Development of an adequate variance model for GNSS carrier phase observations based on turbulence theory

• Applicant: Professor Dr.-Ing. Steffen Schön
• Subject Area: Geodesy, Photogrammetry, Remote Sensing, Geoinformatics, Cartography
• Term: from 2008 to 2015
• Project identifier: Deutsche Forschungsgemeinschaft (DFG) - Project number 67806988

The variance covariance matrix (VCM) of Global Navigation Satellite System (GNSS) observations has a key function for the GNSS analysis. Prominent examples are the determination and validation of the carrier phase ambiguities, coordinates and tropospheric zenith delays as well as derived quantities. However, all currently used covariance models are very simplified and thus they allow only a restricted interpretation of the results. During the first part of this project (SCHO1314/1 1) substantial progress was made towards a concept of more realistic and fully-populated VCM for GNSS observations. The main advantage over common-used mathematical-empirical models is that our model is based on turbulence theory and thus it has a direct link to the underlying physics. Therefore our approach will contribute to a better understanding of the complex processes of atmospheric turbulence. The methodology will be exemplarily developed for refractivity fluctuations in the troposphere and their stochastic description. However it will be transferable to further variations, like e.g., ionospheric scintillations. In the second part of the project proposed here, the concept of turbulence-based VCM will be fully exploited. On the one hand, the benefits of our VCM in the context of parameter estimated will be investigated. On the other hand, the currently unknown relationship between non-stationary, temporally and spatially correlated fluctuations in the observations and the estimated parameter time series will be established. The turbulence-based VCM is appropriate for simulating non-stationary stochastic processes with predefined spatial and temporal correlations. This research will help to significantly improve the separability between apparent and actual variations in high-resolution parameter time series; an open issue in nowadays GNSS-based volcano monitoring, tsunami prediction, or atmosphere research.

• DFG Programme: Research Grants