AUWave is a hybrid deep‑learning framework that reconstructs high‑resolution regional significant wave height (SWH) fields from sparse buoy observations. By combining a station‑wise encoder with a multi‑scale U‑Net enhanced by self‑attention, it outperforms a baseline model, especially when more than one buoy is available, and identifies critical anchor stations through buoy ablation studies. Cross‑basin tests in the Atlantic and Pacific demonstrate the model’s robustness and portability, suggesting its applicability for operational ocean monitoring and data assimilation.
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: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.
arXiv:2606. 13302v1 Announce Type: new Abstract: Wave parameters in the nearshore are crucial for coastal engineering, shoreline protection, marine hazard assessment, and coastal management for climate resilience.
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.
The paper performs a systematic hyperparameter search across five deep learning architectures (DLinear, LSTM, PatchTST, ResAttLstm, and Mamba2) and nine context lengths for forecasting significant wave height (Hs) at a single buoy, then re‑evaluates the best configurations on a 47‑buoy, 37‑year dataset. Across all models, performance converges to a very small spread (SD = 0.0014 m², 0.8% of the grand mean), yet all models still outperform persistence, though none consistently outperforms the others. The study finds that persistence already captures the dominant linear‑inertial signal in univariate Hs, and that model‑class differences are dwarfed by cross‑buoy variance, suggesting diminishing returns for further architecture engineering under univariate input settings.
arXiv:2608. 09683v1 Announce Type: new Abstract: Probabilistic coastal hazard assessments require accurate characterization of tropical cyclone (TC) parameters, yet datasets often contain missing records for the radius of maximum winds (Rmax), a key variable in Joint Probability Method analyses.
arXiv:2608.24561v1 Announce Type: new Abstract: Rapid earthquake magnitude estimation is central to earthquake early warning, yet many operational systems depend on dense regional seismic networks an...
arXiv:2601. 17074v5 Announce Type: replace-cross Abstract: Accurate estimation of unobserved quantities in time-varying inverse problems remains challenging when observations are sparse and only indirectly related to the target variable.
arXiv:2609.13281v1 Announce Type: cross Abstract: Ocean-bottom seismometers (OBS), originally deployed for geophysical research, continuously record low-frequency sound for months to years across bro...
arXiv:2608. 16070v1 Announce Type: cross Abstract: Reliable global ocean forecasting is critical for climate monitoring, marine navigation, and extreme event early warning.
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,...
arXiv:2604.06433v2 Announce Type: replace-cross Abstract: The impact of wave-induced forcing on the mean water level and nearshore currents is typically modeled through excess momentum fluxes, also k...