A PIM-aided Kalman Filter for GPS Tomography of the Ionospheric Electron Content

We develop the formalism for a PIM-based functional for stochastic tomography with a Kalman filter, in which the inversion problem associated with four-dimensional ionospheric stochastic tomography is regularized. For consistency, GPS data is used to select dynamically the best PIM parameters, in a 3DVAR fashion. We demonstrate the ingestion of GPS (IGS and GPS/MET) data into a parameterized ionospheric model, used to select the set of parameters that minimize a suitable cost functional. 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, and present our results in terms of a 4D model of the ionospheric electronic density.