Echo state network model for analyzing solar-wind effects on the AU and AL indices

The properties of the auroral electrojets are examined on the basis of a trained machine-learning model. The relationships between solar-wind parameters and the AU and AL indices are modeled with an echo state network (ESN), a kind of recurrent neural network. We can consider this trained ESN model to represent nonlinear effects of the solar-wind inputs on the auroral electrojets. To identify the properties of auroral electrojets, we obtain various synthetic AU and AL data by using various artificial inputs with the trained ESN. The analyses of various synthetic data show that the AU and AL indices are mainly controlled by the solar-wind speed in addition to Bz of the interplanetary magnetic field (IMF) as suggested by the literature. The results also indicate that the solar-wind density effect is emphasized when solar-wind speed is high and when IMF Bz is near zero. This suggests some nonlinear effects of the solar-wind density.

Solar Wind ∼0.15–1.5 keV Electrons around Corotating Interaction Regions at 1 au

Here we present a statistical study of the ∼0.15–1.5 keV suprathermal electrons observed in uncompressed/compressed slow and fast solar wind around 59 corotating interaction regions (CIRs) with good measurements by Wind 3DP from 1995 through 1997. For each of these CIRs, we fit the strahl and halo energy spectra at ∼0.15–1.5 keV to a Kappa function with a Kappa index κ and kinetic temperature T eff. We find that the ∼0.15–1.5 keV strahl electrons behave similarly in both slow and fast wind: the strahl number density n s positively correlates with the solar wind electron temperature T e and interplanetary magnetic field magnitude ∣B∣, while the strahl pitch angle width Θ s decreases with the solar wind speed V sw. These suggest that the strahl electrons are generated by a similar/same process at the Sun in both slow and fast wind that produces these correlations, and the scattering efficiency of strahl in the interplanetary medium (IPM) decreases with V sw. The ∼0.15–1.5 keV halo electrons also behave similarly in both slow and fast wind: the halo parameter positively correlates with the corresponding strahl parameter, and the halo number density n h positively correlates only with T e . These indicate that the halo formation process in the IPM retains most of the strahl properties, but it erases the relationship between n s and ∣B∣. In addition, κ in compressed wind distributes similarly to that in uncompressed wind, for both the strahl and halo. It shows that CIRs at 1 au are not a significant/effective acceleration source for the strahl and halo.

Recent acceleration of the Earth rotation in the summer of 2020: possible causes and effects

The extreme acceleration of the Earth rotation observed in the summer of 2020 is considered. It is concluded that this phenomenon is a consequence of two factors: the longterm acceleration of the Earth rotation, which has been observed since the 1970s, and the extremely strong meteorological excitation of the LOD, which took place in the summer of 2020. The coincidence of the anomaly of the AAM and the geomagnetic Dst index, as well as the correlation between the LOD on the one hand and the solar wind speed and the Gaussian coefficients of the expansion of the Earth’s magnetic field, on the other, are noted. The problem of negative leap second is considered. Preliminary estimates have been made of introduction of a negative leap second, if the current trends in the behavior of UT1-UTC continue. The conclusion is made about the low probability of such an event.

Global Distribution of the Solar Wind Speed Reconstructed from Improved Tomographic Analysis of Interplanetary Scintillation Observations between 1985 and 2019

Computer-assisted tomography (CAT) for interplanetary scintillation (IPS) observations enables the determination of the global distribution of solar wind speed. We compared solar wind speeds derived from the CAT analysis of IPS observations between 1985 and 2019 with in situ observations conducted by the near-Earth and Ulysses spacecraft. From this comparison, we found that solar wind speeds from the IPS observations for 2009–2019 were systematically higher than the in situ observations, whereas those for the period until 2008 were in good agreement with the in situ observations. Further, we found that the discrepancy between IPS and the in situ observations is improved by changing the power index of the empirical relation between the solar wind speed and density fluctuations. The CAT analysis using an optimal value for the power index determined from the comparison between IPS and in situ observations revealed long-term variations in the solar wind speed distribution over three cycles, leading to a better understanding of the time-varying global heliosphere. We found that polar solar winds become highly anisotropic at the Cycle 24/25 minimum, which is a peculiar aspect of this minimum. The IPS observations showed general agreement with the Parker Solar Probe observations around the perihelion of Orbit 1; this supports the reliability of the CAT analysis. The results of this study suggest that the physical properties of solar wind microturbulence may vary with a long-term decline in the solar activity, which provides important implication on the solar wind acceleration.

Magnetic Field Intensity Modification to Force Free Model of Magnetic Clouds: Website of Wind Examples From Launch to July of 2015

We describe a new NASA website that shows normalized magnetic field (B) magnitude profiles within Wind magnetic clouds (MCs) (i.e., observations versus basic model versus modified model) for 209 MCs observed from launch in late 1994 to July of 2015, where model modification is based on the studies of Lepping et al. (Solar Phys, 2017, 292:27) and Lepping et al. (Solar Phys, 2018, 293:162); the basic force free magnetic cloud parameter fitting model employing Bessel functions (Lepping et al., J. Geophys. Res., 1990, 95:11957) is called the LJB model here. The fundamental principles should be applicable to the B-data from any spacecraft at 1 AU. Earlier (in the LJB study), we justified why the field magnitude can be thought of as decoupled from the field direction within an MC, and further, we justified this idea in terms of actual observations seen over a few decades with examples of MCs from Wind data. The model modification is achieved by adding a correction (“Quad”) value to the LJB model (Bessel function) value in the following manner: B (est)/B0 ≈ [LJB Model + Quad (CA,u)], where B0 is the LJB-estimated field magnitude value on the MC’s axis, CA is the relative closest approach (See Supplementary Appendix A), and u is the distance that the spacecraft travels through the MC from its entrance point. In an average sense, the Quad technique is shown to be successful for 82% of the past modeled MCs, when Quality (Q0) is good or excellent (see Supplementary Appendix A). The Quad technique is successful for 78% of MCs when all cases are considered. So Q0 of the MC LJB-fit is not a big factor when the success of the Quad scheme is considered. In addition, it is found that the Quad technique does not work better for MC events with higher solar wind speed. Yearly occurrence frequency of all MC events (NYearly) and those MC events with ΔσN/σN2 ≥ 0.5 (NΔσN/σN2≥0.5) are well correlated, but there is no solar cycle dependence for normalizing NΔσN/σN2≥0.5 with NYearly.

Seasonal features of geomagnetic activity: a study on the solar activity dependence

Seasonal features of geomagnetic activity and their solar-wind–interplanetary drivers are studied using more than five solar cycles of geomagnetic activity and solar wind observations. This study involves a total of 1296 geomagnetic storms of varying intensity identified using the Dst index from January 1963 to December 2019, a total of 75 863 substorms identified from the SuperMAG AL/SML index from January 1976 to December 2019 and a total of 145 high-intensity long-duration continuous auroral electrojet (AE) activity (HILDCAA) events identified using the AE index from January 1975 to December 2017. The occurrence rates of the substorms and geomagnetic storms, including moderate (-50nT≥Dst>-100nT) and intense (-100nT≥Dst>-250nT) storms, exhibit a significant semi-annual variation (periodicity ∼6 months), while the super storms (Dst≤-250 nT) and HILDCAAs do not exhibit any clear seasonal feature. The geomagnetic activity indices Dst and ap exhibit a semi-annual variation, while AE exhibits an annual variation (periodicity ∼1 year). The annual and semi-annual variations are attributed to the annual variation of the solar wind speed Vsw and the semi-annual variation of the coupling function VBs (where V = Vsw, and Bs is the southward component of the interplanetary magnetic field), respectively. We present a detailed analysis of the annual and semi-annual variations and their dependencies on the solar activity cycles separated as the odd, even, weak and strong solar cycles.

Virtual sounding of solar-wind effects on the AU and AL indices based on an echo state network model

The properties of the auroral electrojets are examined on the basis of a trained machine learning model. The relationships between solar-wind parameters and the AU and AL indices are modeled with an echo state network (ESN), a kind of recurrent neural network. We can consider this trained ESN model to represent nonlinear effects of the solar-wind inputs on the auroral electrojets. To identify the properties of auroral electrojets, we obtain various synthetic AU and AL data by using various artificial inputs with the trained ESN. The analyses of various synthetic data show that the AU and AL indices are mainly controlled by the solar-wind speed in addition to Bz of the interplanetary magnetic field (IMF) as suggested by the literature. The results also indicate that the solar-wind density effect is emphasized when solar-wind speed is high and when IMF Bz is near zero. This suggests some nonlinear effects of the solar-wind density.

Star Wars? Space Weather and Electricity Market: Evidence from China

This paper aims to investigate the impact of space weather on China’s electricity market. Based on data products provided by NOAA and the National Energy Administration in China, this paper uses solar wind velocity as a solar weather indicator and the disturbance storm time index as a magnetospheric weather indicator to match monthly Chinese electricity market data over 10 years. Based on a VAR model, we found that (1) space weather increases the demand for electricity in China, and solar wind speed and the geomagnetic index increase the electricity consumption of the whole of Chinese society, as space weather mainly increases the electricity consumption of the secondary and industrial sectors. (2) The geomagnetic index significantly promotes power station revenue. (3) Space weather is associated with increased energy consumption. The geomagnetic index significantly increases the coal consumption rate of fossil power plants in China, but the solar wind speed has nothing to do with the coal consumption rate of fossil power plants.