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Atmospheric Measurement Techniques An interactive open-access journal of the European Geosciences Union

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Atmos. Meas. Tech., 8, 171-182, 2015
http://www.atmos-meas-tech.net/8/171/2015/
doi:10.5194/amt-8-171-2015
© Author(s) 2015. This work is distributed
under the Creative Commons Attribution 3.0 License.
Research article
12 Jan 2015
Ionospheric assimilation of radio occultation and ground-based GPS data using non-stationary background model error covariance
C. Y. Lin1, T. Matsuo2,3, J. Y. Liu1,4, C. H. Lin5, H. F. Tsai5, and E. A. Araujo-Pradere2 1Institute of Space Science, National Central University, Chungli, Taiwan
2Cooperative Institute for Research in Environmental Sciences, University of Colorado, Boulder, Colorado, USA
3Space Weather Prediction Center, National Oceanic and Atmospheric Administration, Boulder, Colorado, USA
4National Space Organization, HsinChu, Taiwan
5Department of Earth Sciences, National Cheng Kung University, Tainan, Taiwan
Abstract. 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.

Citation: Lin, C. Y., Matsuo, T., Liu, J. Y., Lin, C. H., Tsai, H. F., and Araujo-Pradere, E. A.: Ionospheric assimilation of radio occultation and ground-based GPS data using non-stationary background model error covariance, Atmos. Meas. Tech., 8, 171-182, doi:10.5194/amt-8-171-2015, 2015.
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Short summary
This study presents a new approach to assimilate FORMOSAT-3/COSMIC radio occultation (RO) slant total electron content (TEC) data as well as ground-based GPS slant TEC data into the International Reference Ionosphere to reconstruct 3-D ionospheric election density structure. 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.
This study presents a new approach to assimilate FORMOSAT-3/COSMIC radio occultation (RO) slant...
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