arXiv AI By Simon Donike, Ruben Cartuyvels, Antonino Ian Ferola, Elisa Carli, Diego Fernandez Prieto, Marie-Helene Rio

OceanDepths: A Global Dataset of Paired Subsurface and Surface Ocean Observations

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arXiv:2608. 16373v1 Announce Type: cross Abstract: Despite comprising over 70\% of its surface, the world's oceans are critically underobserved compared to the land surface or the atmosphere.

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arXiv AI
Jun 3

Samudra 2: Scaling Ocean Emulators across Resolutions

arXiv:2606. 02610v1 Announce Type: cross Abstract: Ocean general circulation models (OGCMs) are essential to climate science but computationally expensive, limiting ensemble size and forcing scenarios.

By Yuan Yuan, Jesse Rusak, Alexander Merose, Adam Subel, Pavel Perezhogin, Alistair Adcroft, Carlos Fernandez-Granda, Laure Zanna
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 11

PCA-Enhanced Adaptive NVAR Framework for High-Resolution Sea Surface Temperature Forecasting in the East Sea

arXiv:2606. 12141v1 Announce Type: new Abstract: Accurate forecasting of sea surface temperature (SST) in regional seas such as the East Sea is crucial for monitoring marine ecosystems, assessing climate risks, managing fisheries, and conducting naval operations.

By Sherkhon Azimov, Susana L\'opez-Moreno, Eric Dolores-Cuenca, JinYong Choi, Sangil Kim
arXiv Machine Learning
Jul 21

BG4Sea: Biogeochemical Seasonal Forecastability via Progressive Information Scaling

arXiv:2607. 16731v1 Announce Type: new Abstract: Marine biogeochemical forecasting is increasingly important for managing marine ecosystems and the carbon cycle, yet global, seasonal forecast products lag far behind physical oceanography, held back by the complexity of the processes involved and by data scarcity.

By Gabriela Martinez Balbontin, Anastase Charantonis, Dominique Bereziat, Stefano Ciavatta
arXiv Machine Learning
6d ago

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)