### Abstract

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

Pages (from-to) | 338-352 |

Number of pages | 15 |

Journal | Computational Statistics & Data Analysis |

Volume | 55 |

Issue number | 1 |

Early online date | 8 May 2010 |

DOIs | |

Publication status | Published - 1 Jan 2011 |

### Fingerprint

### Keywords

- tomography
- inversion
- Bayesian modelling
- principal components
- Markov chain Monte Carlo
- ionospheric mapping

### Cite this

**Mapping electron density in the ionosphere: a principal component MCMC algorithm.** / Khorsheed, E; Hurn, Merrilee; Jennison, Christopher.

Research output: Contribution to journal › Article

*Computational Statistics & Data Analysis*, vol. 55, no. 1, pp. 338-352. https://doi.org/10.1016/j.csda.2010.04.029

}

TY - JOUR

T1 - Mapping electron density in the ionosphere: a principal component MCMC algorithm

AU - Khorsheed, E

AU - Hurn, Merrilee

AU - Jennison, Christopher

PY - 2011/1/1

Y1 - 2011/1/1

N2 - The outer layers of the Earth's atmosphere are known as the ionosphere, a plasma of free electrons and positively charged atomic ions. The electron density of the ionosphere varies considerably with time of day, season, geographical location and the sun's activity. Maps of electron density are required because local changes in this density can produce inaccuracies in the Navy Navigation Satellite System (NNSS) and Global Positioning System (GPS). Satellite to ground based receiver measurements produce tomographic information about the density in the form of path integrated snapshots of the total electron content which must be inverted to generate electron density maps. A Bayesian approach is proposed for solving the inversion problem using spatial priors in a parsimonious model for the variation of electron density with height. The Bayesian approach to modelling and inference provides estimates of electron density along with a measure of uncertainty for these estimates, leading to credible intervals for all quantities of interest. The standard parameterisation does not lend itself well to standard Metropolis-Hastings algorithms. A much more efficient form of Markov chain Monte Carlo sampler is developed using a transformation of variables based on a principal components analysis of initial output.

AB - The outer layers of the Earth's atmosphere are known as the ionosphere, a plasma of free electrons and positively charged atomic ions. The electron density of the ionosphere varies considerably with time of day, season, geographical location and the sun's activity. Maps of electron density are required because local changes in this density can produce inaccuracies in the Navy Navigation Satellite System (NNSS) and Global Positioning System (GPS). Satellite to ground based receiver measurements produce tomographic information about the density in the form of path integrated snapshots of the total electron content which must be inverted to generate electron density maps. A Bayesian approach is proposed for solving the inversion problem using spatial priors in a parsimonious model for the variation of electron density with height. The Bayesian approach to modelling and inference provides estimates of electron density along with a measure of uncertainty for these estimates, leading to credible intervals for all quantities of interest. The standard parameterisation does not lend itself well to standard Metropolis-Hastings algorithms. A much more efficient form of Markov chain Monte Carlo sampler is developed using a transformation of variables based on a principal components analysis of initial output.

KW - tomography

KW - inversion

KW - Bayesian modelling

KW - principal components

KW - Markov chain Monte Carlo

KW - ionospheric mapping

UR - http://www.scopus.com/inward/record.url?scp=77958039816&partnerID=8YFLogxK

UR - http://dx.doi.org/10.1016/j.csda.2010.04.029

U2 - 10.1016/j.csda.2010.04.029

DO - 10.1016/j.csda.2010.04.029

M3 - Article

VL - 55

SP - 338

EP - 352

JO - Computational Statistics & Data Analysis

JF - Computational Statistics & Data Analysis

SN - 0167-9473

IS - 1

ER -