scholarly journals Ionospheric data assimilation and forecasting during storms

2016 ◽  
Vol 121 (1) ◽  
pp. 764-778 ◽  
Author(s):  
Alex T. Chartier ◽  
Tomoko Matsuo ◽  
Jeffrey L. Anderson ◽  
Nancy Collins ◽  
Timothy J. Hoar ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Ionospheric Data Assimilation

Ionospheric data assimilation: recovery of strong mid-latitudinal density gradients

2003 ◽  
Vol 65 (10) ◽  
pp. 1087-1097 ◽  
Author(s):  
Jan J. Sojka ◽  
Donald C. Thompson ◽  
Robert W. Schunk ◽  
J.Vincent Eccles ◽  
Jonathan J. Makela ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Density Gradients ◽  
Ionospheric Data Assimilation

scholarly journals Ionospheric assimilation of radio occultation and ground-based GPS data using non-stationary background model error covariance

2014 ◽  
Vol 7 (3) ◽  
pp. 2631-2661 ◽  
Author(s):  
C. Y. Lin ◽  
T. Matsuo ◽  
J. Y. Liu ◽  
C. H. Lin ◽  
H. F. Tsai ◽  
...  
Keyword(s):  
Data Assimilation ◽  
Electron Density ◽  
Radio Occultation ◽  
Model Error ◽  
Background Model ◽  
Ionospheric Electron Density ◽  
Error Covariance ◽  
Ionospheric Data Assimilation ◽  
Stationary Location ◽  
Assimilation Model

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 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 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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