Abstract:
Two different implementations of Gaussian process (GP) models are proposed to
estimate the vertical total electron content (TEC) from dual frequency Global Positioning
System (GPS) measurements. The model falseness of GP and neural network models
are compared using daily GPS TEC data from Sutherland, South Africa, and it is shown
that the proposed GP models exhibit superior model falseness. The GP approach has
several advantages over previously developed neural network approaches, which
include seamless incorporation of prior knowledge, a theoretically principled method for
determining the much smaller number of free model parameters, the provision of estimates
of the model uncertainty, and a more intuitive interpretability of the model.
