Development of a novel adaptive model to represent global ionosphere information from combining space geodetic measurement systems (ADAPIO)

This project focuses on the development of a novel near real-time data adaptive filtering framework for global modeling of the vertical total electron content (VTEC).

Ionospheric data can be acquired from various space geodetic observation techniques such as GNSS, altimetry, DORIS and radio occultation. The project aims to model the temporal and spatial variation of the ionosphere by a combination of these techniques in an adaptive data assimilation framework, which utilizes appropriate basis functions to represent the VTEC. The measurements naturally have inhomogeneous data distribution both in time and space. Therefore, integrating the techniques used in data adaptive basis selection methods such as Multivariate Adaptive Regression B-Splines (BMARS) with recursive filtering to model the daily global ionosphere may deliver important improvements over classical estimation methods. However, it is crucial to adapt these methods for global ionosphere modeling which works within a recursive filter like Kalman filter in such a way that they work properly by means of consistency, computational efficiency and quality. In addition to the Kalman filter, which is commonly used in recursive filtering, advantages proposed by other filtering methods, such as unscented or Ensemble Kalman Filter, in ionosphere modelling are also investigated. Since the ionospheric inverse problem may be ill-posed and also ill-conditioned, a suitable regularization procedure is developed to stabilize the solution. Finally, the resulting VTEC maps are validated by using independent observations or comparing them with other ionospheric products, for example the products from the IGS analysis centers.