### Abstract

We implement a hierarchical Bayesian spatiotemporal (HBST) model to forecast the daily trapped particle flux distribution over the South Atlantic Anomaly (SAA) region. The National Oceanic and Atmospheric Administration (NOAA)-15 data from 1–30 March 2008 with particle energies as >30 keV (mep0e1) and >300 keV (mep0e3) for electrons and 80–240 keV (mep0p2) and > 6900 keV (mep0p6) for protons were used as the model input to forecast the flux values on 31 March 2008. Data were transformed into logarithmic values and gridded in a 5°×5° longitude and latitude size to fulfill the modeling precondition. A Monte Carlo Markov chain (MCMC) was then performed to solve the HBST Gaussian Process (GP) model by using the Gibbs sampling method. The result for this model was interpolated by a Kriging technique to achieve the whole distribution figure over the SAA region. Statistical results of the root mean square error (RMSE), mean absolute percentage error (MAPE), and bias (BIAS) showed a good indicator of the HBST method. The statistical validation also indicated the high variability of particle flux values in the SAA core area. The visual validation showed a powerful combination of HBST-GP model with Kriging interpolation technique. The Kriging also produced a good quality of the distribution map of particle flux over the SAA region as indicated by its small variance value. This suggests that the model can be applied in the development of a Low Earth Orbit (LEO)-Equatorial satellite for monitoring trapped particle radiation hazard.

Original language | English |
---|---|

Pages (from-to) | 1287-1294 |

Number of pages | 8 |

Journal | Journal of Earth System Science |

Volume | 123 |

Issue number | 6 |

Publication status | Published - 1 Aug 2014 |

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### Keywords

- Forecasting
- Hierarchical Bayesian
- SAA
- Spatiotemporal
- Trapped particle

### ASJC Scopus subject areas

- Earth and Planetary Sciences(all)

### Cite this

*Journal of Earth System Science*,

*123*(6), 1287-1294.

**The application of a hierarchical bayesian spatiotemporal model for forecasting the saa trapped particle flux distribution.** / Suparta, Wayan; Gusrizal.

Research output: Contribution to journal › Article

*Journal of Earth System Science*, vol. 123, no. 6, pp. 1287-1294.

}

TY - JOUR

T1 - The application of a hierarchical bayesian spatiotemporal model for forecasting the saa trapped particle flux distribution

AU - Suparta, Wayan

AU - Gusrizal,

PY - 2014/8/1

Y1 - 2014/8/1

N2 - We implement a hierarchical Bayesian spatiotemporal (HBST) model to forecast the daily trapped particle flux distribution over the South Atlantic Anomaly (SAA) region. The National Oceanic and Atmospheric Administration (NOAA)-15 data from 1–30 March 2008 with particle energies as >30 keV (mep0e1) and >300 keV (mep0e3) for electrons and 80–240 keV (mep0p2) and > 6900 keV (mep0p6) for protons were used as the model input to forecast the flux values on 31 March 2008. Data were transformed into logarithmic values and gridded in a 5°×5° longitude and latitude size to fulfill the modeling precondition. A Monte Carlo Markov chain (MCMC) was then performed to solve the HBST Gaussian Process (GP) model by using the Gibbs sampling method. The result for this model was interpolated by a Kriging technique to achieve the whole distribution figure over the SAA region. Statistical results of the root mean square error (RMSE), mean absolute percentage error (MAPE), and bias (BIAS) showed a good indicator of the HBST method. The statistical validation also indicated the high variability of particle flux values in the SAA core area. The visual validation showed a powerful combination of HBST-GP model with Kriging interpolation technique. The Kriging also produced a good quality of the distribution map of particle flux over the SAA region as indicated by its small variance value. This suggests that the model can be applied in the development of a Low Earth Orbit (LEO)-Equatorial satellite for monitoring trapped particle radiation hazard.

AB - We implement a hierarchical Bayesian spatiotemporal (HBST) model to forecast the daily trapped particle flux distribution over the South Atlantic Anomaly (SAA) region. The National Oceanic and Atmospheric Administration (NOAA)-15 data from 1–30 March 2008 with particle energies as >30 keV (mep0e1) and >300 keV (mep0e3) for electrons and 80–240 keV (mep0p2) and > 6900 keV (mep0p6) for protons were used as the model input to forecast the flux values on 31 March 2008. Data were transformed into logarithmic values and gridded in a 5°×5° longitude and latitude size to fulfill the modeling precondition. A Monte Carlo Markov chain (MCMC) was then performed to solve the HBST Gaussian Process (GP) model by using the Gibbs sampling method. The result for this model was interpolated by a Kriging technique to achieve the whole distribution figure over the SAA region. Statistical results of the root mean square error (RMSE), mean absolute percentage error (MAPE), and bias (BIAS) showed a good indicator of the HBST method. The statistical validation also indicated the high variability of particle flux values in the SAA core area. The visual validation showed a powerful combination of HBST-GP model with Kriging interpolation technique. The Kriging also produced a good quality of the distribution map of particle flux over the SAA region as indicated by its small variance value. This suggests that the model can be applied in the development of a Low Earth Orbit (LEO)-Equatorial satellite for monitoring trapped particle radiation hazard.

KW - Forecasting

KW - Hierarchical Bayesian

KW - SAA

KW - Spatiotemporal

KW - Trapped particle

UR - http://www.scopus.com/inward/record.url?scp=84907510436&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=84907510436&partnerID=8YFLogxK

M3 - Article

AN - SCOPUS:84907510436

VL - 123

SP - 1287

EP - 1294

JO - Proceedings of the Indian Academy of Sciences, Earth and Planetary Sciences

JF - Proceedings of the Indian Academy of Sciences, Earth and Planetary Sciences

SN - 2347-4327

IS - 6

ER -