To obtain the crucial information about the boundary layer(Troposphere), there
is a need for measurement of large areas(>25,000 km2) with ne scale measurements of less than 1-2 km area. Over the past years, several methods have been developed to measure atmospheric water vapour fields, but none of them provide information on such small scales(< 1-2 km). With the recent development of high resolution numerical weather model, the need to provide high temporal and spatial data is ever so significant for proper utilization of the model.
This thesis presents a novel approach to estimate the water vapour/refractivity
from the propagation path link delay by using a network of links. We also present in this thesis a different and unique approach to remote sensing of the atmosphere using broadcast free-view TV signals. The theoretical background into the effects of the radio wave propagation through the lower atmosphere has been explored, thereby laying out the issues and shortcomings of existing remote sensing instruments.
The focus of this research is to determine the feasibility of using digital radio and television signals (DVB-T and DAB) data for Tropospheric propagation
delay modelling. To estimate the path delay variability of signal propagation
through the troposphere we have used the ray tracing approach.
For the estimation of phase delay between the transmitter and receiver, a two
dimensional ray tracing algorithm was developed to use different atmospheric
remote sensing data to calculate the propagation delay in the troposphere. Statistical analysis of the path delays to showing the variation effects due to different seasons, distance and time has been presented. This thesis further describes the retrieval algorithm which uses the path delay from ray tracing to implement tomography inversion modelling technique. The technique uses a network of path link delays to estimate refractivity. The simulation results obtained from this investigation show that water vapour/refractivity can be estimated from path link delays using tomography reconstruction technique.
|Date of Award||27 Jun 2017|
|Supervisor||Robert Watson (Supervisor)|