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Abstract Whistler mode waves scatter energetic electrons, causing them to precipitate into the Earth's atmosphere. While the interactions between whistler mode waves and electrons are well understood, the global distribution of electron precipitation driven by whistler mode waves needs futher investigations. We present a two‐stage method, integrating neural networks and quasi‐linear theory, to simulate global electron precipitation driven by whistler mode waves. By applying this approach to the 17 March 2013 geomagnetic storm event, we reproduce the rapidly varying precipitation pattern over various phases of the storm. Then we validate our simulation results with POES/MetOp satellite observations. The precipitation pattern is consistent between simulations and observations, suggesting that most of the observed electron precipitation can be attributed to scattering by whistler mode waves. Our results indicate that chorus waves drive electron precipitation over the premidnight‐to‐afternoon sector during the storm main phase, with simulated peak energy fluxes of 20 erg/cm²/s and characteristic energies of 10–50 keV. During the recovery phase, plume hiss in the afternoon sector can have a comparable or stronger effect than chorus, with peak fluxes of ∼1 erg/cm²/s and characteristic energies between 10 and 200 keV. This study highlights the importance of integrating physics‐based and deep learning approaches to model the complex dynamics of electron precipitation driven by whistler mode waves. 

We analyze the properties of relativistic (>700 keV) electron precipitation (REP) events measured by the low-Earth-orbit (LEO) POES/MetOp constellation of spacecraft from 2012 through 2023. Leveraging the different profiles of REP observed at LEO, we associate each event with its possible driver: waves or field line curvature scattering (FLCS). While waves typically precipitate electrons in a localized radial region within the outer radiation belt, FLCS drives energy-dependent precipitation at the edge of the belt. Wave-driven REP is detected at any MLT sector and L shell, with FLCS-driven REP occurring only over the nightside–a region where field line stretching is frequent. Wave-driven REP is broader in radial extent on the dayside and accompanied by proton precipitation over 03–23 MLT, either isolated or without a clear energy-dependent pattern, possibly implying that electromagnetic ion cyclotron (EMIC) waves are the primary driver. Across midnight, both wave-driven and FLCS-driven REP occur poleward of the proton isotropic boundary. On average, waves precipitate a higher flux of >700 keV electrons than FLCS. Both contribute to energy deposition into the atmosphere, estimated of a few MW. REP is more associated with substorm activity than storms, with FLCS-driven REP and wave-driven REP at low L shells occurring most often during strong activity (SML* < −600 nT). A preliminary analysis of the Solar Wind (SW) properties before the observed REP indicates a more sustained (∼5 h) dayside reconnection for FLCS-driven REP than for wave-driven REP (∼3 h). The magnetosphere appears more compressed during wave-driven REP, while FLCS-driven REP is associated with a faster SW of lower density. These findings are useful not only to quantify the contribution of >700 keV precipitation to the atmosphere but also to shed light on the typical properties of wave-driven vs FLCS-driven precipitation which can be assimilated into physics-based and/or predictive radiation belt models. In addition, the dataset of ∼9,400 REP events is made available to the community to enable future work. 

Abstract We statistically evaluate the global distribution and energy spectrum of electron precipitation at low‐Earth‐orbit, using unprecedented pitch‐angle and energy resolved data from the Electron Losses and Fields INvestigation CubeSats. Our statistical results indicate that during active conditions, the ∼63 keV electron precipitation ratio peaks atL > 6 at midnight, whereas the spatial distribution of precipitating energy flux peaks between the dawn and noon sectors. ∼1 MeV electron precipitation ratio peaks near midnight atL > ∼6 but is enhanced near dusk during active times. The energy spectrum of the precipitation ratio shows reversal points indicating energy dispersion as a function ofLshell in both the slot region and atL > ∼6, consistent with hiss‐driven precipitation and current sheet scattering, respectively. Our findings provide accurate quantification of electron precipitation at various energies in a broad region of the Earth's magnetosphere, which is critical for magnetosphere‐ionosphere coupling. 

Electromagnetic ion cyclotron (EMIC) waves can scatter radiation belt electrons with energies of a few hundred keV and higher. To accurately predict this scattering and the resulting precipitation of these relativistic electrons on short time scales, we need detailed knowledge of the wave field’s spatio-temporal evolution, which cannot be obtained from single spacecraft measurements. Our study presents EMIC wave models obtained from two-dimensional (2D) finite-difference time-domain (FDTD) simulations in the Earth’s dipole magnetic field. We study cases of hydrogen band and helium band wave propagation, rising-tone emissions, packets with amplitude modulations, and ducted waves. We analyze the wave propagation properties in the time domain, enabling comparison within situobservations. We show that cold plasma density gradients can keep the wave vector quasiparallel, guide the wave energy efficiently, and have a profound effect on mode conversion and reflections. The wave normal angle of unducted waves increases rapidly with latitude, resulting in reflection on the ion hybrid frequency, which prohibits propagation to low altitudes. The modeled wave fields can serve as an input for test-particle analysis of scattering and precipitation of relativistic electrons and energetic ions. 

Hiss waves play an important role in removing energetic electrons from Earth’s radiation belts by precipitating them into the upper atmosphere. Compared to plasmaspheric hiss that has been studied extensively, the evolution and effects of plume hiss are less understood due to the challenge of obtaining their global observations at high cadence. In this study, we use a neural network approach to model the global evolution of both the total electron density and the hiss wave amplitudes in the plasmasphere and plume. After describing the model development, we apply the model to a storm event that occurred on 14 May 2019 and find that the hiss wave amplitude first increased at dawn and then shifted towards dusk, where it was further excited within a narrow region of high density, namely, a plasmaspheric plume. During the recovery phase of the storm, the plume rotated and wrapped around Earth, while the hiss wave amplitude decayed quickly over the nightside. Moreover, we simulated the overall energetic electron evolution during this storm event, and the simulated flux decay rate agrees well with the observations. By separating the modeled plasmaspheric and plume hiss waves, we quantified the effect of plume hiss on energetic electron dynamics. Our simulation demonstrates that, under relatively quiet geomagnetic conditions, the region with plume hiss can vary from L = 4 to 6 and can account for up to an 80% decrease in electron fluxes at hundreds of keV at L > 4 over 3 days. This study highlights the importance of including the dynamic hiss distribution in future simulations of radiation belt electron dynamics. 

We show an application of supervised deep learning in space sciences. We focus on the relativistic electron precipitation into Earth’s atmosphere that occurs when magnetospheric processes (wave-particle interactions or current sheet scattering, CSS) violate the first adiabatic invariant of trapped radiation belt electrons leading to electron loss. Electron precipitation is a key mechanism of radiation belt loss and can lead to several space weather effects due to its interaction with the Earth’s atmosphere. However, the detailed properties and drivers of electron precipitation are currently not fully understood yet. Here, we aim to build a deep learning model that identifies relativistic precipitation events and their associated driver (waves or CSS). We use a list of precipitation events visually categorized into wave-driven events (REPs, showing spatially isolated precipitation) and CSS-driven events (CSSs, showing an energy-dependent precipitation pattern). We elaborate the ensemble of events to obtain a dataset of randomly stacked events made of a fixed window of data points that includes the precipitation interval. We assign a label to each data point: 0 is for no-events, 1 is for REPs and 2 is for CSSs. Only the data points during the precipitation are labeled as 1 or 2. By adopting a long short-term memory (LSTM) deep learning architecture, we developed a model that acceptably identifies the events and appropriately categorizes them into REPs or CSSs. The advantage of using deep learning for this task is meaningful given that classifying precipitation events by its drivers is rather time-expensive and typically must involve a human. After post-processing, this model is helpful to obtain statistically large datasets of REP and CSS events that will reveal the location and properties of the precipitation driven by these two processes at all L shells and MLT sectors as well as their relative role, thus is useful to improve radiation belt models. Additionally, the datasets of REPs and CSSs can provide a quantification of the energy input into the atmosphere due to relativistic electron precipitation, thus offering valuable information to space weather and atmospheric communities.