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
基于Gauss-Markov卡尔曼滤波的电离层数值同化现报预报系统的构建——以中国及周边地区为例的观测系统模拟试验

In 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 area as observation system to do the simulation experiment. International Reference Ionosphere (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.