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Background
Measures of physical observables (which change over time) to study natural phenomena is of extraordinary importance for comprehending and characterisation of the underlying physical process. In particular, measurements from space geodetic techniques provide observational ata that are used to depict the global picture of the Earth. The geodetic observations have a wide range of scientific applications such as fluctuations of the Earth (including precession, polar motion, nutation etc.,) and all types of height changes and deformation due to mass transfer between the solid Earth, the atmosphere and hydrosphere. However, these measurements are influenced by many geophysical processes such as weather and climate encompassing the atmospheric structure and dynamics, mass fluctuations, large water mass circulations and sea level fluctuations, postglacial rebound and, tide of solid Earth and Oceans; are often embedded with measurement error and are often corrupted by unknownnoise sources. All the physical processes and noise introduce biases to the geophysical signal tructure that are embedded in the parameters derived from data. It is crucial that theserocesses are therefore understood in data analysis. Key auxiliary parameters associated with all the primary geodetic target parameters are the tropospheric WV or the tropospheric delay of radio signals induced by WV and meteorological parameters.
Geodetic time series often exhibits irregular, nonstationary and wide-band signals due to the complex interaction between different components of the Earth system. In the majority of the applications of studying time series of geodetic data, the underlying structure is often assumed to be stationary. It is unlikely therefore that analysis techniques developed to reliably display the spatial-temporal variability of geodetic data are robust. Applying non-linear time series techniques to geodetic observations provide new information about the complex dynamics of the atmosphere-Earth system. In order to extract and characterise the relevant geophysical signals embedded in the data, a variety of measures (especially those that are data adaptive) are welcome.
1. Introduction
1.1. Background
1.2. Significance of the stud
1.3. Aim and objectives
1.4. Research questions
1.5. Hypothesis
1.6. Scope of the study
2. Tropospheric modelling for space geodetic applications
2.1. Introduction
2.2. Structure of the atmosphere
2.3. Refractivity, tropospheric delay and precipitable WV
2.4. Overview of geodetic VLBI, GNSS and SLR
2.4.1. GPS delay observable
2.4.2. The geodetic VLBI delay observable
2.4.3. The geodetic SLR delay observable
2.5. Derived tropospheric parameters in geodetic analyses
2.6. Recent developments in modelling TD and WV
2.7. Analysis strategies for TD/WV in space geodesy
3. Data and methodology
3.1. Introduction
3.2. Research methodology
3.3. Data 39
3.4. Data pre-processing
3.4.1. Box-Cox transformation
3.4.2. Estimation of periodic cycles and adaptive filtering
3.5. Data analysis strategies
3.5.1. Detrended fluctuation analysis
3.5.2. Wavelet transform
3.5.3. Hilbert-Huang transform
4. Modelling the stochastic properties of WV time series .
4.1. Introduction .
4.2. Basic concepts of time series analysis
4.3. Random variables
4.5. Stochastic processes
4.6. Geodetic parameters time series.
4.7. Concluding remarks .
5. Multi-scale WV fluctuation characteristics over southern Africa
5.1. Introduction
5.2. Vertical profile of WV from SHADOZ data
5.3. Multiscale organisation of WV in mid- and low-tropical Africa
6. Non-linear and nonstationary processes in geodetic TD/WV.
6.1. Introduction .
6.3. On the noise-Assisted geodetic data analysis.
6.3.1. Correlation of tropospheric WV and temperature using phase differences
6.4. Assessing the effect of non-linearity/stationarity in atmospheric range
6.4.1. SLR atmospheric range correction
6.5. Concluding remarks
7. Concluding remarks
Bibliography
