Global 3-D ionospheric electron density reanalysis based on multisource data assimilation

<p>The response of the Ionosphere - Thermosphere (IT) system to severe storm conditions is of great importance to fully understand its coupling mechanisms. The challenge to represent the governing processes of the upper atmosphere depends, to a large extent, on an accurate representation of the true state of the IT system, that we obtain by assimilating relevant measurements into physics-based models. Thermospheric Mass Density (TMD) is the summation of total neutral mass within the atmosphere that is derived from accelerometer measurements of satellite missions such as CHAMP, GOCE, GRACE(-FO) and Swarm. TMD estimates can be assimilated into physics-based models to modify the state of the processes within the IT system. Previous studies have shown that this modification can potentially improve the simulations and predictions of the ionospheric electron density. These differences could also be interpreted as an indicator of the ionosphere-thermosphere interaction. The research presented here, aims to quantify the impact of data satellite based TMD assimilation on numerical model results.</p><p>Subject of this study is the Coupled Thermosphere-Ionosphere-Plasmasphere electrodynamics (CTIPe) physics-based model in combination with the recently developed Thermosphere-Ionosphere Data Assimilation (TIDA) scheme. TMD estimates from the ESA&#8217;s Swarm mission are assimilated in CTIPe-TIDA during the 16 to the 20 of March 2015. This period was characterized by a strong geomagnetic storm that triggered significant changes in the IT system, the so-called St. Patrick day storm 2015. To assess the changes in the IT system during storm conditions due to data assimilation, the model results from assimilating SWARM mass density normalized to the altitude of 400 km are compared to independent thermospheric estimates like GRACE-TMDS. In order to evaluate the impact of the data assimilation on the ionosphere, the corresponding output of electron density is compared to high-quality electron density estimates derived from data-driven model of the DGFI-TUM.</p>

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

Atmospheric Measurement Techniques Discussions ◽

10.5194/amtd-7-2631-2014 ◽

2014 ◽

Vol 7 (3) ◽

pp. 2631-2661 ◽

Cited By ~ 1

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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Regional 3-D ionospheric electron density specification on the basis of data assimilation of ground-based GNSS and radio occultation data

Space Weather ◽

10.1002/2016sw001363 ◽

2016 ◽

Vol 14 (6) ◽

pp. 433-448 ◽

Cited By ~ 18

Author(s):

Ercha Aa ◽

Siqing Liu ◽

Wengeng Huang ◽

Liqin Shi ◽

Jiancun Gong ◽

...

Keyword(s):

Data Assimilation ◽

Electron Density ◽

Radio Occultation ◽

Ionospheric Electron Density ◽

Occultation Data ◽

Radio Occultation Data

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

Atmospheric Measurement Techniques ◽

10.5194/amt-8-171-2015 ◽

2015 ◽

Vol 8 (1) ◽

pp. 171-182 ◽

Cited By ~ 25

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 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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