arXiv AI
BridgeCast: Bridging Ocean Wave Forecasts to Reanalysis via Flow Matching with Exogenous Variables
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Oct 15
OceanFlow: Coherent Flow Matching for Probabilistic Ocean Modeling
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 Machine Learning
Jun 9
Observation-driven correction of numerical weather prediction for marine winds
arXiv:2512. 03606v2 Announce Type: replace Abstract: Accurate marine wind forecasts are essential for safe navigation, ship routing, and energy operations, yet they remain challenging because observations over the ocean are sparse, heterogeneous, and temporally variable.
arXiv AI
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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.
arXiv Machine Learning
Oct 3
Physics-Refined Spatiotemporal Forecasting on Open-Boundary Hydrologic Graphs
The paper introduces a physics‑refined framework for spatiotemporal forecasting on open‑boundary hydrologic graphs, addressing instability caused by missing external boundary forcing. It learns ghost node proxies to approximate unobserved inputs and applies two physics refiners: one enforcing local consistency with two‑hop neighbors, and another using a physics‑guided graph neural operator to reduce long‑horizon drift. Experiments on two real‑world hydrologic graphs show improved prediction accuracy and stability compared to existing learning‑based and physics‑informed models.
arXiv Statistics ML
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Physics-Guided Flow Matching: A Generative AI Framework for Global Total Electron Content Forecasting
The paper introduces a physics‑guided generative AI framework that uses flow matching to forecast global total electron content (TEC) one day ahead. By embedding ionospheric dynamics described by a partial differential equation into the transformation process, the model blends physical knowledge with statistical patterns, adjusting their influence locally. Trained on a year of TIEGCM‑generated TEC data, the approach outperforms statistical, deep learning, and other generative baselines, achieving a 39.2% lower RMSE than ConvLSTM and a 23.8% improvement over the same framework without physical guidance.
arXiv Machine Learning
Oct 5
SDECast: Probabilistic Weather Forecasting in Continuous Time with Neural SDEs
SDECast is a Neural Stochastic Differential Equation framework designed for continuous‑time probabilistic weather forecasting. Unlike traditional autoregressive models that operate at fixed temporal resolutions and can accumulate errors at short time steps, SDECast learns stochastic dynamics directly in physical space without repeated SDE simulation during training. Experiments on a simulated geophysical flow demonstrate that SDECast captures meaningful drift dynamics and reproduces continuous‑time behavior, while large‑scale tests show skillful probabilistic forecasts up to five days ahead at hourly resolution.