Space Weather (Ionosphere) Data assimilation database

This website provides the ionospheric analysis data based on the ensemble data assimilation method, using the ionospheric observations, such as the total electron content (TEC) from the global ground-based GPS receiver networks and the ionospheric radio occultation (RO) data from the FORMOSAT-3/COSMIC and FORMOSAT-7/COSMIC2 satellites, to adjust the initial state of the three-dimensional electron density structure in the ionosphere model (TIEGCM) [Chen et al., 2016a]. This adjustment will let the model initial states closer to the real observations and further get the better accuracy of forecast results [Chen et al., 2016b]. Not only the electron density but also the other physical parameters, such as the neutral density, neutral wind, neutral temperature, etc., will also be adjusted in this data assimilation system. Using these assimilation results, we can further investigate the variations of background physical parameters and their effects during ionospheric events [Chen et al., 2017; 2019].

The algorithm of data assimilation is to adjust the model states by employing the observation data. As shown in the 1st and 2nd time steps in the following figure, since a physical model can’t fully understand all the parameters and their variations for the real world, there are differences between the model and the observations. When we apply the data assimilation at the 3rd time step, the model is adjusted to close to the observation. This adjustment will also affect the accuracy of forecast results, as shown by the comparison of blue-dotted squares and blue squares.

This website provides the past and the recent ionospheric data assimilation results during geomagnetic storms, solar quiet periods, and the special events, such as solar eclipses.

Reference:

Chen, C. H., C. H. Lin, T. Matsuo, W. H., Chen, I. T., Lee, J. Y., Liu, J. T. Lin, and C. T., Hsu, Ionospheric data assimilation with thermosphere-ionosphere-electrodynamics general circulation model and GPS-TEC during geomagnetic storm conditions, Journal of Geophysical Research, 121, 5708-5722, doi:10.1002/2015JA021787, 2016a.

Chen, C. H., C. H. Lin, J. Y. Liu, T. Matsuo, and W. H. Chen, Ionospheric data assimilation modeling of 2015 St. Patrick’s Day geomagnetic storm, Journal of Geophysical Research, 121, 11,549-11,559, doi:10.1002/2016JA023346, 2016b.

Chen, C. H., C. H. Lin, W.-H. Chen, and T. Matsuo, Modeling the ionosphericprereversal enhancement by using coupled thermosphere-ionosphere data assimilation, Geophys. Res. Lett., 44, 1652–1659, doi:10.1002/2016GL071812, 2017.

Chen, C. H., C. H. Lin, and T. Matsuo, Ionospheric responses to the 21 August 2017 solar eclipse by using data assimilation approach, Progress in Earth and Planetary Science, 6:13, doi:10.1186/s40645-019-0263-4, 2019.