Wavelet Analysis of the Quasi-27d Oscillations of Solar Index F10.7

Ma, Ruiping; Qiao, Ji; Xu, Jiyao

doi:10.1016/j.chinastron.2007.10.005

Cited by 8 publications

(4 citation statements)

References 10 publications

(6 reference statements)

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“…It was also discovered that F10.7 historical data have the characteristic of short quasi‐27‐day activity period oscillations due to the rotation of the Sun. Figure shows the corresponding periods of F10.7 (Ma, ). The period is short (approximately 25 days) near the Earth's equator, whereas the period is long (approximately 35 days) in the polar regions, and the average period is around 27 days (see Bouwer, ; Kane, ).…”

Section: Discussionmentioning

confidence: 99%

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Linear Multistep F10.7 Forecasting Based on Task Correlation and Heteroscedasticity

Wang

Zhong

et al. 2018

Earth and Space Science

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Solar 10.7‐cm radio flux (F10.7) is an important measure of solar radio emission activity. Accurate F10.7 forecasting plays a key role in both space weather and global environment forecasts. We discover that forecasting errors are heteroscedastic, something that is not often considered in previous models. In addition, task correlation between different forecasting steps is ignored in current multistep‐ahead forecast models. In this work, we propose a linear multistep forecasting model based on the correlation between different forecasting steps and the characteristic of heteroscedasticity. Further, we introduce a variational Bayesian procedure to optimize the model. The performance of the proposed model is tested on F10.7 historical data. The results show that the proposed model improves the performance of multistep F10.7 forecasting by considering correlation and heteroscedasticity.

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Section: Discussionmentioning

confidence: 99%

“…Xiao et al (2017) introduced the back Earth and Space Science 10.1029/2018EA000393 Figure 1. Corresponding periods of F10.7 (Ma, 2007). propagation (BP) neural network to forecast the F10.7. The BP neural network is a multilayer feedforward neural network, which is trained by error BP algorithm.…”

Section: Introductionmentioning

confidence: 99%

Linear Multistep F10.7 Forecasting Based on Task Correlation and Heteroscedasticity

Wang

Zhong

et al. 2018

Earth and Space Science

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“…Its extension, the cross-wavelet transform and wavelet coherence, can be used to reveal similarities in the states of the two systems and allow us to study the synchronization or phase difference in two time series [13]- [14].…”

Section: Methodsmentioning

confidence: 99%

An investigation of north-south asynchrony of solar filament activity: Wavelet transform analyses

Yu¹,

Deng

Feng

2013

2013 IEEE Third International Conference on Information Science and Technology (ICIST)

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Monthly counts of filament numbers in the time interval of 1954 April to 2010 June are proposed to determine their phase asynchrony between the solar northern and southern hemispheres by using advanced wavelet transforms. Both the cross-wavelet transform and wavelet coherence analyses show asynchronous behaviour with strong phase mixing in the highfrequency components of hemispheric flare activity, and strong synchronous behaviour with coherent phase angles in the lowfrequency components, corresponding to the period-scales around the 11-year Schwabe cycle. This study shows that the wavelet transforms techniques for the hemispheric phase analysis of solar time series is fairly useful.

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“…Changes in solar EUV radiation are produced by the nonuniform distribution and motion of EUV radiation sources, such as active regions, over the Sun surface as well as the random fluctuation of radiation intensity. As the Sun rotates with a quasi‐27 day period, the solar EUV flux measured at 1 AU shows a variation with a broad spectral peak between about 22 and 32 days [ Pap et al , 1990; Kane et al , 2001; Kane , 2002, 2003a; Woods et al , 2005; Ma et al , 2007]. Similarly, there are also quasi‐27 day variations in solar wind and interplanetary magnetic field (IMF) as solar active regions near solar maximum and corotating interaction regions (CIRs) near solar minimum rotate with the Sun.…”

Section: Introductionmentioning

confidence: 99%

The effect of ∼27 day solar rotation on ionospheric F₂ region peak densities (N_mF₂)

Ma¹,

Xu²,

Wang³

et al. 2012

J. Geophys. Res.

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[1] Ionospheric F 2 region peak electron densities (N m F 2 ) observed from 11 ionosonde stations in the East Asian-Australian sector from 1969 to 1986 have been used to investigate the effect of $27 day solar rotation on the ionosphere. These stations were located from the magnetically equatorial regions to the middle latitudes in both hemispheres. We found that, averaged over all stations and for 18 years, the normalized standard deviation of the midday $27 day variations of N m F 2 was 8% and that of the midnight variations was 10%. We applied different data analysis methods, including Fourier transform, band-pass filter, and multiple linear regression analysis, to determine quantitatively the sources of the observed $27 day variations of N m F 2 and their relative contributions to these variations. Our results show that the $27 day variations in solar radiation and geomagnetic activity, caused by solar rotation, are the main drivers of the ionospheric $27 day variations. They accounted for more than 85% of the variations seen in the N m F 2 $27 day variation, and their contributions became about 95% at higher latitudes. At geomagnetically low latitudes, the contribution of the $27 day variation in solar EUV radiation was greater than that of the $27 day variation in geomagnetic activity. However, the contribution from geomagnetic activity became more significant and was even larger than the contribution of solar radiation at higher latitudes, especially at midnight. At all latitudes the correlation between the $27 day variations of N m F 2 and solar radiation was evidently positive, whereas that between N m F 2 and geomagnetic activity was positive at geomagnetically low latitudes and became negative at higher middle latitudes. We did not found large seasonal or solar cycle changes in the $27 day variations of N m F 2 . These variations, however, did show significant differences between the two hemispheres.

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Wavelet Analysis of the Quasi-27d Oscillations of Solar Index F10.7

Cited by 8 publications

References 10 publications

Linear Multistep F10.7 Forecasting Based on Task Correlation and Heteroscedasticity

Linear Multistep F10.7 Forecasting Based on Task Correlation and Heteroscedasticity

An investigation of north-south asynchrony of solar filament activity: Wavelet transform analyses

The effect of ∼27 day solar rotation on ionospheric F₂ region peak densities (N_mF₂)

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Wavelet Analysis of the Quasi-27d Oscillations of Solar Index F10.7

Cited by 8 publications

References 10 publications

Linear Multistep F10.7 Forecasting Based on Task Correlation and Heteroscedasticity

Linear Multistep F10.7 Forecasting Based on Task Correlation and Heteroscedasticity

An investigation of north-south asynchrony of solar filament activity: Wavelet transform analyses

The effect of ∼27 day solar rotation on ionospheric F2 region peak densities (NmF2)

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The effect of ∼27 day solar rotation on ionospheric F₂ region peak densities (N_mF₂)