NOAA has launched a groundbreaking new suite of operational, artificial intelligence (AI)-driven global weather prediction models, marking a significant advancement in forecast speed, efficiency, and accuracy.
means you’ve safely connected to the .gov website. Share sensitive information only on official, secure websites.NOAA has launched a groundbreaking new suite of operational, artificial intelligence -driven global weather prediction models, marking a significant advancement in forecast speed, efficiency, and accuracy.
The models will provide forecasters with faster delivery of more accurate guidance, while using a fraction of computational resources. “NOAA’s strategic application of AI is a significant leap forward in American weather model innovation,” said Neil Jacobs, Ph.D., NOAA administrator. “These AI models reflect a new paradigm for NOAA in providing improved accuracy for large-scale weather and tropical tracks, and faster delivery of forecast products to meteorologists and the public at a lower cost through drastically reduced computational expenses.” : A weather forecast model that implements AI to deliver improved weather forecasts more quickly and efficiently than its traditional counterpart.: An AI-based ensemble system that provides a range of probable forecast outcomes to meteorologists and decision-makers. Early results show improved performance over the traditional GEFS, extending forecast skill by an additional 18 to 24 hours.: A pioneering, hybrid"grand ensemble" that combines the new AI-based AIGEFS with NOAA’s flagship ensemble model, the Global Ensemble Forecast System. Initial testing shows that this model, a first-of-its kind approach for an operational weather center, consistently outperforms both the AI-only and physics-only ensemble systems. — a new AI-based system that uses a variety of data sources to generate weather forecasts comparable to those produced by traditional weather prediction systems, such as GFS. Performance: shows improved forecast skill over the traditional GFS for many large-scale features. Notably, it demonstrates a significant reduction in tropical cyclone track errors at longer lead times. Efficiency: AIGFS’s most transformative feature. A single 16-day forecast uses only 0.3% of the computing resources of the operational GFS and finishes in approximately 40 minutes. This reduced latency means forecasters get critical data more quickly than they do from the traditional GFS. Area for future improvement: Though track forecasts are better, v1.0 shows a degradation in tropical cyclone intensity forecasts, which future versions will address. This AIGFS forecast in the form of a map, for December 10, 2025, shows the heavy precipitation from an atmospheric river hitting the U.S. Pacific Northwest. AI weather models like this one will protect life and property by improving forecast accuracy and timeliness for events such as the catastrophic flooding that impacted the Northwest. — an AI-based 31-member ensemble, similar to the GEFS, that provides a range of possibilities for weather forecasters and decision-makers rather than a single forecast model solution.Efficiency: requires only 9% of the computing resources of the operational GEFS. Area for future improvement: developers continue to improve the ensemble’s ability to create a range of forecast outcomes. — the most innovative application in the new suite. The HGEFS is a 62-member"grand ensemble" created by combining the 31 members of the physical GEFS with the 31 members of the AI-based AIGEFS. Performance: by combining two different modeling systems , the HGEFS creates a larger, more robust ensemble that more effectively represents forecast uncertainty. As a result, the HGEFS consistently outperforms both the GEFS and the AIGEFS across most major verification metrics. A NOAA first: to our knowledge, NOAA is the first organization in the world to implement such a hybrid physical-AI ensemble system., a joint initiative between NOAA's National Weather Service, Oceanic and Atmospheric Research labs, the Environmental Modeling Center in NOAA’s National Centers for Environmental Prediction, and the Earth Prediction Innovation Center. “Using Project EAGLE and the Earth Prediction Innovation Center, NOAA scientists continue to work with members of academia and private industry on more advancements in forecasting technology,” added Jacobs. The team leveraged Google DeepMind's GraphCast model as an initial foundation and fine-tuned the model using NOAA's own Global Data Assimilation System analyses. This additional training with NOAA data improved the Google model's performance, particularly when using GFS-based initial conditions.
Modeling Forecasts Artificial Intelligence Or AI Weather
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