arXiv:2610.03759v3 Announce Type: replace-cross Abstract: Ocean wave forecasting is essential for maritime safety, offshore operations, and coastal resilience, yet remains challenging due to systemat...
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.
arXiv:2609.13206v1 Announce Type: cross Abstract: Marine ecosystems are increasingly impacted by climate change, necessitating tools to identify and predict spatial habitat information. To build such...
arXiv:2609.13242v1 Announce Type: cross Abstract: Subgrid parametrizations of vertical mixing in ocean models depend on free coefficients that cannot be measured directly and must be calibrated again...
arXiv:2607. 19147v1 Announce Type: cross Abstract: Data-driven methods have revolutionized ocean modeling, yet current approaches rely heavily on complete reanalysis datasets, imposing computational constraints and limiting model performance to that of the training data.
OceanMoE is a structured conditional sparse Mixture-of-Experts framework designed for long‑horizon multivariate ocean forecasting. It fuses cross‑variable information to build target‑specific local representations and performs content‑conditioned sparse routing at each spatial location, with the number of active experts adjusted by router confidence. Experiments on ORAS5 data show that OceanMoE reduces aggregate forecasting error and maintains lower geometric‑mean normalized RMSE compared to baselines, while expert allocation varies with prediction targets and locations.
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: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.
The paper introduces Pangu‑Bayes, a probabilistic forecasting hierarchy that separates atmospheric‑state uncertainty from learned‑model uncertainty as distinct stochastic variables, allowing cross‑flow perturbations of the evolving state with Bayesian parameter samples. In tests on 90 held‑out 2023 tropical cyclones, Pangu‑Bayes reduces track, pressure, and wind errors by 54.2%, 17.2%, and 24.9% respectively, and improves rapid‑intensification detection. The study finds that atmospheric‑state variability more consistently improves track prediction, while learned‑model variability more often enhances intensity prediction, demonstrating how model‑defined uncertainty resolution can be linked to target‑dependent value and dynamical interpretation.
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.
Subseasonal-to-seasonal (S2S) forecasting is societally critical, supporting decision-making in sectors ranging from water and agricultural management to disaster risk reduction, energy planning, and...
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.