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Abstract Plasma sheet electron precipitation into the diffuse aurora is critical for magnetosphere‐ionosphere coupling. Recent studies have shown that electron phase space holes can pitch‐angle scatter electrons and may produce plasma sheet electron precipitation. These studies have assumed identical electron hole parameters to estimate electron scattering rates (Vasko et al., 2018,https://doi.org/10.1063/1.5039687). In this study, we have re‐evaluated the efficiency of this scattering by incorporating realistic electron hole properties from direct spacecraft observations into computing electron diffusion rates and lifetimes. The most important electron hole properties in this evaluation are their distributions in velocity and spatial scale and electric field root‐mean‐square intensity (). Using direct measurements of electron holes during a plasma injection event observed by the Van Allen Probe at, we find that when4 mV/m electron lifetimes can drop below 1 h and are mostly within strong diffusion limits at energies below10 keV. During an injection observed by the THEMIS spacecraft at, electron holes with even typical intensities (1 mV/m) can deplete low‐energy (a few keV) plasma sheet electrons within tens of minutes following injections and convection from the tail. Our results confirm that electron holes are a significant contributor to plasma sheet electron precipitation during injections.
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Seasonal Variation of the D‐Region Ionosphere: Very Low Frequency (VLF) and Machine Learning Models
Abstract The D‐region ionosphere (6090 km) plays an important role in long‐range communication and response to solar and space weather; however, it is difficult to directly measure with currently available technology. Very low frequency (VLF) radio remote sensing is one of the more promising approaches, using the efficient reflection of VLF waves from the D‐region. A number of VLF beacons can therefore be turned into diagnostic tools. VLF remote sensing techniques are useful and can provide global coverage, but in practice have been applied to a limited area and often on only a small number of days. In this work, we expand the use of a recently introduced machine learning based approach (Gross & Cohen, 2020,https://doi.org/10.1029/2019JA027135) to observe and model the D‐region electron density using VLF transmitting beacons and receivers. We have extended the model to cover nighttime in addition to daytime, and have applied it to track D‐region waveguide parameters, h’ and, over 400 daytimes and 150 nighttimes on up to 21 transmitter‐receiver paths across the continental US. Using an exponential fit, h’ represents the height of the ionosphere andrepresents the slope of the electron density. Using this data set, we quantify diurnal, daily and seasonal variations of the D‐region ionosphere for both daytime and nighttime D‐region ionosphere. We show that our model identifies expected variations, as well as producing results in line with other previous studies. Additionally, we show that our daytime predictions exhibit a larger autocorrelation at higher time lags than our nighttime predictions, indicating a model with persistence may perform better.
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- PAR ID:
- 10366930
- Publisher / Repository:
- DOI PREFIX: 10.1029
- Date Published:
- Journal Name:
- Journal of Geophysical Research: Space Physics
- Volume:
- 126
- Issue:
- 9
- ISSN:
- 2169-9380
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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