Bayesian statistical ionospheric tomography improved by incorporating ionosonde measurements

Norberg, Johannes; Virtanen, Ilkka I.; Roininen, Lassi; Vierinen, Juha; Orispää, Mikko; Kauristie, Kirsti; Lehtinen, Markku S.

doi:https://doi.org/10.5194/amt-9-1859-2016

Articles | Volume 9, issue 4

https://doi.org/10.5194/amt-9-1859-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

https://doi.org/10.5194/amt-9-1859-2016

© Author(s) 2016. This work is distributed under
the Creative Commons Attribution 3.0 License.

Articles | Volume 9, issue 4

Research article

| Highlight paper

|

28 Apr 2016

Research article | Highlight paper |

| 28 Apr 2016

Bayesian statistical ionospheric tomography improved by incorporating ionosonde measurements

Johannes Norberg, Ilkka I. Virtanen, Lassi Roininen, Juha Vierinen, Mikko Orispää, Kirsti Kauristie, and Markku S. Lehtinen

Data sets

Dynasonde Database EISCAT http://dynserv.eiscat.uit.no/DD/Iono_form.php

International Reference Ionosphere - IRI-2007 Virtual Ionosphere, Thermosphere, Mesosphere Observatory (VITMO) http://omniweb.gsfc.nasa.gov/vitmo/iri_vitmo.html

Short summary

We validate 2-D ionospheric tomography reconstructions against EISCAT incoherent scatter radar measurements. The method is based on Bayesian statistical inversion. We employ ionosonde measurements for the choice of the prior distribution parameters and use a sparse matrix approximation for the computations. This results in a computationally efficient tomography algorithm with clear probabilistic interpretation. We find that ionosonde measurements improve the reconstruction significantly.