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Title: PreMevE: New Predictive Model for Megaelectron-volt Electrons inside Earth's Outer Radiation Belt

Journal Article · · Space Weather
DOI:https://doi.org/10.1029/2018SW002095· OSTI ID:1496751
ORCiD logo [1]; ORCiD logo [1]; ORCiD logo [2]; ORCiD logo [3]
  1. Los Alamos National Lab. (LANL), Los Alamos, NM (United States); New Mexico Consortium, Los Alamos, NM (United States)
  2. New Mexico Consortium, Los Alamos, NM (United States)
  3. Los Alamos National Lab. (LANL), Los Alamos, NM (United States)
+ Show Author Affiliations

Abstract This work designs a new model called PreMevE to predict storm time distributions of relativistic electrons within Earth's outer radiation belt. This model takes advantage of the cross‐energy, cross‐L‐shell, and cross‐pitch angle coherence associated with wave‐electron resonant interactions, ingests observations from belt boundaries—mainly by a National Oceanic and Atmospheric Administration Polar Operational Environmental Satellite in low‐Earth orbit, and provides high‐fidelity nowcast (multiple‐hour prediction) and forecast (>~1 day) of MeV electron fluxes over L‐shells between 2.8 and 7 through linear prediction filters. PreMevE can not only reliably anticipate incoming enhancements of MeV electrons during storms with at least 1‐day forewarning time but also accurately specify the evolving event‐specific electron spatial distributions afterward. The performance of PreMevE is assessed against long‐term in situ data from one Van Allen Probe and a Los Alamos National Laboratory geosynchronous satellite. This new model enhances our preparedness for severe MeV electron events in the future and further adds new science utility to existing and next‐generation low‐Earth orbit space infrastructure.

Research Organization:
Los Alamos National Laboratory (LANL), Los Alamos, NM (United States)
Sponsoring Organization:
USDOE
Grant/Contract Number:
89233218CNA000001
OSTI ID:
1496751
Alternate ID(s):
OSTI ID: 1499815
Report Number(s):
LA-UR-18-29269
Journal Information:
Space Weather, Vol. 17, Issue 3; ISSN 1542-7390
Publisher:
American Geophysical UnionCopyright Statement
Country of Publication:
United States
Language:
English
Citation Metrics:
Cited by: 23 works
Citation information provided by
Web of Science

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Cited By (1)

Quality of Prediction of Daily Relativistic Electrons Flux at Geostationary Orbit by Machine Learning Methods
  • Myagkova, Irina; Efitorov, Alexander; Shiroky, Vladimir
  • Artificial Neural Networks and Machine Learning – ICANN 2019: Text and Time Series: 28th International Conference on Artificial Neural Networks, Munich, Germany, September 17–19, 2019, Proceedings, Part IV, p. 556-565 https://doi.org/10.1007/978-3-030-30490-4_45
book September 2019

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Related Subjects

79 ASTRONOMY AND ASTROPHYSICS
Heliospheric and Magnetospheric Physics
MeV electron events
event‐specific predictions
outer radiation belt
precipitation at low‐earth‐orbits (LEO)
linear predictive filters
LANL GEO observations