Huracan: A skillful end-to-end data-driven system for ensemble data assimilation and weather prediction
arXiv:2508.18486v2 Announce Type: replace-cross Abstract: Over the past few years, machine learning-based data-driven weather prediction has been transforming operational weather forecasting by providing more accurate forecasts while using a mere fraction of computing power compared to traditional numerical weather prediction (NWP). However, those models still rely on initial conditions from NWP, putting an upper limit on their forecast abilities. A few end-to-end systems have since been proposed, but they have yet to match the forecast skill of state-of-the-art NWP competitors. In this work,
The continuous advancements in machine learning and accessible computational power are enabling more sophisticated AI models to tackle complex scientific problems like weather prediction.
Achieving end-to-end AI-driven weather prediction at state-of-the-art levels means significantly faster and more energy-efficient forecasts, impacting multiple industries and national preparedness.
Operational weather forecasting could become less reliant on traditional numerical weather prediction (NWP) models, transitioning to more AI-centric, resource-efficient systems.
- · AI compute providers
- · Logistics and agriculture sectors
- · Emergency services
- · Weather data companies
- · Traditional NWP model developers
- · High-performance computing centers (potentially reduced demand for NWP-specific
More accurate and timely weather forecasts lead to improved decision-making across weather-sensitive industries.
Reduced computational costs for weather prediction could democratize access to advanced forecasting capabilities globally.
Nations that lead in AI weather prediction may gain a strategic advantage in disaster preparedness, resource management, and economic planning.
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Read at arXiv cs.LG