Ionospheric TEC data assimilation based on Gauss–Markov Kalman filter

Abstract

Ionospheric Total Electron Content (TEC) is an important parameter for space weather research. Based on GNSS data from China Crustal Movement Observation Network (CMONOC) and International Reference Ionosphere (IRI) model, we develop a TEC model in China and adjacent region (15°N − 55°N and 70°E − 140°E) with spatial and temporal resolution of 1°×1° and 30 min. This model is developed by using Kalman filter algorithm. In the covariance matrix, the monthly average is used as the background reference field of the error matrix, and the spatial variation of TEC deviating from the reference background field in each day of the month is statistically analyzed. The assimilative results show that the data assimilation method can effectively combine the observational data with the background model to make up for the temporal and spatial limitations of the observations. In addition, the data assimilation algorithm can produce a short-term TEC forecast. The predicted results are consistent to the observations in the early stages of the forecast. With the increase of predicting time, the impact of the observational data on the predicted results weakens. The results become more biased toward the background data, and the biases can be corrected by observation data.