number, Hα flare index, and coronal index are well positively correlated with each other. We have analyzed the statistical analysis of the above parameters using of linear model and second order polynomial fits model.
文摘Periodicity is common in natural processes, however, extraction tools are typically difficult and cumbersome to use. Here we report a computational method developed in MATLAB through a function called Periods with the aim to find the main harmonic components of time series data. This function is designed to obtain the period, amplitude and lag phase of the main harmonic components in a time series (Periods and lag phase components can be related to climate, social or economic events). It is based on methods of periodic regression with cyclic descent and includes statistical significance testing. The proposed method is very easy to use. Furthermore, it does not require full understanding of time series theory, nor require many inputs from the user. However, it is sufficiently flexible to undertake more complex tasks such as forecasting. Additionally, based on previous knowledge, specific periods can be included or excluded easily. The output results are organized into two groups. One contains the parameters of the adjusted model and their F statistics. The other consists of the harmonic parameters that best fit the original series according to their importance and the summarized statistics of the comparisons between successive models in the cyclic descent process. Periods is tested with both, simulated and actual sunspot and Multivariate ENSO Index data to show its performance and accuracy.
