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.

Summary generated by The Flow from the publisher's feed. The full article lives at arXiv AI.

safety

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

Aug 4

K-STEMIT: Knowledge-Informed Spatio-Temporal Efficient Multi-Branch Graph Neural Network for Subsurface Stratigraphy Thickness Estimation from Radar Data

arXiv:2604. 09922v2 Announce Type: replace Abstract: Subsurface stratigraphy contains important spatio-temporal information about accumulation, deformation, and layer formation in polar ice sheets.

By Zesheng Liu, Maryam Rahnemoonfar

benchmarks

arXiv AI

Jul 7

Evaluating Skill and Stability of ArchesWeather and ArchesWeatherGen under Multi-Decadal Climate Simulations

arXiv:2605. 29976v2 Announce Type: replace-cross Abstract: We evaluate the climate simulation capabilities of ArchesWeather and ArchesWeatherGen, two machine learning models originally trained for weather forecasting and evaluated up to a 10-day lead time.

By Renu Singh, Robert Brunstein, Antonia Jost, Yana Hasson, Thomas Rackow, Claire Monteleoni, Christian Lessig, Guillaume Couairon

benchmarks

arXiv Machine Learning

Jun 8

Advanced Flood Prediction with Physics-Guided Deep Learning: Combining UNet, FNO, and SAR/Optical Imagery

arXiv:2606. 06524v1 Announce Type: cross Abstract: Accurate and scalable flood mapping remains challenging due to limited ground observations, heterogeneous terrain conditions, and the difficulty of enforcing hydrodynamic consistency within data-driven models.

By Tewodros Syum Gebre, Jagrati Talreja, Leila Hashemi-Beni

rag multimodal benchmarks

Related stories

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

K-STEMIT: Knowledge-Informed Spatio-Temporal Efficient Multi-Branch Graph Neural Network for Subsurface Stratigraphy Thickness Estimation from Radar Data

Evaluating Skill and Stability of ArchesWeather and ArchesWeatherGen under Multi-Decadal Climate Simulations

Advanced Flood Prediction with Physics-Guided Deep Learning: Combining UNet, FNO, and SAR/Optical Imagery