scholarly journals Sunspot numbers: Implications on Eastern African rainfall

2014 ◽  
Vol 110 (1/2) ◽  
pp. 1-5 ◽  
Author(s):  
Francis Gachari ◽  
David M. Mulati ◽  
Joseph N. Mutuku

Following NASA’s prediction of sunspot numbers for the current sunspot cycle, Cycle 24, we now include sunspot numbers as an explanatory variable in a statistical model. This model is based on fitting monthly rainfall values with factors and covariates obtained from solar–lunar geometry values and sunspot numbers. The model demonstrates high predictive skill in estimating monthly values by achieving a correlation coefficient of 0.9 between model estimates and the measurements. Estimates for monthly total rainfall for the period from 1901 to 2020 for Kenya indicate that the model can be used not only to estimate historical values of rainfall, but also to predict monthly total rainfall. We have found that the 11-year solar sunspot cycle has an influence on the frequency and timing of extreme hydrology events in Kenya, with these events occurring every 5±2 years after the turning points of sunspot cycles. While solar declination is the major driver of monthly variability, sunspots and the lunar declinations play a role in the annual variability and may have influenced the occurrence of the Sahelian drought of the mid-1980s that affected the Sahel region including the Greater Horn of Africa. Judging from the reflection symmetry, the trend of the current maximum and the turning point of the sunspot minimum at the end of the Modern Maximum, with a 95% level of confidence, drought conditions similar to those of the early 1920s may reoccur in the year 2020±2.

2014 ◽  
Vol 4 (2) ◽  
pp. 477-483
Author(s):  
Debojyoti Halder

Sunspots are temporary phenomena on the photosphere of the Sun which appear visibly as dark spots compared to surrounding regions. Sunspot populations usually rise fast but fall more slowly when observed for any particular solar cycle. The sunspot numbers for the current cycle 24 and the previous three cycles have been plotted for duration of first four years for each of them. It appears that the value of peak sunspot number for solar cycle 24 is smaller than the three preceding cycles. When regression analysis is made it exhibits a trend of slow rising phase of the cycle 24 compared to previous three cycles. Our analysis further shows that cycle 24 is approaching to a longer-period but with smaller occurrences of sunspot number.


Solar Physics ◽  
2014 ◽  
Vol 290 (2) ◽  
pp. 635-643 ◽  
Author(s):  
H. S. Ahluwalia ◽  
R. C. Ygbuhay
Keyword(s):  

Solar Physics ◽  
2009 ◽  
Vol 260 (1) ◽  
pp. 225-232 ◽  
Author(s):  
Nipa J. Bhatt ◽  
Rajmal Jain ◽  
Malini Aggarwal

2010 ◽  
Author(s):  
H. S. Ahluwalia ◽  
R. C. Ygbuhay ◽  
M. Maksimovic ◽  
K. Issautier ◽  
N. Meyer-Vernet ◽  
...  
Keyword(s):  

1990 ◽  
Vol 43 (3) ◽  
pp. 357 ◽  
Author(s):  
JO Murphy

Initially, the rise and fall components of the ll-year solar sunspot cycle are approximated by separate least-squares polynomials for four cycle classifications, which are determined by the magnitude of the average of the annual sunspot numbers per cycle. Following, a method is formulated to generate detailed reconstruction of the annual variation of a solar cycle based on this cycle average, and the results obtained for cycles -4 through to 21 are compared with the annual Zurich values. This procedure is then employed to establish annual sunspot numbers using published average cycle values obtained from atmospheric carbon 14 variations, which have been derived from the chemical analysis of tree ring sections. The reconstructed sequences are correlated with the observed cycle values and with tree ring width index chronologies which exhibit a significant II-year periodicity. It is anticipated that the long carbon 14 records and parallel dendrochronological data could be employed to obtain a more detailed portrayal of previous periods of strong solar activity than that given by current estimates based on historical records.


2020 ◽  
Vol 38 (6) ◽  
pp. 1237-1245
Author(s):  
Zhanle Du

Abstract. Predicting the maximum intensity of geomagnetic activity for an upcoming solar cycle is important in space weather service and for planning future space missions. This study analyzed the highest and lowest 3-hourly aa index (aaH∕aaL) in a 3 d interval, smoothed by 363 d to analyze their variation with the 11-year solar cycle. It is found that the maximum of aaH (aaHmax) is well correlated with the preceding minimum of either aaH (aaHmin, r=0.85) or aaL (aaLmin, r=0.89) for the solar cycle. Based on these relationships, the intensity of aaHmax for solar cycle 25 is estimated to be aaHmax(25)=83.7±6.9 (nT), about 29 % stronger than that of solar cycle 24. This value is equivalent to the ap index of apmax(25)=47.4±4.4 (nT) if employing the high correlation between ap and aa (r=0.93). The maximum of aaL (aaLmax) is also well correlated with the preceding aaHmin (r=0.80). The maximum amplitude of the sunspot cycle (Rm) is much better correlated with high geomagnetic activity (aaHmax, r=0.79) than with low geomagnetic activity (aaLmax, r=0.37). The rise time from aaHmin to aaHmax is weakly anti-correlated to the following aaHmax (r=-0.42). Similar correlations are also found for the 13-month smoothed monthly mean aa index. These results are expected to be useful in understanding the geomagnetic activity intensity of solar cycle 25.


2022 ◽  
Vol 924 (2) ◽  
pp. 59
Author(s):  
J. Y. Lu ◽  
Y. T. Xiong ◽  
K. Zhao ◽  
M. Wang ◽  
J. Y. Li ◽  
...  

Abstract In this paper, a novel bimodal model to predict a complete sunspot cycle based on comprehensive precursor information is proposed. We compare the traditional 13 month moving average with the Gaussian filter and find that the latter has less missing information and can better describe the overall trend of the raw data. Unlike the previous models that usually only use one precursor, here we combine the implicit and geometric information of the solar cycle (peak and skewness of the previous cycle and start value of the predicted cycle) with the traditional precursor method based on the geomagnetic index and adopt a multivariate linear approach with a higher goodness of fit (>0.85) in the fitting. Verifications for cycles 22–24 demonstrate that the model has good performance in predicting the peak and peak occurrence time. It also successfully predicts the complete bimodal structure for cycle 22 and cycle 24, showing a certain ability to predict whether the next solar cycle is unimodal or bimodal. It shows that cycle 25 is a single-peak structure and that the peak will come in 2024 October with a peak of 145.3.


Water ◽  
2019 ◽  
Vol 11 (9) ◽  
pp. 1843
Author(s):  
Elias Silva de Medeiros ◽  
Renato Ribeiro de Lima ◽  
Ricardo Alves de Olinda ◽  
Carlos Antonio Costa dos Santos

The purpose of this study was to provide a detailed framework to use the spatiotemporal kriging to model the space-time variability of precipitation data in Paraíba, which is located in the northeastern region of Brazil (NEB). The NEB is characterized by an irregular, highly variable distribution of rainfall in space and time. In this region, it is common to find high rates of rainfall at locations adjacent to those with no record of rain. Paraíba experiences localized periods of drought within rainy seasons and distinct precipitation patterns among the state’s mesoregions. The mean precipitation values observed at several irregularly spaced rain gauge stations from 1994 to 2014 showed remarkable variations among the mesoregions in Paraíba throughout the year. As a consequence of this behavior, there is a need to model the rainfall distribution jointly with space and time. A spatiotemporal geostatistical methodology was applied to monthly total rainfall data from the state of Paraíba. The rainfall data indicate intense spatial and temporal variabilities that directly affect the water resources of the entire region. The results provide a detailed spatial analysis of sectors experiencing precipitation conditions ranging from a scarcity to an excess of rainfall. The present study should help drive future research into spatiotemporal rainfall patterns across all of NEB.


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