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The near-polar orbit satellites of Swarm mission provide a good opportunity to investigate the conjugacy of equatorial plasma irregularities (EPIs) since their trajectories at low latitudes are basically aligned with fixed geographical longitude. However, the Swarm in situ electron density occasionally shows EPIs at only one hemisphere at this longitude. In this study, we provide detailed analysis of such EPI events from the in situ electron densities and onboard global positioning system (GPS) measurements of Swarm low pair satellites, and simultaneous GPS data from two geomagnetically conjugate ground stations at the Africa longitudes. The result indicates that when Swam in situ electron density sometime shows EPIs at only one hemisphere, the GPS scintillations are still observed from the Swarm onboard receiver and by the two conjugate ground stations. It implies that the EPIs should generally elongate along the geomagnetic flux tube. More than two-year statistic results show that the onset time of scintillation in the northern station is on average 16 and 18 min earlier than that in the southern station for September equinox and December solstice in 2015, while for March equinox in 2016 the onset time of scintillation of northern station is about 11 min later than that of southern station, which indicates the asymmetry features of EPIs along the flux tube. Further analysis of nearly three-year GPS data from two conjugate stations at the Asia longitudes, we find that during solar maximum years the local sunset time plays an important role for causing the difference of onset time of scintillation between two conjugate stations.
Data assimilation aims to blend incomplete and inaccurate data with physics-based dynamical models. In the Earth's radiation belts, it is used to reconstruct electron phase space density, and it has become an increasingly important tool in validating our current understanding of radiation belt dynamics, identifying new physical processes, and predicting the near-Earth hazardous radiation environment. In this study, we perform reanalysis of the sparse measurements from four spacecraft using the three-dimensional Versatile Electron Radiation Belt diffusion model and a split-operator Kalman filter over a 6-month period from 1 October 2012 to 1 April 2013. In comparison to previous works, our 3-D model accounts for more physical processes, namely, mixed pitch angle-energy diffusion, scattering by Electromagnetic Ion Cyclotron waves, and magnetopause shadowing. We describe how data assimilation, by means of the innovation vector, can be used to account for missing physics in the model. We use this method to identify the radial distances from the Earth and the geomagnetic conditions where our model is inconsistent with the measured phase space density for different values of the invariants mu and K. As a result, the Kalman filter adjusts the predictions in order to match the observations, and we interpret this as evidence of where and when additional source or loss processes are active. The current work demonstrates that 3-D data assimilation provides a comprehensive picture of the radiation belt electrons and is a crucial step toward performing reanalysis using measurements from ongoing and future missions.
A comprehensive description of electromagnetic processes related to equatorial plasma depletions (EPDs) is essential for understanding their evolution and day-to-day variability. Recently, field-aligned currents (FACs) flowing at both western and eastern edges of EPDs were observed to be interhemispheric rather than anti-parallel about the dip equator, as suggested by previous theoretical studies. In this paper, we investigate the spatial and temporal variability of the FACs orientation using simultaneous measurements of electron density and magnetic field gathered by ESA’s Swarm constellation mission. By using empirical models, we assess the role of the Pedersen conductance in the preference of the FACs to close either in the northern or southern magnetic hemisphere. Here we show that the closure of the FACs agrees with an electrostatic regime determined by a hemispherical asymmetry of the Pedersen conductance. That is, the EPD-related FACs close at lower altitudes in the hemisphere with the highest conductivity. The evidence of this conclusion stands on the general agreement between the longitudinal and seasonal variability of both the conductivity and the FACs orientation.
Ground and space-based geomagnetic data were used in the investigation of the longitudinal, seasonal and lunar phase dependence of the equatorial counter electrojet (CEJ) occurrence in the Peruvian, Brazilian, African, Indian and Philippine sectors during geomagnetically quiet days from the solar cycle 24 (2008 to 2018). We found that CEJ events occur more frequently during the morning (MCEJ) than in the afternoon (ACEJ). The highest MCEJ and ACEJ occurrence rates were observed for the Brazilian sector. Distinct seasonal dependence was found for each longitudinal sector under investigation. The lunar phase dependence was determined for the first time for the Philippine sector (longitude 125 degrees E), and it was shown to be less pronounced than in the Peruvian, Brazilian and African sectors. We demonstrate that differences in CEJ rates derived from ground-based and satellite data can arise from the longitudinal separation between low-latitude and equatorial stations that are used to determine the signal and its consequent time delay in their sunrise/sunset times at ionospheric heights.
In magnetized plasmas such as the ionosphere, electric currents develop in regions of strong density gradients to balance the resulting plasma pressure gradients. These currents, usually known as diamagnetic currents decrease the magnetic pressure where the plasma pressure increases, and vice versa. In the low‐latitude ionosphere, equatorial plasma depletions (EPDs) are well known for their steep plasma density gradients and adverse effect on radio wave propagation. In this paper, we use continuous measurements of the magnetic field and electron density from the European Space Agency's Swarm constellation mission to assess the balance between plasma and magnetic pressure across large‐scale EPDs. The analysis is based on the magnetic fluctuations related to diamagnetic currents flowing at the edges of EPDs. This study shows that most of the EPDs detected by Swarm present a decrease of the plasma pressure relative to the ambient plasma. However, EPDs with high plasma pressure are also identified mainly in the vicinity of the South Atlantic magnetic anomaly. From the electron density measurements, we deduce that such an increase in plasma pressure within EPDs might be possible by temperatures inside the EPD as high as twice the temperature of the ambient plasma. Due to the distinct location of the high‐pressure EPDs, we suggest that a possible heating mechanism might be due to precipitation of particle from the radiation belts. This finding corresponds to the first observational evidence of plasma pressure enhancements in regions of depleted plasma density in the ionosphere.
The Kp index is a measure of the midlatitude global geomagnetic activity and represents short-term magnetic variations driven by solar wind plasma and interplanetary magnetic field. The Kp index is one of the most widely used indicators for space weather alerts and serves as input to various models, such as for the thermosphere and the radiation belts. It is therefore crucial to predict the Kp index accurately. Previous work in this area has mostly employed artificial neural networks to nowcast Kp, based their inferences on the recent history of Kp and on solar wind measurements at L1. In this study, we systematically test how different machine learning techniques perform on the task of nowcasting and forecasting Kp for prediction horizons of up to 12 hr. Additionally, we investigate different methods of machine learning and information theory for selecting the optimal inputs to a predictive model. We illustrate how these methods can be applied to select the most important inputs to a predictive model of Kp and to significantly reduce input dimensionality. We compare our best performing models based on a reduced set of optimal inputs with the existing models of Kp, using different test intervals, and show how this selection can affect model performance.