The analysis of assumptions’ error sources on assimilating ground-based/spaceborne ionospheric observations

Abstract

In this paper, GNSS ionospheric observations from ground-based and spaceborne systems were simulated, and the global 3D ionospheric density field was reconstructed by Kalman-Filter algorithm. Various errors and influences in the assimilation of the ionospheric data were analyzed, and corresponding improvement methods were proposed, which were verified from the following two aspects based on simulation analysis:1. From the statistics of slant total electron content(TEC): (a) the influence of the shell with altitude between 800 km and 20000 km was 20̃30%, and could be reduced by the two-step assimilation method; (b) the influence of ionospheric time variations could reach ̃10%, and was reduced to ̃5% by the correction proposed in this paper; (c) the influence of the ionospheric grid representation was ̃2.5%, and was reduced to 0.9% by the method of the bilinear interpolation at intercept midpoint. The three errors mentioned above are called assumptions’ error, for they always be ignored or deduced by theoretical assumptions.2. The results of electron density reconstruction showed algorithm with assumptions errors corrections was better than origin one, which confirmed the effectiveness of the correction algorithms to the assumptions errors. And it was also showed that the Kalman filter assimilation algorithm was better than the Abel-Retrieved method, especially in ionospheric F2 region, for there is no ionospheric symmetric assumption.