A hierarchical Bayesian spatio-temporal model to forecast trapped particle fluxes over the SAA region

Wayan Suparta, Gusrizal, Karel Kudela, Zaidi Isa

Research output: Contribution to journalArticle

Abstract

The particles trapped in the Earth's inner radiation belts could harm low Earth orbit (LEO) satellites. Although the inner radiation belts are usually stable, their response to extremely large solar geomagnetic events can produce satellite anomalies. The risk is higher because of frequent LEO satellite passes through the South Atlantic Anomaly (SAA). A model for forecasting the trapped particle flux distribution in equatorial LEO based on the hierarchical Bayesian spatio-temporal (HBST) statistical model was developed to address the risk to satellites. This model is applicable to low- and medium-energy electrons and protons under all solar activity conditions. Dynamic rather than static data were also used. A simple HBST model named the Gaussian process (GP) was developed using NOAA 15 - 17 data, which categorized particle energies as > 30 keV (mep0e1) and > 300 keV (mep0e3) for electrons and 80 - 240 keV (mep0p2) and 800 - 2500 keV (mep0p4) for protons in the SAA region. The goal of this study is to examine the applicability of this model during a quiet period (15 - 19 May 2009) and a period of high solar activity (26 - 30 October 2003). The forecast was then interpolated using a Kriging technique to estimate the particle distribution. Statistical and visual validations showed good indicators, with average mean relative error (MRE) values of 20 - 30% for both periods and a similar pattern as that of the National Oceanic and Atmospheric Administration (NOAA) map. This work contributes a method for predicting the trapped particle flux distribution at low latitude LEOs.

Original languageEnglish
JournalTerrestrial, Atmospheric and Oceanic Sciences
Volume28
Issue number3
DOIs
Publication statusPublished - 1 Jun 2017

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anomaly
solar activity
electron
kriging
energy
particle
forecast
distribution
radiation

Keywords

  • Forecasting
  • Hierarchical Bayesian
  • Spatio-temporal
  • Trapped particle

ASJC Scopus subject areas

  • Oceanography
  • Atmospheric Science
  • Earth and Planetary Sciences (miscellaneous)

Cite this

A hierarchical Bayesian spatio-temporal model to forecast trapped particle fluxes over the SAA region. / Suparta, Wayan; Gusrizal; Kudela, Karel; Isa, Zaidi.

In: Terrestrial, Atmospheric and Oceanic Sciences, Vol. 28, No. 3, 01.06.2017.

Research output: Contribution to journalArticle

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