A High Accuracy Spatial Reconstruction Method Based on Surface Theory for Regional Ionospheric TEC Prediction

In order to achieve more accurate spatial reconstruction of ionospheric total electron content (TEC) and promote improved satellite positioning and ranging applications, a high accuracy spatial reconstruction (HASR) method for TEC is proposed based on the surface theory. The core theory of this method is as follows: (a) Any surface can be uniquely determined by its first and second fundamental quantities; (b) By direct difference approximation, differential equations are transformed into algebraic equations to solve Gauss equations faster. At the same time, taking parts of Europe as an example, the proposed HASR method is used to determine the correlation coefficients and the number of iterations of the model by using the relative root mean square error (RRMSE) as the evaluation criterion. The statistical results show that the TEC predicted by the HASR method is highly consistent with the actual observed values of ionospheric observation stations, and the prediction RRMSE is 9.75%. Compared with the Kriging interpolation with scale factor, the prediction accuracy of the HASR method is improved by 8.5%. We hope this method can provide ideas for the spatial reconstruction of other ionospheric parameters and further promote the realization of complete and accurate space weather forecast. Due to the limited number of observation stations, the total electron content (TEC) in a region always needs to be reconstructed. In order to improve the spatial prediction accuracy of TEC, a new high-accuracy spatial reconstruction (HASR) method was proposed based on surface theory. The HASR transforms the difficult differential equation into an algebraic equation which is easy to deal with by direct difference approximation, greatly reduces the complexity of the algorithm and the amount of computation, and has good convergence properties. The statistical results show that the TEC predicted by the HASR method is highly consistent with the actual observed values of ionospheric observation stations. Compared with the Kriging interpolation with scale factor, the prediction accuracy of the HASR method is improved. This method is expected to provide a reference for the modeling of other ionospheric parameters and further promote the practical application of the ionosphere. Based on the surface theory, a high-accuracy spatial reconstruction (HASR) method was proposed to improve the prediction accuracy of total electron contentDifferential equation is transformed into the algebraic equation by direct difference approximation, and the algorithm complexity is reducedThe prediction accuracy of the HASR method is 9.75%, which improved the baseline of Kriging interpolation by 8.5%