Bayesian statistical ionospheric tomography improved by incorporating ionosonde measurements
Name
Norberg-2016-Bayesian statistical.pdf
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4.05 MB
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Author(s)
Norberg, Johannes
Virtanen, Ilkka I.
Roininen, Lassi
Vierinen, Juha
Orispää, Mikko
Kauristie, Kirsti
Lehtinen, Markku S.
Date Issued
April 2016
Journal
Atmospheric Measurement Techniques
Publisher
Copernicus GmbH
Citation
Norberg, Johannes et al. “Bayesian Statistical Ionospheric Tomography Improved by Incorporating Ionosonde Measurements.” Atmospheric Measurement Techniques 9.4 (2016): 1859–1869.
Version
Final published version
Abstract
We validate two-dimensional ionospheric tomography reconstructions against EISCAT incoherent scatter radar measurements. Our tomography method is based on Bayesian statistical inversion with prior distribution given by its mean and covariance. We employ ionosonde measurements for the choice of the prior mean and covariance parameters and use the Gaussian Markov random fields as a sparse matrix approximation for the numerical computations. This results in a computationally efficient tomographic inversion algorithm with clear probabilistic interpretation.
We demonstrate how this method works with simultaneous beacon satellite and ionosonde measurements obtained in northern Scandinavia. The performance is compared with results obtained with a zero-mean prior and with the prior mean taken from the International Reference Ionosphere 2007 model. In validating the results, we use EISCAT ultra-high-frequency incoherent scatter radar measurements as the ground truth for the ionization profile shape.
We find that in comparison to the alternative prior information sources, ionosonde measurements improve the reconstruction by adding accurate information about the absolute value and the altitude distribution of electron density. With an ionosonde at continuous disposal, the presented method enhances stand-alone near-real-time ionospheric tomography for the given conditions significantly.
MIT Department
Haystack Observatory
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Creative Commons Attribution
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DOI of Published Version
https://doi.org/10.5194/amt-9-1859-2016
