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. We present an observation-informed correction approach for global numerical weather prediction (NWP) of marine winds. Rather than forecasting winds directly, we learn local correction patterns by assimilating the latest in-situ observations to adjust the Global Forecast System (GFS) output. We propose ORCA (Observation-informed
The increasing availability of in-situ ocean observations combined with advancements in AI/ML allows for more precise, real-time corrections to traditional NWP models.
Improved marine wind forecasts are critical for operational efficiency and safety across global shipping, offshore energy, and maritime logistics, reducing risks and costs.
Traditional numerical weather prediction models can now be significantly enhanced by real-time, observation-driven AI corrections, leading to more dynamic and accurate marine wind intelligence.
- · Shipping and logistics industry
- · Offshore energy companies
- · AI/ML weather tech providers
- · Marine insurance sector
- · Legacy weather forecasting services (without AI integration)
- · Companies reliant on less accurate forecasts
Enhances the safety and efficiency of global maritime operations.
Could lead to optimized shipping routes, reducing fuel consumption and emissions.
Potentially enables new forms of autonomous marine transport and advanced offshore energy infrastructure development due to increased predictability.
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Read at arXiv cs.LG