TEC

About: TEC is a research topic. Over the lifetime, 5119 publications have been published within this topic receiving 84696 citations.

New aspects of the ionospheric response to the October 2003 superstorms from multiple-satellite observations

Abstract: The total electron content (TEC) data measured by the Jason, CHAMP, GRACE, and SAC-C satellites, the in situ electron densities from CHAMP and GRACE, and the vertical E x B drifts from the ROCSAT, have been utilized to examine the ionospheric response to the October 2003 superstorms. The combination of observations from multiple satellites provides a unique global view of ionospheric storm effects, especially over the Pacific Ocean and American regions, which were under sunlit conditions during the main phases of the October 2003 superstorms. The main results of this study are as follows: (1) There were substantial increases in TEC in the daytime at low and middle latitudes during both superstorms. (2) The enhancements were greater during the 30 October superstorm and occurred over a wider range of local times. (3) They also tended to peak at earlier local times during this second event. (4) These TEC enhancement events occurred at the local times when there were enhancements in the upward vertical drift. (5) The strong upward vertical drifts are attributed to penetration electric fields, suggesting that these penetration electric fields played a significant role in the electron density enhancements during these superstorms. Overall, the main contribution of this study is the simultaneous view of the storm time ionospheric response from multiple satellites, and the association of local time differences in ionospheric plasma response with measured vertical drift variations.

Solar proxies pertaining to empirical ionospheric total electron content models

Abstract: [1] Solar proxies and indices exhibiting extreme ultraviolet (EUV) irradiance that affects the ionospheric total electron content (TEC) were examined through training an artificial neural network (ANN). A TEC database was constructed from a dense GPS receiver network over Japan from April 1997 to March 2008, covering an entire 11 year solar activity period. In empirical models of upper atmospheric parameters, such as the International Reference Ionosphere model and the Mass Spectrometer and Incoherent Scatter thermospheric model, the 10.7 cm solar radio flux (F10.7) or the sunspot number (R) is used as a proxy for determining the solar activity. In the present study, ANN training for predicting TEC as a target parameter was done by including new solar proxies/indices in the input space that were based on direct measurements of solar EUV/UV flux, SOHO_SEM26–34 (the integrated 26–34 nm EUV emission), and Mg II cwr (the core-to-wing ratio of Mg II 280 nm line), as well as the traditional indices F10.7 and R. Root mean square errors (RMSEs) of TEC were compared after the training was completed using a variety of combinations of solar proxies. When a single proxy was used, SOHO_SEM26–34 yielded the smallest RMSE, or it was the best proxy for modeling ionospheric TEC. Further, general improvements were obtained by combining different types of proxies and short- and long-term means of them. The best combination was the 3 day smoothed daily, 7 day and 27 day backward mean values of Mg II cwr, SOHO_SEM26–34, and the 10.7 cm radio flux.

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

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.