A new global TEC model for estimating transionospheric radio wave propagation errors

Abstract

Space-based navigation and radar systems operating at single frequencies of <10 GHz require ionospheric corrections of the signal delay or range error. Because this ionospheric propagation error is proportional to the total electron content of the ionosphere along the ray path, a user friendly TEC model covering global scale and all levels of solar activity should be helpful in various applications. Since such a model is not available yet, we present an empirical model approach that allows determining global TEC very easily. Although the number of model coefficients and parameters is rather small, the model describes main ionospheric features with good quality. Presented is the empirical approach describing dependencies on local time, geographic/geomagnetic location and solar irradiance and activity. The non-linear approach needs only 12 coefficients and a few empirically fixed parameters for describing the broad spectrum of TEC variation at all levels of solar activity. The model approach is applied on high-quality global TEC data derived by the Center for Orbit Determination in Europe (CODE) at the University of Berne over more than half a solar cycle (1998–2007). The model fits to these input data with a negative bias of 0.3 TECU and a RMS deviation of 7.5 TECU. As other empirical models too, the proposed Global Neustrelitz TEC Model NTCM-GLis climatological, i.e. the model describes the average behaviour under quiet geomagnetic conditions. During severe space weather events the actual TEC data may deviate from the model values considerably by more than 100%. A preliminary comparison with independent data sets as TOPEX/Poseidon altimeter data reveals similar results for NeQuick and NTCM-GL with RMS deviations in the order of 5 and 11 TECU (1 TECU = 1016 electrons/m2) for low and high-solar activity conditions, respectively. The more extended data base of ionosphere information that accumulates in the coming years will help in further improving the set of coefficients of the model.