2 citations · 2 across the 8 of their papers we have counts for
10 papers
Toward Enhanced Water Detection in SWOT Pixel Clouds using Dynamic Graph Neural Networks
Christoph Baumann, Junyang Gou, Christian Schwatke +3
The Surface Water and Ocean Topography (SWOT) mission offers unprecedented freshwater monitoring capabilities through its innovative wide-swath measurement system, which generates…
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…
GeoXplain: On-the-Fly Visual Explanations for Weather Foundation Models
Clemens Walter Koprolin, Leonardo Trentini, Benedikt Soja +2
Weather and climate foundation models produce high-dimensional forecasts whose learned relationships are difficult to inspect with static plots alone. GeoXplain is an interactive P…
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…
D-SHIFT: Transferring High Spatial Information from GRACE Monthly TWSA Mascon to Daily Products Using Generative Adversarial Networks
Andreas Dombos, Junyang Gou, Benedikt Soja
The Gravity Recovery and Climate Experiment (GRACE) and GRACE Follow-On missions provide monthly terrestrial water storage anomaly (TWSA) estimates for monitoring large-scale water…
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…
Finetuning a Weather Foundation Model with Lightweight Decoders for Unseen Physical Processes
Fanny Lehmann, Firat Ozdemir, Benedikt Soja +3
Recent advances in AI weather forecasting have led to the emergence of so-called "foundation models", typically defined by expensive pretraining and minimal fine-tuning for downstr…
CIDR interpolation: an enhanced SSA-based temporal filling framework for restoring continuity in downscaled GRACE(-FO) TWSA products
Yu Gao, Wenyuan Zhang, Junyang Gou +3
Global total water storage anomaly (TWSA) products derived from the Gravity Recovery and Climate Experiment (GRACE) and its Follow-On mission (GRACE-FO) are critical for hydrologic…
Uncertainties of Satellite-based Essential Climate Variables from Deep Learning
Junyang Gou, Arnt-Børre Salberg, Mostafa Kiani Shahvandi +9
Accurate uncertainty information associated with essential climate variables (ECVs) is crucial for reliable climate modeling and understanding the spatiotemporal evolution of the E…