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20242026
most citedUncertainties of Satellite-based Essential Climate Variables from Deep Learning

2 citations · 2 across the 8 of their papers we have counts for

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5 papers · 1 filter

physics.geo-ph2026

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…

physics.geo-ph2026

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…

physics.geo-ph2026

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…

physics.geo-ph2025

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…

physics.geo-ph2024★ 2 cited

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…