IRI-Plas modeli ve YKS ölçümleri kullanarak iyonkürede 3 boyutlu elektron yoğunluğu kestirimi

Three dimensional imaging of the electron density distribution in the ionosphere is a crucial task for investigating the ionospheric effects. Dual-frequency Global Positioning System (GPS) satellite signals can be used to estimate the Slant Total Electron Content (STEC) along the propagation path between a GPS satellite and ground based receiver station. However, the estimated GPS-STEC are very sparse and highly non-uniformly distributed for obtaining reliable 3D electron density distributions derived from the measurements alone. Standard tomographic reconstruction techniques are not accurate or reliable enough to represent the full complexity of variable ionosphere. On the other hand, model based electron density distributions are produced according to the general trends of the ionosphere, and these distributions do not agree with measurements, especially for geomagnetically active hours. In this thesis, a novel regional 3D electron density distribution reconstruction technique, namely IONOLAB-CIT, is proposed to assimilate GPS-STEC into physical ionospheric models. The IONOLAB-CIT is based on an iterative optimization framework that tracks the deviations from the ionospheric model in terms of F2 layer critical frequency and maximum ionization height resulting from the comparison of International Reference Ionosphere extended to Plasmasphere (IRI-Plas) model generated STEC and GPS-STEC. The IONOLAB-CIT is applied successfully for the reconstruction of electron density distributions over Turkey, during calm and disturbed hours of ionosphere using Turkish National Permanent GPS Network (TNPGN-Active). Reconstructions are also validated by predicting the STEC measurements that are left out in the reconstruction phase. The IONOLAB-CIT is compared with the real ionosonde measurements over Greece, and it is shown that the IONOLAB-CIT results are in good compliance with the ionosonde measurements. The results of the IONOLAB-CIT technique are also tracked and smoothed in time by using Kalman filtering methods for increasing the robustness of the results.