arXiv AI July 28, 2026 By Islam Mansour, Georg Fischer, Ronny Haensch, Irena Hajnsek

Hybrid AI-Physical Modeling for Penetration Bias Correction in X-band InSAR DEMs: A Greenland Case Study

arXiv:2504. 08909v2 Announce Type: replace Abstract: Digital elevation models derived from Interferometric Synthetic Aperture Radar (InSAR) data over glacial and snow-covered regions often exhibit systematic elevation errors, commonly termed "penetration bias.

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arXiv AI

Aug 24

MeltwaterBench: Deep learning for spatiotemporal downscaling of surface meltwater

arXiv:2512.12142v2 Announce Type: replace-cross Abstract: The Greenland ice sheet is melting at an accelerated rate due to processes that are not fully understood and hard to measure. The distributio...

By Bj\"orn L\"utjens, Patrick Alexander, Raf Antwerpen, Til Widmann, Guido Cervone, Marco Tedesco

benchmarks

arXiv Machine Learning

Sep 25

Efficient Continuous DEM Reconstruction under Limited Target-Resolution Supervision

The paper introduces SCOPE, a method for reconstructing high‑resolution digital elevation models (DEMs) from coarser‑resolution training pairs. SCOPE learns a continuous terrain representation by predicting a latent coefficient field on a low‑resolution grid and reusing local Fourier residual functions, thereby decoupling coefficient prediction from output‑grid construction. Experiments on diverse land–ocean datasets show that SCOPE outperforms competing methods across six metrics, reduces reconstruction error by about 12 % at three‑times‑unseen scale, and achieves these gains with only a modest increase in computational cost.

By Zekai Shi, Meng Zhang, Haokun Zhang, Bo Zhang

fine-tuning

arXiv Machine Learning

Jun 5

The Equilibrium Response of Atmospheric Machine-Learning Models to Uniform Sea Surface Temperature Warming

arXiv:2510. 02415v3 Announce Type: replace-cross Abstract: Machine learning models for the global atmosphere that are capable of producing stable, multi-year simulations of Earth's climate have recently been developed.

By Bosong Zhang, Timothy M. Merlis

benchmarks

arXiv AI

Jul 24

Knowledge-Guided Time-Varying Causal Inference for Arctic Sea Ice Dynamics

arXiv:2601. 17647v3 Announce Type: replace-cross Abstract: Quantifying the causal relationship between sea ice thickness and sea surface height (SSH) is essential for understanding the mechanisms driving polar climate dynamics.

By Akila Sampath, Vandana Janeja, Jianwu Wang

diffusion benchmarks safety

arXiv Machine Learning

Jul 7

Exploring Convolutional Neural Processes for Weather Downscaling

arXiv:2607. 04190v1 Announce Type: new Abstract: Global reanalysis products such as ERA5-Land provide spatially complete weather fields but at resolutions too coarse for local applications, particularly in mountainous regions where temperature can vary by several degrees over short distances.

By Francisco Passos

benchmarks

arXiv Machine Learning

Sep 15

Data-driven Prediction of Satellite-observed Avalanche Activity from Snowpack Simulations

arXiv:2609.15485v1 Announce Type: new Abstract: Avalanche forecasting requires knowledge of snowpack conditions and recent avalanche activity, but field observations are sparse across large mountain...

By Jakob Grah, Filippo Maria Bianchi, Bert Kruyt, Karsten M\"uller

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MeltwaterBench: Deep learning for spatiotemporal downscaling of surface meltwater

Efficient Continuous DEM Reconstruction under Limited Target-Resolution Supervision

The Equilibrium Response of Atmospheric Machine-Learning Models to Uniform Sea Surface Temperature Warming

Knowledge-Guided Time-Varying Causal Inference for Arctic Sea Ice Dynamics

Exploring Convolutional Neural Processes for Weather Downscaling

Data-driven Prediction of Satellite-observed Avalanche Activity from Snowpack Simulations