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

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

Read the original on arXiv Machine Learning →

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.

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

arXiv AI
Jun 26

Sampling sea state using a diffusion model

arXiv:2606. 26389v1 Announce Type: cross Abstract: Sea state prediction is essential for operational maritime applications and coupled earth system modeling, yet current spectral wave models remain computationally prohibitive for many use cases, including online coupling to climate simulations and making probabilistic (ensemble-based) predictions.

By Jiarong Wu, Bertrand Chapron, 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
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
Jun 11

LakeFM: Toward a Foundation Model for Aquatic Ecosystems Using Irregular Multivariate Multi-depth Time Series Data

arXiv:2606. 11268v1 Announce Type: new Abstract: Understanding and forecasting lake dynamics is critical for monitoring water quality and ecosystem health across lakes and reservoirs.

By Abhilash Neog, Sepideh Fatemi, Medha Sawhney, Kazi Sajeed Mehrab, Aanish Pradhan, Bennett J. McAfee, Emma Marchisin, Arka Daw, Robert Ladwig, Cayelan C. Carey, Paul Hanson, Anuj Karpatne