Browsing by Subject "3D Convolutional Neural Network"

This thesis presents DeepGT, a 3D Convolutional Neural Network designed to enhance the spatial resolution of GNSS Tropospheric Tomography, a technique for estimating atmospheric water vapor distribution using GNSS signals. By utilizing Slant Wet Delays from dense GNSS networks and boundary meteorological data from Numerical Weather Prediction models, DeepGT refines low-resolution tomographic wet refractivity fields. The proposed method quadruples the horizontal resolution, while improving the accuracy of the tomographic reconstruction. Two experiments are conducted to validate this: one with real-world SWEPOS data and another with a hypothetical dense GNSS network. The results demonstrate the potential of deep learning models such as DeepGT in enhancing GNSS Meteorology, with implications for improved weather forecasting and climate studies.