Development of an Ionospheric Numerical Assimilation Nowcast and Forecast System Based on Gauss‐Markov Kalman Filter–an Observation System Simulation Experiment taking Example for China and Its Surrounding Area

Abstract

AbstractIn this paper, we constructed an ionospheric data assimilation system based on Gauss‐Markov Kalman filter and gave some test results. We chose some ionosphere stations (including meridional project stations, China lithosphere deformation GPS network, part of IGS stations) in China and its surrounding areas as the observational system to do the simulation experiment. The International Reference Model (IRI) is chosen to be the background model, while NeQuick model output is taken to be the observations. Our assimilation results show that it can get good estimation of ionosphere electron density by ingesting the observed slant TEC data into the model by Kalman filter. It illustrates that our assimilation algorithm is feasible and the selected parameters are reasonable. We also obtained reasonable short‐time forecast results by Gauss‐Markov assumption.