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

Wave-induced loss of ultra-relativistic electrons in the Van Allen radiation belts.

The dipole configuration of the Earth's magnetic field allows for the trapping of highly energetic particles, which form the radiation belts. Although significant advances have been made in understanding the acceleration mechanisms in the radiation belts, the loss processes remain poorly understood. Unique observations on 17 January 2013 provide detailed information throughout the belts on the energy spectrum and pitch angle (angle between the velocity of a particle and the magnetic field) distribution of electrons up to ultra-relativistic energies. Here we show that although relativistic electrons are enhanced, ultra-relativistic electrons become depleted and distributions of particles show very clear telltale signatures of electromagnetic ion cyclotron wave-induced loss. Comparisons between observations and modelling of the evolution of the electron flux and pitch angle show that electromagnetic ion cyclotron waves provide the dominant loss mechanism at ultra-relativistic energies and produce a profound dropout of the ultra-relativistic radiation belt fluxes

Discussions on stakeholder requirements for space weather related models

Participants of the 2017 European Space Weather Week in Ostend, Belgium, discussed the stakeholder requirements for space weather related models. It was emphasized that stakeholders show an increased interest in space weather related models. Participants of the meeting discussed particular prediction indicators that can provide first order estimates of the impact of space weather on engineering systems

Strong whistler mode waves observed in the vicinity of Jupiter’s moons

Understanding of wave environments is critical for the understanding of how particles are accelerated and lost in space. This study shows that in the vicinity of Europa and Ganymede, that respectively have induced and internal magnetic fields, chorus wave power is significantly increased. The observed enhancements are persistent and exceed median values of wave activity by up to 6 orders of magnitude for Ganymede. Produced waves may have a pronounced effect on the acceleration and loss of particles in the Jovian magnetosphere and other astrophysical objects. The generated waves are capable of significantly modifying the energetic particle environment, accelerating particles to very high energies, or producing depletions in phase space density. Observations of Jupiter’s magnetosphere provide a unique opportunity to observe how objects with an internal magnetic field can interact with particles trapped in magnetic fields of larger scale objects

Model Evaluation Guidelines for Geomagnetic Index Predictions

Geomagnetic indices are convenient quantities that distill the complicated physics of some region or aspect of near‐Earth space into a single parameter. Most of the best‐known indices are calculated from ground‐based magnetometer data sets, such as Dst, SYM‐H, Kp, AE, AL, and PC. Many models have been created that predict the values of these indices, often using solar wind measurements upstream from Earth as the input variables to the calculation. This document reviews the current state of models that predict geomagnetic indices and the methods used to assess their ability to reproduce the target index time series. These existing methods are synthesized into a baseline collection of metrics for benchmarking a new or updated geomagnetic index prediction model. These methods fall into two categories: (1) fit performance metrics such as root‐mean‐square error and mean absolute error that are applied to a time series comparison of model output and observations and (2) event detection performance metrics such as Heidke Skill Score and probability of detection that are derived from a contingency table that compares model and observation values exceeding (or not) a threshold value. A few examples of codes being used with this set of metrics are presented, and other aspects of metrics assessment best practices, limitations, and uncertainties are discussed, including several caveats to consider when using geomagnetic indices.Plain Language SummaryOne aspect of space weather is a magnetic signature across the surface of the Earth. The creation of this signal involves nonlinear interactions of electromagnetic forces on charged particles and can therefore be difficult to predict. The perturbations that space storms and other activity causes in some observation sets, however, are fairly regular in their pattern. Some of these measurements have been compiled together into a single value, a geomagnetic index. Several such indices exist, providing a global estimate of the activity in different parts of geospace. Models have been developed to predict the time series of these indices, and various statistical methods are used to assess their performance at reproducing the original index. Existing studies of geomagnetic indices, however, use different approaches to quantify the performance of the model. This document defines a standardized set of statistical analyses as a baseline set of comparison tools that are recommended to assess geomagnetic index prediction models. It also discusses best practices, limitations, uncertainties, and caveats to consider when conducting a model assessment.Key PointsWe review existing practices for assessing geomagnetic index prediction models and recommend a “standard set” of metricsAlong with fit performance metrics that use all data‐model pairs in their formulas, event detection performance metrics are recommendedOther aspects of metrics assessment best practices, limitations, uncertainties, and geomagnetic index caveats are also discussedPeer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/147764/1/swe20790_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147764/2/swe20790.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/147764/3/swe20790-sup-0001-2018SW002067-SI.pd

Reconstruction of electron radiation belts using data assimilation and machine learning

We present a reconstruction of radiation belt electron fluxes using data assimilation with low-Earth-orbiting Polar Orbiting Environmental Satellites (POES) measurements mapped to near equatorial regions. Such mapping is a challenging task and the appropriate methodology should be selected. To map POES measurements, we explore two machine learning methods: multivariate linear regression (MLR) and neural network (NN). The reconstructed flux is included in data assimilation with the Versatile Electron Radiation Belts (VERB) model and compared with Van Allen Probes and GOES observations. We demonstrate that data assimilation using MLR-based mapping provides a reasonably good agreement with observations. Furthermore, the data assimilation with the flux reconstructed by NN provides better performance in comparison to the data assimilation using flux reconstructed by MLR. However, the improvement by adding data assimilation is limited when compared to the purely NN model which by itself already has a high performance of predicting electron fluxes at high altitudes. In the case an optimized machine learning model is not possible, our results suggest that data assimilation can be beneficial for reconstructing outer belt electrons by correcting errors of a machine learning based LEO-to-MEO mapping and by providing physics-based extrapolation to the parameter space portion not included in the LEO-to-MEO mapping, such as at the GEO orbit in this study