Phase analysis of solar activity indices using wavelet techniques

A precise knowledge of solar extreme ultraviolet (EUV) irradiance is of great importance for better understanding of Earth′s ionosphere and thermosphere. The search for an ideal solar EUV proxy is vital since the ionospheric and thermospheric models are based on the solar proxies of EUV radiation. In this study, the phase asynchrony analysis of solar EUV data with other solar activity indices during solar cycle 23 is done. The cross-wavelet transform (XWT) technique is used to reveal the phase difference between the two time series of solar indices. Analysis reveals that the phase relationship between the indices is both time and frequency dependent. The solar indices F10.7 and Mg II core-to-wing index are found to be more synchronous with solar EUV data for low frequency components.

Temporal variations of different solar activity indices through the solar cycles 21-23

Here, we compare the sunspot counts and the number of sunspot groups (SGs) with variations of total solar irradiance (TSI), magnetic activity, Ca II K-flux, faculae and plage areas. We applied a time series method for extracting the data over the descending phases of solar activity cycles (SACs) 21, 22 and 23, and the ascending phases 22 and 23. Our results suggest that there is a strong correlation between solar activity indices and the changes in small (A, B, C and H-modified Zurich Classification) and large (D, E and F) SGs. This somewhat unexpected finding suggests that plage regions substantially decreased in spite of the higher number of large SGs in SAC 23 while the Ca II K-flux did not decrease by a large amount nor was it comparable with SAC 22 and relates with C and DEF type SGs. In addition to this, the increase of facular areas which are influenced by large SGs, caused a small percentage decrease in TSI while the decrement of plage areas triggered a higher decrease in the magnetic field flux. Our results thus reveal the potential of such a detailed comparison of the SG analysis with solar activity indices for better understanding and predicting future trends in the SACs.

Creating poetry in time perspective

The author discusses the results of a study of multiyear variations in the occurrence of letters in 12,925 pieces of poetry composed by 317 Russian poets in 1796–2012 in correlation with geocosmic weather and of seasonal variations in the occurrence of letters in 2,551 poems by 132 male poets and 30 female poets. The conclusion is that there are multiyear and seasonal fluctuations of the occurrence of letters, which, for virtually all 33 letters, are synergetic with variations in the main indices of geocosmic weather. Temperature, Earth’s rotation rate, precipitation and solar activity indices appear to be the most influential ones, with 23, 17, 17 and 11 correlations, respectively, at p≤0.05–0.001. Seasonal variations in parameters of poetry created by men and by women are significantly different, which reflects gender differences in natural adjustment.

Statistical Study of Solar Forcing of Total Column Ozone Variation Over Three Cities in Kenya

In this study, a statistical analysis between three solar activity indices (SAI) namely; sunspot number (ssn), F10.7 index (sf) and Mg II index (mg) and total column ozone (TCO) time series over three cities in Kenya namely; Nairobi (1.17º S; 36.46º E), Kisumu (0.03º S; 34.45º E) and Mombasa (4.02º S; 39.43º E) for the period 1985 - 2011 are considered. Pearson and cross correlations, linear and multiple regression analyses are performed. All the statistical analyses are based on 95% confidence level. SAI show decreasing trend at significant levels with highest decrease in international sunspot number and least in Mg II index. TCO are highly correlated with each other at (0.936< r < 0.955, p < 0.001). SAI are also highly correlated with each other at (0.941< r < 0.976, p < 0.001) and are significantly positively correlated with TCO over the study period except Mg II index at Kisumu. TCO and SAI have correlations at both long and short lags. At all the cities, F10.7 index has an immediate impact and Mg II index has a delayed impact on TCO. A linear relationship exists between the two variables in all the cities. An increase in TCO of about 2 – 3 % (Nairobi), 1 – 2% (Kisumu) and 3 – 4 % (Mombasa) is attributed to solar activity indices. The multiple correlation coefficients and significant levels obtained show that 3 – 5% of the TCO at Nairobi, Kisumu and Mombasa can be predicted by the SAI.