Ionospheric Kalman Filter Assimilation Based on Covariance Localization Technique

Abstract

The data assimilation algorithm is a common algorithm in space weather research. Based on the GNSS data from the China Crustal Movement Observation Network (CMONOC) and the International Reference Ionospheric Model (IRI), a fast three-dimensional (3D) electron density nowcasting model for China and its adjacent regions was developed. Unlike the traditional Gaussian background covariance model, the error covariance of the IRI model, based on the IGS grid TEC data, is estimated in this work. Due to the large scale of the high-resolution covariance matrix, it cannot be stored and calculated directly on a personal computer. The covariance localization (CL) technique is introduced to sparse the covariance matrix while removing the pseudo-correlation of the covariance matrix. After localization, the covariance matrix can be converted into a sparse matrix for storage and calculation, which greatly reduces the computer memory requirement of the assimilation model and improves the calculation speed of the model. Based on this algorithm, a series of experiments were carried out in this work. The experimental results show that this algorithm can effectively assimilate the observed GNSS to the background field, make up for the temporal and spatial limitations of the observed data, and improve the accuracy of the ionospheric electron density nowcast. Compared with the digisonde observed foF2 (the critical frequency of the ionospheric F2 layer), the RMSE of the assimilation model is 0.44 MHz lower than that of the IRI model.

Assimilation and Inversion of Ionospheric Electron Density Data Using Lightning Whistlers

“…An arbitrary number of parabolic segments may be fitted to the profile to approximate its shape. A file with the suffix SAO can be generated to store all the information [4]. Figure 4 shows the location distribution of four digisonde stations of the Meridian Project.…”

“…For the error covariance matrix of the background value, the assumption is made that the background value has a Gaussian distribution in the vertical direction and can be separated [4,8], and its elements are expressed as follows:…”

“…Data assimilation makes up for the deficiencies of empirical modeling and theoretical modeling and effectively improves the accuracy of ionospheric models. The basic theory is to adopt a series of algorithms, such as optimal estimation theory, the Kalman filtering method, and the variational method, to assimilate and fuse observations on a background model based on the physical mechanism to minimize the overall error between the model and observations, thus obtaining more accurate physical parameters [4].…”

“…The accuracy of the assimilation results was verified by ionosonde. Based on the GNSS data from the China Crustal Movement Observation Network (CMONOC), Qiao et al [4] adopted the Kalman filter assimilation method to establish a fast electron density nowcasting model for China and its adjacent regions. These researchers used data assimilation technology as a method to study ionospheric physics and to adjust the output parameters of the modified model by absorbing the observation data.…”

“…Ionospheric vertical sounding has difficulty detecting the ionization state of the D-layer, and cannot carry out ionosphere detection above the F-layer peak value. The occultation observations have a low spatial resolution in higher ionosphere regions [4]. Lightning whistlers propagate along the geomagnetic field in the ionosphere and are a natural signal with a high incidence and intensity, which makes them easily detectable by satellites around the world.…”