Since its launch in 2002, NASA’s dual-satellite GRACE mission has provided invaluable data on Earth’s gravity field, enabling the study of mass redistribution through changes in equivalent water height (EWH). However, gaps in GRACE data hinder accurate climate modelling. This the
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Since its launch in 2002, NASA’s dual-satellite GRACE mission has provided invaluable data on Earth’s gravity field, enabling the study of mass redistribution through changes in equivalent water height (EWH). However, gaps in GRACE data hinder accurate climate modelling. This thesis investigates gap-filling using neural networks, with a focus on error and uncertainty quantification. Neural networks are trained on ESA’s Swarm EWH data and NASA’s GLDAS soil moisture data to predict GRACE EWH. The impact of sampling additional training data using quantified input errors is also explored. An experiment involving 20,400 models across major river basins demonstrates that neural networks achieve lower errors in EWH gravity field solutions compared to Swarm. The inclusion of additional training data significantly reduces errors and uncertainties, underscoring the potential of incorporating noise-level variation to enhance future models and improve climate change predictions.
