arXiv Machine Learning
Taking a Second Look: Correcting Sea Ice Forecasts with Sparse Observations
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arXiv AI
Sep 28
AUWave: A Data-Driven Model for Reconstructing Significant Wave Heights Using Sparse Observations
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 Machine Learning
Jun 9
SwAIther-Precip: Lead-Time-Aware Bias Correction Enables Kilometer-Scale Downscaling of Global AI Precipitation Forecasts over Switzerland
arXiv:2605. 16163v2 Announce Type: replace-cross Abstract: Skillful medium-range precipitation forecasting at kilometer scale remains challenging over complex terrain because precipitation arises from multiscale nonlinear processes that global models cannot explicitly resolve at affordable cost.
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 Machine Learning
Aug 6
Transferable Dual-Stream Representations for Mesoscale-Preserving Sea Surface Temperature Downscaling
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 Machine Learning
Jul 7
From Global to Local: Efficient Regional Weather Downscaling with Global Weather Foundation Model
arXiv:2607. 03279v1 Announce Type: new Abstract: Accurate regional weather prediction requires resolving fine-scale structure while remaining consistent with global dynamics.
arXiv Machine Learning
Jun 11
PCA-Enhanced Adaptive NVAR Framework for High-Resolution Sea Surface Temperature Forecasting in the East Sea
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.