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| Titel |
Ionospheric assimilation of radio occultation and ground-based GPS data using non-stationary background model error covariance |
| VerfasserIn |
C. Y. Lin, T. Matsuo, J. Y. Liu, C. H. Lin, H. F. Tsai, E. A. Araujo-Pradere |
| Medientyp |
Artikel
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| Sprache |
Englisch
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| ISSN |
1867-1381
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| Digitales Dokument |
URL |
| Erschienen |
In: Atmospheric Measurement Techniques ; 8, no. 1 ; Nr. 8, no. 1 (2015-01-12), S.171-182 |
| Datensatznummer |
250116045
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| Publikation (Nr.) |
 copernicus.org/amt-8-171-2015.pdf |
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| Zusammenfassung |
| Ionospheric data assimilation is a powerful approach to reconstruct the 3-D
distribution of the ionospheric electron density from various types of
observations. We present a data assimilation model for the ionosphere, based
on the Gauss–Markov Kalman filter with the International Reference Ionosphere
(IRI) as the background model, to assimilate two different types of slant
total electron content (TEC) observations from ground-based GPS and
space-based FORMOSAT-3/COSMIC (F3/C) radio occultation. Covariance models for
the background model error and observational error play important roles in
data assimilation. The objective of this study is to investigate impacts of
stationary (location-independent) and non-stationary (location-dependent)
classes of the background model error covariance on the quality of
assimilation analyses. Location-dependent correlations are modeled using
empirical orthogonal functions computed from an ensemble of the IRI outputs,
while location-independent correlations are modeled using a Gaussian
function. Observing system simulation experiments suggest that assimilation
of slant TEC data facilitated by the location-dependent background model
error covariance yields considerably higher quality assimilation analyses.
Results from assimilation of real ground-based GPS and F3/C radio occultation
observations over the continental United States are presented as TEC and
electron density profiles. Validation with the Millstone Hill incoherent
scatter radar data and comparison with the Abel inversion results are also
presented. Our new ionospheric data assimilation model that employs the
location-dependent background model error covariance outperforms the earlier
assimilation model with the location-independent background model error
covariance, and can reconstruct the 3-D ionospheric electron density
distribution satisfactorily from both ground- and space-based GPS
observations. |
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