Comparative testing of four ionospheric models driven with GPS measurements

In the context of the European Space Agency/European Space Operations Centre funded Study �GNSS Contribution to Next Generation Global Ionospheric Monitoring,� four ionospheric models based on GNSS data (the Electron Density Assimilative Model, EDAM; the Ionosphere Monitoring Facility, IONMON v2; the Tomographic Ionosphere model, TOMION; and the Neustrelitz TEC Models, NTCM) have been run using a controlled set of input data. Each model output has been tested against differential slant TEC (dSTEC) truth data for high (May 2002) and low (December 2006) sunspot periods. Three of the models (EDAM, TOMION, and NTCM) produce dSTEC standard deviation results that are broadly consistent with each other and with standard deviation spreads of ~1 TECu for December 2006 and ~1.5 TECu for May 2002. The lowest reported standard deviation across all models and all stations was 0.99 TECu (EDAM, TLSE station for December 2006 night). However, the model with the best overall dSTEC performance was TOMION which has the lowest standard deviation in 28 out of 52 test cases (13 stations, two test periods, day and night). This is probably related to the interpolation techniques used in TOMION exploiting the spatial stationarity of vertical TEC error decorrelation

Atmospheric signal propagation

GNSS satellites emit signals which propagate as electromagnetic waves through space to the receivers which are located on or near the Earth’s surface or on other satellites. Thereby, electromagnetic waves travel through the ionosphere and the neutral atmosphere (troposphere) which causes signals to be delayed, damped and refracted as the refractivity index of the propagation media is not equal to one. In this chapter, the nature and effects of GNSS signal propagation in both the troposphere and the ionosphere, is examined. After a brief review of the fundamentals of electromagnetic waves their propagation in refractive media, the effects of the neutral atmosphere are discussed. In addition empirical correction models as well as state-of- the-art atmosphere delay estimation approaches are presented. Effects related to signal propagtion through the ionosphere are dealt in a dedicated section by describing the error contribution of first up to third order terms in the refractive index and ray path bending. After discussing diffraction and scattering phenomena due to ionospheric irregularities, mitigation techniques for different types of applications are presented

Ionosphere Monitoring

Global navigation satellite system (GSSS)-based monitoring of the ionosphere is important in a twofold manner. Firstly, GNSS measurements provide valuable ionospheric information for correcting and mitigating ionospheric range errors or to warn users in particular in precise and safety of life (SoL) applications. Secondly, spatial and temporal resolution of ground- and space-based measurements is high enough to explore the dynamics of ionospheric processes such as the origin and propagation of ionospheric storms. It is discussed how ground- and space-based GNSS measurements are used to create globalmaps of total electron content (TEC) and to reconstruct the highly variable three-dimensional (3-D) electron density distribution on global scale under perturbed conditions. Thus, the monitoring results can be used for correcting ionospheric errors in single-frequency applications as well as for studying the driving forces of space weather-induced perturbation features at a broad range of temporal and spatial scales. Whereas large- and mediumscale perturbations affect accuracy and reliability of GNSS measurements, small-scale plasma irregularities and plasma bubbles have a direct impact on the continuity of GNSS availability by causing strong and rapid fluctuations of the signal strength, known as radio scintillations. It is discussed how better understanding of space weather-related phenomena may help to model and forecast ionospheric behavior even under perturbed conditions. Hence, ionospheric monitoring contributes to the successful mitigation of range errors or performance degradation associated with the ionospheric impact on a broad spectrum of GNSS applications