arXiv AI

MeltwaterBench: Deep learning for spatiotemporal downscaling of surface meltwater

arXiv Machine Learning

Aug 12

Deep Learning-Based Statistical Downscaling of Sea Surface Temperature Using a Residual Corrective Neural Network

arXiv:2608. 10022v1 Announce Type: cross Abstract: The large-scale oceanic and atmospheric forecasts provided by global climate models typically lack sufficient resolution to accurately capture the response of the coastal ocean to atmospheric forcing and coastal circulation that drive fine-scale SST variability.

By Onkar Jadhav, Tim French, Ivica Janekovic, Nicole L. Jones, Matthew Rayson

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

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 Machine Learning

Jul 7

GlacierCastAI: Predicting Glacier Retreat from Multi-Modal Satellite Imagery and Climate Signals

arXiv:2607. 04117v1 Announce Type: new Abstract: ERA5 seasonal climate variables contain predictive information about future glacier retreat beyond what satellite imagery alone provides, yet existing deep learning methods focus on mapping current boundaries rather than forecasting future ones.

By Arunkumar Ramachandran

multimodal benchmarks

arXiv Machine Learning

Aug 13

Earth observation embeddings are effective sub-grid descriptors for probabilistic weather downscaling

arXiv:2608. 12271v1 Announce Type: new Abstract: Global weather reanalyses and forecasts resolve the evolving atmospheric state on coarse grids, but site-specific applications require predictions at arbitrary locations where near-surface conditions also depend on unresolved terrain and land-surface properties.

By Pedro Sousa (Department of Computer Science, University of Cambridge), Will Tebbutt (Department of Engineering, University of Cambridge), Sadiq Jaffer (Department of Computer Science, University of Cambridge), Robin Young (Department of Computer Science, University of Cambridge), Anil Madhavapeddy (Department of Computer Science, University of Cambridge), Richard E. Turner (Department of Engineering, University of Cambridge)

rag

arXiv Machine Learning

Jul 7

From Global to Local: Efficient Regional Weather Downscaling with Global Weather Foundation Model

arXiv:2607. 03279v1 Announce Type: new Abstract: Accurate regional weather prediction requires resolving fine-scale structure while remaining consistent with global dynamics.

By Wiktor Kamzela, Jakub Kubiak, Adam Dobosz, J\k{e}drzej Miczke, Anatol Kaczmarek, Piotr Wyrwi\'nski, Wojciech Stefaniak, Wojciech Kot{\l}owski

diffusion

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 AI

Jun 17

Remote sensing data imputation using deep learning for multispectral imagery

arXiv:2605. 24003v2 Announce Type: replace-cross Abstract: Remote sensing techniques have been increasingly utilised in aquatic applications in recent years.

By Shuang Liu, Fiona Johnson, Rohitash Chandra

arXiv Machine Learning

3d ago

Multi-Label Proportion Learning for Sea-Ice Type Prediction

arXiv:2609.16347v1 Announce Type: new Abstract: Sea-ice type prediction is important for climate monitoring, maritime navigation, and decision-making in polar regions. The main source of label data f...

By Samira Alkaee Taleghan, Younghyun Koo, Andrew P. Barrett, Farnoush Banaei-Kashani

multimodal

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 Machine Learning

Jun 18

Spatiotemporal downscaling and nowcasting of urban land surface temperatures with deep neural networks

arXiv:2605. 13566v2 Announce Type: replace Abstract: Land Surface Temperature (LST) is a key variable for various applications, such as urban climate and ecology studies.

By Solomiia Kurchaba, Angela Meyer

benchmarks safety

arXiv Machine Learning

Jul 8

Domain-Adaptive Climate Downscaling Under Temporal Distribution Shift

arXiv:2607. 05645v1 Announce Type: new Abstract: Deep-learning-based climate downscaling aims to learn relationships from historical low-resolution (LR) and high-resolution (HR) climate data to generate HR climate projections.

By Shuochen Wang, Nishant Yadav, Auroop R. Ganguly

fine-tuning safety

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