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Neptune: An AI model for Global Ocean Subseasonal Prediction
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arXiv AI
Sep 10
Neptune: An AI model for Global Ocean Subseasonal Prediction
Neptune is an end‑to‑end data‑driven framework that emulates global ocean and sea‑ice states for subseasonal‑to‑seasonal (S2S) forecasting up to 60 days. It combines Convolutional Neural Networks and Spherical Fourier Neural Operators to capture both local features and global cross‑scale interactions, producing daily outputs for temperature, salinity, currents, sea‑surface height, and sea‑ice metrics at 1° and 0.25° resolution. Evaluated against metrics such as RMSE, CRPS, ACC, and climate indices (ENSO, IOD), Neptune reproduces the spatio‑temporal evolution of oceanic fields and remains stable over long timescales.
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.
arXiv Machine Learning
Oct 22
YingLan: A Multimodal Ocean Foundation Model for Unified Representation Learning from Heterogeneous Observations
arXiv:2610.29313v1 Announce Type: new Abstract: Multimodal ocean modeling remains challenging due to substantial heterogeneity in data structures, physical semantics, and spatiotemporal characteristi...
arXiv Machine Learning
Sep 25
HClimRep-Ocean: A Global Ocean Emulator on an Unstructured Mesh
HClimRep‑Ocean is a machine‑learning emulator that operates directly on the native unstructured mesh of the FESOM2 ocean model, trained on a 209‑year AWI‑CM3 control run and run without atmospheric forcing except at initialization. It shows strong skill for current forecasts at 30‑day lead times, outperforming all references, while temperature and salinity forecasts are best served by a damped‑anomaly persistence approach. In independent OceanBench testing, a reanalysis‑trained variant achieves the lowest RMSE against GLORYS reanalysis, demonstrating the competitiveness of the native‑mesh approach.
arXiv AI
Aug 18
OceanDepths: A Global Dataset of Paired Subsurface and Surface Ocean Observations
OceanDepths is the first open, global, AI‑ready dataset that pairs satellite‑derived sea surface temperature, salinity, and height with co‑located EN4 subsurface temperature and salinity profiles, complemented by GLORYS12 reanalysis data. It covers 2000–2024 at 0.1°×0.1° spatial resolution and weekly temporal resolution, providing over 9.5 million paired profiles interpolated to 50 depth levels. The dataset’s 4‑D multivariate structure, high resolution, long temporal extent, and extreme sparsity of subsurface observations make it a challenging testbed for novel AI methods, with demonstrated use in subsurface state reconstruction and potential for observation‑based forecasting.
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.