Ionospheric assimilation of radio occultation and ground-based GPS data using non-stationary background model error covariance Article Swipe
C. Y. Lin
,
Tomoko Matsuo
,
Jann‐Yenq Liu
,
Chien‐Hung Lin
,
H. F. Tsai
,
E. A. Araujo‐Pradere
·
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.5194/amt-8-171-2015
YOU?
·
· 2015
· Open Access
·
· DOI: https://doi.org/10.5194/amt-8-171-2015
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.
Related Topics
Concepts
Data assimilation
Radio occultation
Covariance
Millstone Hill
TEC
Ionosphere
Remote sensing
Incoherent scatter
Total electron content
Environmental science
COSMIC cancer database
Meteorology
Mathematics
Statistics
Geology
Physics
Geophysics
Astrophysics
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.5194/amt-8-171-2015
- https://amt.copernicus.org/articles/8/171/2015/amt-8-171-2015.pdf
- OA Status
- gold
- Cited By
- 65
- References
- 36
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W2044922933
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W2044922933Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.5194/amt-8-171-2015Digital Object Identifier
- Title
-
Ionospheric assimilation of radio occultation and ground-based GPS data using non-stationary background model error covarianceWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2015Year of publication
- Publication date
-
2015-01-12Full publication date if available
- Authors
-
C. Y. Lin, Tomoko Matsuo, Jann‐Yenq Liu, Chien‐Hung Lin, H. F. Tsai, E. A. Araujo‐PradereList of authors in order
- Landing page
-
https://doi.org/10.5194/amt-8-171-2015Publisher landing page
- PDF URL
-
https://amt.copernicus.org/articles/8/171/2015/amt-8-171-2015.pdfDirect link to full text PDF
- Open access
-
YesWhether a free full text is available
- OA status
-
goldOpen access status per OpenAlex
- OA URL
-
https://amt.copernicus.org/articles/8/171/2015/amt-8-171-2015.pdfDirect OA link when available
- Concepts
-
Data assimilation, Radio occultation, Covariance, Millstone Hill, TEC, Ionosphere, Remote sensing, Incoherent scatter, Total electron content, Environmental science, COSMIC cancer database, Meteorology, Mathematics, Statistics, Geology, Physics, Geophysics, AstrophysicsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
65Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 2, 2024: 6, 2023: 10, 2022: 13, 2021: 5Per-year citation counts (last 5 years)
- References (count)
-
36Number of works referenced by this work
- Related works (count)
-
10Other works algorithmically related by OpenAlex
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