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DOE OSTI2025

Predicting cutoff L-shells of solar protons using the GPPSn particle dataset

Solar energetic protons (SEPs) arriving at the Earth trigger severe radiation storms in the near-Earth space, directly impacting space missions operating at various altitudes. Therefore, monitoring SEP events and predicting the penetration depths of solar protons are critical for aerospace sectors. Building on previous efforts, here we demonstrate the feasibility of using proton measurements from the Global Prompt Proton Sensor network (GPPSn), enabled by Los Alamos National Laboratory developed combined X-ray dosimeters aboard GPS satellites, to characterize and predict the penetration of solar protons into the geomagnetic field. The inclined medium-Earth-orbits (MEOs) of the global GPS constellation offer a unique advantage of allowing simultaneous measurements of penetrating solar protons inside both open- and closed-field line regions. Therefore, the L-profiles of ∼10s–100 MeV solar protons and their associated cutoff L-shells can be determined from the GPPSn dataset, using predefined threshold proton flux values rather than traditional flux ratios. After examining a list of SEP event intervals across solar cycles 23, 24 and 25—including the 2024 Mother’s Day superstorm, we showcase how the latest GPPSn proton dataset (release v1.10), reprocessed and calibrated, can not only be used to monitor solar proton distributions inside the dynamic geomagnetic field for individual events, but also to derive a new empirical model linking cutoff L-shells with several key space weather parameters. This newly developed SEPCL-MEO model demonstrates high predictive performance; for example, predictions for > 30 MeV solar protons yield a correlation coefficient of 0.85 and performance efficiency of 0.67 when validated against GPPSn observations. Results from this pilot study underscores the scientific and operational value of the GPPSn dataset, and this dataset—when paired with machine-learning techniques—can play a critical role in observing and predicting the effects of future incoming SEP events, including extreme ones.

58 GEOSCIENCES↗

DOE OSTI2023

Outer Radiation Belt Flux and Phase Space Density Response to Sheath Regions: Van Allen Probes and GPS Observations

Turbulent and compressed sheath regions preceding interplanetary coronal mass ejections strongly impact electron dynamics in the outer radiation belt. Changes in electron flux can occur on timescales of tens of minutes, which are unlikely to be captured by a two-satellite mission. The recently released Global Positioning System (GPS) data set generally has shorter revisit times (at L ~ 4–8) owing to the large number of satellites in the constellation equipped with energetic particle detectors. Investigating electron fluxes at energies from 140 keV to 4 MeV and sheaths observed in 2012–2018, we show that the flux response to sheaths on a timescale of 6 hr, previously reported from Van Allen Probes (RBSP) data, is reproduced by GPS measurements. Furthermore, GPS data enables derivation of the response on a timescale of 30 min, which further confirms that the energy and L-shell dependent changes in electron flux are associated with the impact of the sheath. Sheath-driven loss is underestimated over longer timescales as the electrons recover during the ejecta. We additionally show the response of electron phase space density (PSD), which is a key quantity in identifying non-adiabatic loss from the system and electron energization through wave-particle interactions. The PSD response is calculated from both RBSP and GPS data for the 6 hr timescale, as well as from GPS data for the 30 min timescale. The response is divided based on the geoeffectiveness of the sheaths revealing that electrons are effectively accelerated only during geoeffective sheaths, while loss commonly occurs during all sheaths.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2022

Comparison of Energetic Electron Fluxes Measured by GPS and THEMIS Spacecraft in the Inner Magnetosphere

Key elements of space weather models are energetic electron fluxes in the inner magnetosphere and the outer radiation belt. Flux depletion is driven by various loss processes: scattering into atmosphere, magnetopause shadowing. Flux enhancement is driven by various acceleration processes: local wave-particle interactions, radial transport, plasma sheet injections. Many of these processes operate on ~ hour timescales. Such mesoscale flux variations are not well traced by equatorial spacecraft with much longer orbits. Energetic electron detectors onboard the Global Positioning System (GPS) constellation provide a unique opportunity for probing such ~ hour-scale flux variations. Measurements from up to 23 identically instrumented GPS satellites cover a wide energy and L-shell range with a subhour time resolution. However, their orbits are inclined and thus all measurements at L-shell >4.3 are off-equatorial. Here, in this report, we present a comparison of equatorial THEMIS and nonequatorial GPS measurements of omnidirectional ≤600 keV electron fluxes. Such a comparison allows us to derive coefficients for using off-equatorial GPS fluxes to infer the equatorial values. These coefficients depend on particle energy and L-shell. We demonstrate a new data set derived from GPS measurements and discuss how it can be used to investigate mesoscale dynamics of energetic electron fluxes in the inner magnetosphere.

79 ASTRONOMY AND ASTROPHYSICS↗

DOE OSTI2020

Global Prompt Proton Sensor Network: Monitoring Solar Energetic Protons Based on GPS Satellite Constellation

Energetic particle instruments on board Global Positioning System (GPS) satellites form a powerful global prompt proton sensor network (GPPSn) that provides an unprecedented opportunity to monitor and characterize solar energetic protons targeting the Earth. The medium-Earth orbits of the GPS constellation have the unique advantage of allowing solar energetic protons to be simultaneously measured from multiple points in both open- and closed-field line regions. Examining two example intervals of solar proton events, we showcase in this study how GPS proton data are prepared, calibrated, and utilized to reveal important features of solar protons, including their source, acceleration/scattering by interplanetary shocks, the relative position of Earth when impinged by these shocks, the shape of solar particle fronts, the access of solar protons inside the dynamic geomagnetic field, and temporally varying proton distributions in both energy and space. By comparing to Van Allen Probes data, GPS proton observations are further demonstrated not only to be useful for qualitatively monitoring the dynamics of solar protons but also for quantitative scientific research including determining cutoff L-shells. Finally, our results establish that this GPPSn can join forces with other existing solar proton monitors and contribute to observing, warning, understanding, and ultimately forecasting the incoming solar energetic proton events.

47 OTHER INSTRUMENTATION↗

DOE OSTI2021

Data-Driven Specification of Earth's Magnetic Field

As our society is increasingly dependent on technologies in space, predicting space weather becomes ever more important. We have come to rely on technologies such as cellphones, GPS, the Internet, and other commercial and military assets. In order to protect these systems a high priority is to develop better understanding of the harmful conditions of space weather encountered in the near-Earth radiation environment which are mostly dictated by the dynamics of energetic charged particles. The main objective of our proposal was to improve the specification and forecast of the particle distribution in the Earth’s inner magnetosphere using data assimilation together with machine learning (ML) algorithms in our ring current-atmosphere interactions model with self-consistent magnetic and electric fields (RAM-SCBE). Specifically, we explored the application of machine learning algorithms to emulate and improve, through infusion of observational data, the magnetic field (SCBE) of our ring current model. Our work advanced towards the characterization of the RAM-SCBE particle flux field and its potentially different simulation scenarios. We explored how the model behaves in a number of simulation scenarios in order to better understand how the model reacts to perturbations in the solar wind. We were able to capture this behavior through the simulation of an ensemble of RAM-SCB model solutions. The ensemble showed the important correlations between the particle fluxes and the magnetic field. We were able to start the training of a neural network (Autoencoder) to identify the dominant features of the magnetic field, and the main correlations (linear and non-linear) between the particle fluxes and the magnetic field. Our next steps consists of training a neural network to capture the time evolution of the dominant features and obtain a consistent surrogate model for the RAM-SCB.

42 ENGINEERING↗

DOE OSTI2024

PreMevE‐MEO: Predicting Ultra‐Relativistic Electrons Using Observations From GPS Satellites

Abstract Ultra‐relativistic electrons with energies greater than or equal to two megaelectron‐volt (MeV) pose a major radiation threat to spaceborne electronics, and thus specifying those highly energetic electrons has a significant meaning to space weather communities. Here we report the latest progress in developing our predictive model for MeV electrons in the outer radiation belt. The new version, primarily driven by electron measurements made along medium‐Earth‐orbits (MEO), is called PREdictive MEV Electron (PreMevE)‐MEO model that nowcasts ultra‐relativistic electron flux distributions across the whole outer belt. Model inputs include >2 MeV electron fluxes observed in MEOs by a fleet of GPS satellites as well as electrons measured by one Los Alamos satellite in the geosynchronous orbit. We developed an innovative Sparse Multi‐Inputs Latent Ensemble NETwork (SmileNet) which combines convolutional neural networks with transformers, and we used long‐term in situ electron data from NASA's Van Allen Probes mission to train, validate, optimize, and test the model. It is shown that PreMevE‐MEO can provide hourly nowcasts with high model performance efficiency and high correlation with observations. This prototype PreMevE‐MEO model demonstrates the feasibility of making high‐fidelity predictions driven by observations from longstanding space infrastructure in MEO, thus has great potential of growing into an invaluable space weather operational warning tool.

79 ASTRONOMY AND ASTROPHYSICS↗