arXiv:2508. 10178v3 Announce Type: replace-cross Abstract: Shelf seas are important for the economy and the carbon cycle, but shelf sea observations for carbon pools are often sparse, or highly uncertain.
arXiv:2610.15518v1 Announce Type: cross Abstract: Probabilistic ocean modeling is essential for risk-aware marine operations, climate analysis, and scientific decision-making, where downstream decisi...
arXiv:2605.15470v3 Announce Type: replace Abstract: Ocean dynamics are inherently chaotic, yet existing machine learning ocean models produce only deterministic forecasts. We introduce Njord, a proba...
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:2608. 04230v1 Announce Type: new Abstract: Deep learning models for scientific spatio-temporal downscaling often minimize reconstruction error while failing to preserve physically meaningful multi-scale structure.
arXiv:2608. 01864v1 Announce Type: cross Abstract: Predicting drought risk is essential for anticipating impacts on water resources, agriculture, ecosystems, and climate adaptation planning.
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.
arXiv:2601.16834v4 Announce Type: replace Abstract: Wall-to-wall biomass density estimation from NASA's GEDI mission requires interpolating sparse LIDAR observations across heterogeneous landscapes....
arXiv:2609.12744v1 Announce Type: new Abstract: Timely monitoring of coastal water quality is critical for environmental protection, yet conventional satellite workflows rely on downlink and ground p...
arXiv:2607. 07834v1 Announce Type: new Abstract: Pseudo-nitzschia diatoms pose recurrent risks to coastal ecosystems and shellfish harvesting along the Portuguese Atlantic coast.
Reliable global ocean forecasting is critical for climate monitoring, marine navigation, and extreme event early warning. Physics-based ocean forecasting models impose prohibitive computational costs,...
The paper presents a probabilistic post‑processing framework that uses a conditional Variational Autoencoder (cVAE) with a non‑parametric decoder to adjust seasonal Arctic sea‑ice forecasts. By replacing the Gaussian decoder with a generator and optimizing with Continuous Ranked Probability Score (CRPS), the method produces larger, bias‑corrected ensembles that are better calibrated, more consistent with observations, and exhibit higher resolution and sharper spectral power than standard cVAEs. The approach demonstrates improved forecast skill and reduced errors compared to benchmark predictions.