The Global Navigation Satellite System (GNSS) technique has become an integral part of all applications, where mobility plays an important role. The basic observable in the GNSS positioning is the time required for electromagnetic signals to travel from the GNSS satellite (transmitter) to a GNSS receiver. This travelling time, multiplied by the speed of light, provides a measure of the apparent distance (pseudo-range) between them. By knowing the position of GNSS satellites from Precise Orbit Determination (POD), the unknown position of the GNSS receiver and its uncertainty can be computed when at least four range measurements exist.
From the mostly used GNSS constellations, GPS and GLONASS satellites orbit at altitudes of around 20,000 km, while BeiDou and Galileo satellites orbit a bit higher, i.e., around 21,500 km and 23,000 km, respectively. The signals from GNSS satellites must transit the ionosphere (i.e., the part of atmosphere between 60 and 2000 km containing ionized plasma of different gas components) on their way to receivers. These free electrons add delay on the code-derived pseudo-range and advance the career phase signals. These effects must be eliminated in some way to achieve high accuracy in GNSS positioning, navigation, and timing applications. As a result, the ionospheric modelling has received ever increasing attention in various fields including radio communication, navigation, satellite positioning and other space technologies.
Ionospheric models can be divided into three main categories:
1) physics-based models
2) empirical models
3) mathematical models.
Our research group works on developing Data Assimilation (DA), as well as Calibration and Data Assimilation (C/DA) frameworks to improve the models of the group (1) and (2), see e.g., Fernandez-Gomez et al. (2022), Forootan et al. (2022), and Kosary et al. (2022).
We also work on developing novel functional techniques to model ionosphere using GNSS and other observations, see, e.g., Farzaneh and Forootan (2018, 2020).
Improving the simulation of electron density by applying DA for global coupled models such as TIEGCM
Related Publications:
Fernandez-Gomez, I., Kodikara, T., Borries, C., Forootan, E., Schmidt, M., Codrescu, M. (2022), Improving estimates of the ionosphere during geomagnetic storm conditions through assimilation of thermospheric mass density. Earth Planets Space, 74, doi:10.1186/s40623-022-01678-3
Forootan, E., Kosary, M., Farzaneh, S., Kodikara, T., Vielberg, K., Schumacher, M. (2022), Forecasting global and multi-level thermospheric neutral density and ionospheric electron content by tuning models against satellite-based accelerometer measurements. Scientific Reports, 12, doi:10.1038/s41598-022-05952-y
Kosary, M., Forootan, E., Farzaneh, S., Schumacher, M. (2022), A sequential Calibration approach based on the Ensemble Kalman Filter (C-EnKF) for forecasting Total Electron Content (TEC). Journal of Geodesy, 96, doi:10.1007/s00190-022-01623-y
Farzaneh, S., Forootan, E. (2020), A least squares solution to regionalize VTEC estimates for positioning applications. MDPI Remote Sensing, 12 (21), doi.10.3390/rs12213545
Farzaneh, S., Forootan, E. (2018), Reconstructing regional ionospheric electron density: a combined spherical Slepian function and Empirical Orthogonal Function approach. Surveys in Geophysics, 39 (2), doi:10.1007/s10712-017-9446-y