8 papers
Integrating GNSS-Derived Zenith Wet Delay into a Weather Foundation Model Improves Precipitation Forecasting
Leonardo Trentini, Fanny Lehmann, Laura Crocetti +1
Global Navigation Satellite Systems (GNSS), best known for positioning, also serve weather science, as atmospheric water vapour delays their signals. This delay, the Zenith Wet Del…
GNSS-FM: A Self-Supervised Foundation Model for Daily GNSS Displacement Time Series
Nick Teutschmann, Laura Crocetti, Fanny Lehmann +2
Displacement time series from Global Navigation Satellite Systems (GNSS) are essential for a wide range of applications, including monitoring tectonic crustal deformations and inve…
Can AI Weather Models Predict Beyond Two Weeks? A Quantitative Benchmark and Analysis of Long Rollouts
Fanny Lehmann, Firat Ozdemir, Yun Cheng +4
While AI weather models excel at short-to-medium range forecasts (up to 15 days), they frequently suffer from ill-defined "instabilities" when rolled out over longer horizons. This…
Correcting Neural Operator Spectral Bias via Diffusion Posterior Sampling with Sparse Observations
Niccolò Perrone, Fanny Lehmann, Stefania Fresca +1
Neural operator surrogates (NO) approximate PDE solutions orders of magnitude faster than numerical solvers, but suffer from spectral bias: high-frequency content is systematically…
Earth System Foundation Model (ESFM): A unified framework for heterogeneous data integration and forecasting
Firat Ozdemir, Yun Cheng, Salman Mohebi +11
Foundation models (FMs) for the Earth system learn statistical relationships between physical variables across massive datasets to enable versatile downstream applications through…
Integrating Fourier Neural Operators with Diffusion Models to improve Spectral Representation of Synthetic Earthquake Ground Motion Response
Niccolò Perrone, Fanny Lehmann, Hugo Gabrielidis +2
Nuclear reactor buildings must be designed to withstand the dynamic load induced by strong ground motion earthquakes. For this reason, their structural behavior must be assessed in…