Ionospheric assimilation of radio occultation and ground-based GPS data using non-stationary background model error covariance
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 Ionospher...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English |
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Copernicus Publications
2015-01-01
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| Series: | Atmospheric Measurement Techniques |
| Online Access: | http://www.atmos-meas-tech.net/8/171/2015/amt-8-171-2015.pdf |
| _version_ | 1811331116196429824 |
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| author | C. Y. Lin T. Matsuo J. Y. Liu C. H. Lin H. F. Tsai E. A. Araujo-Pradere |
| author_facet | C. Y. Lin T. Matsuo J. Y. Liu C. H. Lin H. F. Tsai E. A. Araujo-Pradere |
| author_sort | C. Y. Lin |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-13T16:14:17Z |
| format | Article |
| id | doaj.art-c59dfc3afcd04f6abe0d609d9551e3ca |
| institution | Directory Open Access Journal |
| issn | 1867-1381 1867-8548 |
| language | English |
| last_indexed | 2024-04-13T16:14:17Z |
| publishDate | 2015-01-01 |
| publisher | Copernicus Publications |
| record_format | Article |
| series | Atmospheric Measurement Techniques |
| spelling | doaj.art-c59dfc3afcd04f6abe0d609d9551e3ca2022-12-22T02:40:06ZengCopernicus PublicationsAtmospheric Measurement Techniques1867-13811867-85482015-01-018117118210.5194/amt-8-171-2015Ionospheric assimilation of radio occultation and ground-based GPS data using non-stationary background model error covarianceC. Y. Lin0T. Matsuo1J. Y. Liu2C. H. Lin3H. F. Tsai4E. A. Araujo-Pradere5Institute of Space Science, National Central University, Chungli, TaiwanCooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado, USAInstitute of Space Science, National Central University, Chungli, TaiwanDepartment of Earth Sciences, National Cheng Kung University, Tainan, TaiwanDepartment of Earth Sciences, National Cheng Kung University, Tainan, TaiwanCooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado, USAIonospheric 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.http://www.atmos-meas-tech.net/8/171/2015/amt-8-171-2015.pdf |
| spellingShingle | C. Y. Lin T. Matsuo J. Y. Liu C. H. Lin H. F. Tsai E. A. Araujo-Pradere Ionospheric assimilation of radio occultation and ground-based GPS data using non-stationary background model error covariance Atmospheric Measurement Techniques |
| title | Ionospheric assimilation of radio occultation and ground-based GPS data using non-stationary background model error covariance |
| title_full | Ionospheric assimilation of radio occultation and ground-based GPS data using non-stationary background model error covariance |
| title_fullStr | Ionospheric assimilation of radio occultation and ground-based GPS data using non-stationary background model error covariance |
| title_full_unstemmed | Ionospheric assimilation of radio occultation and ground-based GPS data using non-stationary background model error covariance |
| title_short | Ionospheric assimilation of radio occultation and ground-based GPS data using non-stationary background model error covariance |
| title_sort | ionospheric assimilation of radio occultation and ground based gps data using non stationary background model error covariance |
| url | http://www.atmos-meas-tech.net/8/171/2015/amt-8-171-2015.pdf |
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