"The A.I. Global Forecast System and the A.I. Global Ensemble Forecast System will now be routinely offered alongside its other weather model products. These new weather models offer slight increases in accuracy, but can be computed roughly 1,000 times faster than traditional numerical weather prediction models. NOAA is also offering a hybrid ensemble forecast, the HGEFS, which incorporates predictions from both the AIGEFS and the conventional GEFS."
"The new developments are the result of Project EAGLE, a collaboration between NOAA, the Earth Prediction Innovation Center, the Office of Oceanic and Atmospheric Research, and the National Weather Service. Project EAGLE aimed to standardize the process for gathering, formatting, and storing forecast and observational data for training new A.I. models, as well as streamline the process for operationalizing new advances into usable forecasts."
"Researchers have been investigating the uses of machine learning for weather forecasting for about 20 years, but a lot of the excitement over operational A.I. weather forecasts came in November 2023 when Google DeepMind launched GraphCast. As the first large scale demonstration of huge computational speedup and moderate accuracy increase for a global weather model, GraphCast quickly inspired more interest from weather forecasting agencies like the European Center for Midrange Weather Forecasting."
NOAA launched three AI-based weather forecast products: the A.I. Global Forecast System, the A.I. Global Ensemble Forecast System, and the hybrid HGEFS. The AI models provide modest accuracy gains while running roughly 1,000 times faster than traditional numerical weather prediction systems. The HGEFS blends AI ensemble outputs with conventional GEFS forecasts. The developments stem from Project EAGLE, a collaboration to standardize forecast and observational data collection, formatting, and storage and to streamline operational deployment of AI advances. Interest in operational AI forecasts surged after GraphCast in November 2023 and informed subsequent systems like ECMWF's AIFS.
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