Ingestion of GPS data into a parameterized ionospheric model for tomography of the electron distribution of the ionosphere

We develop a PIM-based functional for stochastic tomography with a Kalman filter: which aids in the regularization of the inversion problem associated with 4-dimensional ionospheric stochastic tomography. We let the GPS data select dinamically the best PIM parameters, in a. 3DVAR fashion. We collect GPS data from GPS/MET and IGS for one of the World Space Days and we ingest them in a Parameterized Ionospheric Model (PIM). The process selects the ionospheric parameters (Solar activity index F-10.7 and the Magnetic activity index K-p) that best fit the PIM model. We then compare our deduced ionospheric parameters with the values provided by the US National Geophysical Data Center (NGDC). The resulting PIM fitted model is compared to direct 3D voxel tomography. We demonstrate the value of this method analyzing IGS and GPS/MET GPS data.