Energetic Electron Flux Predictions in the Near‐Earth Plasma Sheet From Solar Wind Driving

Swiger, B. M.; Liemohn, Michael W.; Ganushkina, N. Y.; Dubyagin, S.

doi:10.1029/2022sw003150

Cited by 8 publications

(8 citation statements)

References 104 publications

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“…To the best of our knowledge, only a few studies have evaluated the effects of space weather on aviation by systematically considering space weather, satellite navigation, and ATM together. In previous studies, Solar activity has shown an 11-year cycle (Luhmann et al, 2022;Swiger et al, 2022). The annual mean total sunspot number since 1900 is illustrated in Figure 1.…”

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confidence: 91%

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Forward‐Looking Study of Solar Maximum Impact in 2025: Effects of Satellite Navigation Failure on Aviation Network Operation in the Greater Bay Area, China

Xue,

Yang,

Liu

et al. 2023

Space Weather

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Satellite navigation based on the Global Navigation Satellite System can provide aircraft with more precise guidance and increase flight efficiency. However, severe space weather events can cause satellite navigation failure due to the dramatic increase in total electron content and irregularities in the ionosphere. Consequently, ground navigation has to be used to replace satellite navigation, increasing aircraft separation standards and reducing airspace capacity. As a result, numerous flights may be delayed or even canceled, incurring significant financial losses. The occurrence peak of space weather events generally coincides with the 11‐year‐cycle solar maximum, and 2025 is expected to be the upcoming solar maximum. The Greater Bay Area (GBA), located in the equatorial ionization anomaly region of China, is particularly vulnerable to space weather impacts. To explore the effects of satellite navigation failure on flight operation, we conduct this looking‐forward study and propose solution methods from the standpoint of Air Traffic Management, by simulating satellite navigation failure scenarios. Based on the projected flight volume in 2025 related to the GBA airports, simulation results show that the economic costs can be tens of millions of Euros, which is dependent on the duration of satellite navigation failure and the time interval of ground navigation‐based landing. We believe that this study can be a benchmark for evaluating the potential economic effects of forthcoming space weather on flight operations.

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confidence: 91%

“…Solar activity has shown an 11‐year cycle (Luhmann et al., 2022; Swiger et al., 2022). The annual mean total sunspot number since 1900 is illustrated in Figure 1.…”

Section: Introductionmentioning

confidence: 99%

Forward‐Looking Study of Solar Maximum Impact in 2025: Effects of Satellite Navigation Failure on Aviation Network Operation in the Greater Bay Area, China

Xue,

Yang,

Liu

et al. 2023

Space Weather

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“…There exists another class of empirical models that output average environmental characteristics based on solar wind parameters or geomagnetic activity indices (e.g., Denton et al, 2015;Sillanpää et al, 2017;Stepanov et al, 2021). A subclass within this category employs a machine learning approach for determining plasmaspheric density (Bortnik et al, 2016;Chu et al, 2017;Zhelavskaya et al, 2017;Zhou et al, 2022), and for predicting electron fluxes (Boynton et al, 2016(Boynton et al, , 2019Simms et al, 2022Simms et al, , 2023Swiger et al, 2022). However, these models pose challenges for practical application in risk assessment, as neither the solar wind parameters nor the activity indices are known in advance for future missions.…”

Section: Introductionmentioning

confidence: 99%

PEMEM Percentile: New Plasma Environment Specification Model for Surface Charging Risk Assessment

Dubyagin,

Ganushkina,

Sicard

et al. 2024

JGR Space Physics

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The Plasma Environment Modeling in the Earth's Magnetosphere (PEMEM) is a European Space Agency activity supporting the development of a new specification model for the spacecraft surface charging risk assessment. This paper presents a description of the basic model version: the PEMEM percentile model. The model is intended to be used for space missions with near‐equatorial orbits. The model is based on the Van Allen Probes particle measurements inside the geostationary orbit. The model's primary input is a planned spacecraft trajectory. It outputs statistical characteristics of the plasma environment which are expected to be encountered during a mission lifetime. These characteristics include differential electron and proton flux percentiles for a set of energies (percentile spectra), and percentiles of the integrated electron flux. The model covers the energy range of 1–100 keV for electrons and 40 eV–51 keV for protons. Since extreme spacecraft charging usually occurs in the eclipse, the same characteristics can be separately output for the periods when the spacecraft is shadowed by the Earth.

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“…In this study, we explore the ability of several multivariable prediction model types to predict electron flux that have been used at various electron energies: neural networks (e.g., Chu et al, 2021;Freeman et al, 1998;Katsavrias et al, 2022;Koons and Gorney, 1991;Ling et al, 2010;Ma et al, 2022;Simms and Engebretson, 2020;Smirnov et al, 2020;Swiger et al, 2022), autoregressive MA time series transfer functions (ARMAX) (Balikhin et al, 2011;Boynton et al, 2013Boynton et al, , 2015Simms & Engebretson, 2020;Simms, Engebretson, Clilverd, Rodger, Lessard, et al, 2018), conventional regression (value-predicting) (Simms et al, 2014(Simms et al, , 2016, and logistic regression (which classifies predictions into groups) (Capman et al, 2019;Neter et al, 1990;Simms & Engebretson, 2020).…”

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confidence: 99%

Predicting Geostationary 40–150 keV Electron Flux Using ARMAX (an Autoregressive Moving Average Transfer Function), RNN (a Recurrent Neural Network), and Logistic Regression: A Comparison of Models

et al. 2023

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We screen several algorithms for their ability to produce good predictive models of hourly 40–150 keV electron flux at geostationary orbit (data from GOES‐13) using solar wind, Interplanetary Magnetic Field, and geomagnetic index parameters that would be available for real time forecasting. Value‐predicting models developed using ARMAX (autoregressive moving average transfer function), RNN (recurrent neural network), or stepwise‐reduced regression produced roughly similar results. Including magnetic local time as a categorical variable to describe both the differing levels of flux and the differing influence of parameters improved the models (r as high as 0.814; Heidke Skill Score (HSS) as high as 0.663), however value‐predicting models did a poor job at predicting highs and lows. Diagnostic tests are introduced (cubic fit to observation‐prediction relationship and Lag1 correlation) that better assess predictions of extremes than single metrics such as root mean square error, mean absolute error, or median symmetric accuracy. Classifier models (RNN and logistic regression) were equally able to predict flux rise above the 75th percentile (HSS as high as 0.667). Logistic regression models were improved by the addition of multiplicative interaction and quadratic terms. Only predictors from 1 or 3 hr before were necessary and a detailed description of flux time series behavior was not needed. Stepwise selection of these variables trimmed non‐contributing parameters for a more parsimonious and portable logistic regression model that predicted as well as neural network‐derived models. We provide a logistic regression model (LL3: LogisticLag3) based on inputs measured 3 hr previous, along with optimal probability thresholds, for future predictions.

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Energetic Electron Flux Predictions in the Near‐Earth Plasma Sheet From Solar Wind Driving

Cited by 8 publications

References 104 publications

Forward‐Looking Study of Solar Maximum Impact in 2025: Effects of Satellite Navigation Failure on Aviation Network Operation in the Greater Bay Area, China

Forward‐Looking Study of Solar Maximum Impact in 2025: Effects of Satellite Navigation Failure on Aviation Network Operation in the Greater Bay Area, China

PEMEM Percentile: New Plasma Environment Specification Model for Surface Charging Risk Assessment

Predicting Geostationary 40–150 keV Electron Flux Using ARMAX (an Autoregressive Moving Average Transfer Function), RNN (a Recurrent Neural Network), and Logistic Regression: A Comparison of Models

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