On How High-Latitude Chorus Waves Tip the Balance Between Acceleration and Loss of Relativistic Electrons.

Modeling and observations have shown that energy diffusion by chorus waves is an important source of acceleration of electrons to relativistic energies. By performing long-term simulations using the three-dimensional Versatile Electron Radiation Belt code, in this study, we test how the latitudinal dependence of chorus waves can affect the dynamics of the radiation belt electrons. Results show that the variability of chorus waves at high latitudes is critical for modeling of megaelectron volt (MeV) electrons. We show that, depending on the latitudinal distribution of chorus waves under different geomagnetic conditions, they cannot only produce a net acceleration but also a net loss of MeV electrons. Decrease in high-latitude chorus waves can tip the balance between acceleration and loss toward acceleration, or alternatively, the increase in high-latitude waves can result in a net loss of MeV electrons. Variations in high-latitude chorus may account for some of the variability of MeV electrons

Scattering of Ultra-relativistic Electrons in the Van Allen Radiation Belts Accounting for Hot Plasma Effects.

Electron flux in the Earth's outer radiation belt is highly variable due to a delicate balance between competing acceleration and loss processes. It has been long recognized that Electromagnetic Ion Cyclotron (EMIC) waves may play a crucial role in the loss of radiation belt electrons. Previous theoretical studies proposed that EMIC waves may account for the loss of the relativistic electron population. However, recent observations showed that while EMIC waves are responsible for the significant loss of ultra-relativistic electrons, the relativistic electron population is almost unaffected. In this study, we provide a theoretical explanation for this discrepancy between previous theoretical studies and recent observations. We demonstrate that EMIC waves mainly contribute to the loss of ultra-relativistic electrons. This study significantly improves the current understanding of the electron dynamics in the Earth's radiation belt and also can help us understand the radiation environments of the exoplanets and outer planets

Quantifying the average and the likelihood of increases in space weather indices and in situ measurements during Solar Cycles 20–23

It is known that space weather harshly affects spacecraft performance, yet spacecraft operations and understanding the cause of anomalies can be challenging due to the complexity of environmental metrics. In this work, we analyse five metrics and in-situ measurements (Kp, Dst, and AE index, and high-energy proton and electron flux) throughout Solar Cycles 20–23 (1964 to 2008), and provide a baseline for the environment during the phases of the solar cycles (maximum, minimum, declining or ascending). We define increased activity as activity greater than two median absolute deviations (MADs) above the average activity for each phase. MAD is used, rather than standard deviation, because it is more resilient to outliers. The average and MAD values are tabulated in Table 3 to Table 6. We determine the probability that increased activity occurs 3, 14 or 30 days before a random day to distinguish between increased/quiet activities and to aid in correlating intensifications of the environment and anomalous satellite performance

Modelling relative total electron content in Europe during storm time using a neural network

The ionospheric state is constantly changing and can be described by the integrated electron density estimation commonly known as the total electron content (TEC). The estimate of ionospheric TEC during geomagnetic storms can vary significantly compared to the TEC during quiet conditions. Therefore, it is important that ionospheric models also perform well during perturbed or storm conditions. We developed a neural network (NN)-based model that predicts the storm-time TEC relative to the 27-day median prior to the storm events. The network uses the 27-day median TEC, latitude, longitude, universal time, storm time, solar radio flux index F10.7, global storm index SYM-H and geomagnetic activity index Hp30 as input parameters and the output is the relative TEC with respect to the 27-day median. A storm dataset has been used containing the TEC maps from UQRG global ionosphere maps (GIMs) from the years 1998 until 2020 and comprises in total of 398 storm events. The model was tested with unseen data from 33 storm events that occurred during 2015 and 2020 representing a high- and low solar activity year, respectively. The performance of the storm-time model during the storms in the test dataset was compared with the Neustrelitz TEC model (NTCM) and the NN-based quiet time TEC model, both developed at German Aerospace Center (DLR) and the storm-time model outperforms both

Bayesian inference of quasi-linear radial diffusion parameters using Van Allen Probes

The Van Allen radiation belts in the magnetosphere have been extensively studied using models based on radial diffusion theory, which is based on a quasi-linear approach with prescribed inner and outer boundary conditions. The 1-d diffusion model requires the knowledge of a diffusion coefficient and an electron loss timescale, which are typically parameterized in terms of various quantities such as the spatial ($L$) coordinate or a geomagnetic index (for example, $Kp$). These terms are empirically derived, not directly measurable, and hence are not known precisely, due to the inherent non-linearity of the process and the variable boundary conditions. In this work, we demonstrate a probabilistic approach by inferring the values of the diffusion and loss term parameters, along with their uncertainty, in a Bayesian framework, where identification is obtained using the Van Allen Probe measurements. Our results show that the probabilistic approach statistically improves the performance of the model, compared to the parameterization employed in the literature

Modelling of Storm-Time Relative Total Electron Content using a Fully Connected Neural Network

During geomagnetic storms the total electron content (TEC) can dramatically change compared to quiet-time conditions. Therefore, it is still a challenging task for ionospheric models to predict accurately during storm times. In this work, the relative TEC with respect to the preceding 27-day median TEC is predicted, during storm time for the European region (with longitudes 30°W–50°E and latitudes 32.5°N–70°N) using machine learning techniques. A fully connected neural network (NN) is proposed that uses the 27-day median TEC (referred to as median TEC), latitude, longitude, universal time, storm time, solar radio flux index F10.7, global storm index SYM-H and geomagnetic activity index Hp30 as inputs and the output of the network is the relative TEC. The model was trained with storm-time relative TEC data, computed with UQRG global ionosphere maps (GIMs), from the time period of 1998 until 2019 (2015 is excluded) and contains 365 storms. The model was tested with unseen storm data from 33 storm events during 2015 and 2020. The storm-time relative TEC model’s predictions showed the seasonal behavior of the storms including positive and negative storm phases during winter and summer, respectively, and a mixture of both phases was seen during equinoxes. The relative TEC was converted to the actual TEC, using the median TEC, and was compared to the Neustrelitz TEC model (NTCM) and a NN-based quiet-time TEC model. The storm model outperforms the NTCM by 1.87 TEC units (TECU) and the quiet-time model by 1.34 TECU during storm time

Magnetospheric formation processes of the diffuse aurora

Resonant wave-particle interactions in the Earth's magnetosphere can lead to the scattering of plasma sheet electrons which in turn cause the optical phenomenon of diffuse aurora. Specifically, electrostatic electron cyclotron harmonic (ECH) waves can effectively precipitate hundreds of eV to tens of keV electrons into the upper atmosphere. This process can generally be treated as a diffusion problem, requiring the numerical calculation of bounce-averaged quasi-linear diffusion coefficients. ECH waves are thought to be generated by the loss cone instability of the ambient hot electron distribution. Therefore, the determination of ECH wave-induced scattering rates requires information about the properties of the hot plasma sheet electrons responsible for the wave excitation. We report our progress on analysing the sensitivity of ECH wave-induced electron scattering effects to the temperature of the hot electron components, which has an influence on the growth rate of the waves

Dependence of ECH wave-induced scattering rates on the electron distribution

Through resonant wave-particle interactions with electrostatic electron cyclotron harmonic (ECH) waves, low-energy (100s of eV to 10s of keV) plasma sheet electrons can be scattered into the atmospheric loss cone and contribute to diffuse auroral precipitation. This process consequently influences the evolution of the electron phase space density, but is currently not included in numerical codes simulating the radiation belt dynamics. In order to describe the ECH wave-induced electron scattering process, bounce-averaged quasi-linear diffusion coefficients need to be calculated. As ECH waves are thought to be generated by the loss cone instability of the ambient hot electron distribution, the numerical calculation of ECH wave-induced scattering rates requires the specification of the wave propagation characteristics, the background magnetic field and plasma density as well as properties of the hot plasma sheet electrons responsible for the wave excitation. In this study, we analyze the dependence of the bounce-averaged quasi-linear scattering rates by ECH waves on the temperature of the hot electron components in the electron distribution. By assuming the background plasma parameters based on previous observations, scattering rates are computed for hot electron temperatures varying from hundreds of eV to several keV, which is consistent with observations as well. A wave power spectral profile based on statistical wave properties is assumed and used to calculate weighted diffusion coefficients. We find that the hot electron temperature influences the growth rate and wave normal angle distribution of the waves, changing the pitch angle diffusion coefficients and lifetimes of the electrons near the loss cone