Bayesian statistical ionospheric tomography improved by incorporating ionosonde measurements
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...
| Main Authors: | , , , , , , |
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| Format: | Article |
| Language: | English |
| Published: |
Copernicus Publications
2016-04-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | http://www.atmos-meas-tech.net/9/1859/2016/amt-9-1859-2016.pdf |
| Summary: | 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.
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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.
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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. |
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| ISSN: | 1867-1381 1867-8548 |