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Multi-source AI news clustered, deduplicated, and scored 0–100 across authority, cluster strength, headline signal, and time decay.

  1. TOOL · arXiv cs.LG English(EN) ·

    Observation-driven correction of numerical weather prediction for marine winds

    Researchers have developed ORCA, a transformer-based deep learning model designed to correct errors in numerical weather predictions for marine winds. By assimilating in-situ observations, ORCA adjusts Global Forecast System (GFS) output, demonstrating significant error reduction up to 48 hours in advance. The model shows particular effectiveness along coastlines and shipping routes where observational data is more plentiful, offering a practical post-processing solution for improving forecast accuracy. AI

    IMPACT This model could improve maritime safety and operational efficiency by providing more accurate wind forecasts.

  2. RESEARCH · arXiv cs.LG English(EN) · · [2 sources]

    Physics-Guided Dual Decoding and Spectral Supervision for Global 3D Hydrometeor Prediction

    Researchers have developed PredHydro-Net, a novel deep learning framework designed to improve 3D hydrometeor forecasting. This physics-guided model addresses the limitations of standard deep learning in predicting extreme weather events by employing a dual-decoding architecture and spectral supervision. PredHydro-Net demonstrates superior performance compared to existing deep learning models and operational systems in detecting extreme events and accurately representing spatial textures, while also showing strong consistency with satellite data. AI

    IMPACT Improves accuracy and spatial fidelity in extreme weather event prediction, offering a more robust approach to long-tailed atmospheric forecasting.