By Isaac Schultz Published February 24, 2025 | Comments (0) | A view of a sailing boat during cloudy weather and sunset in San Francisco, California, United States on August 17, 2024. Photo by Tayfun Coskun/Anadolu via Getty Images The European Center for Medium-Range Weather Forecasts (ECMWF) just launched an AI-powered forecasting model, which the center says outperforms state-of-the-art physics-based models by up to 20%. The model is dubbed the Artificial Intelligence Forecasting System (AIFS). According to an ECMWF release, the new model operates at faster speeds than physics-based models and takes approximately 1,000 times less energy to make a forecast. The ECMWF, now in its 50th year of operation, produced ENS, one of the worlds leading medium-range weather prediction models. Medium-range forecasting includes weather predictions made between three days and 15 days in advance, but ECMWF also forecasts weather up to a year ahead. Weather forecast models are essential for states and local governments to stay prepared for extreme weather eventsas well as for more daily needs, like knowing what the weather will be like on your upcoming vacation.Traditional weather prediction models make forecasts by solving physics equations. A limitation of these models is that they are approximations of atmospheric dynamics. A compelling aspect of AI-driven models is that they could learn more complex relationships and dynamics in weather patterns directly from the data, rather than relying only on previously known and documented equations. The ECMWFs announcement comes on the heels of Google DeepMinds GenCast model for AI-powered weather prediction, the next iteration of Googles weather prediction software that includes NeuralGCM and GraphCast. GenCast outperformed ENS, the ECMWFs leading weather prediction model, on 97.2% of targets across different weather variables. With lead times greater than 36 hours, GenCast was more accurate than ENS on 99.8% of targets.But the European Center is innovating, too. The launch of AIFS-single is just the first operational version of the system. This is a huge endeavour that ensures the models are running in a stable and reliable way, said Florian Pappenberger, Director of Forecasts and Services at ECMWF, in the center release. At the moment, the resolution of the AIFS is less than that of our model (IFS), which achieves 9 km [5.6-mile] resolution using a physics-based approach.We see the AIFS and IFS as complementary, and part of providing a range of products to our user community, who decide what best suits their needs, Pappenberger added. The team will explore hybridizing data-driven and physics-based modeling to improve the organizations ability to predict weather with precision. Physics-based models are key to the current data-assimilation process, said Matthew Chantry, Strategic Lead for Machine Learning at ECMWF and Head of the Innovation Platform, in an email to Gizmodo. This same data-assimilation process is also vital to initialize every day machine learning models, and allow them to make forecasts.One of the next frontiers for machine learning weather forecasting is this data-assimilation step, which if solved would mean that the full weather forecasting chain could be based on machine learning, Chantry added. Chantry is a co-author of a study awaiting peer review that describes a data-driven, end-to-end forecast system that does not rely on physics-based reanalysis.Called GraphDOP, the system uses observable quantities such as brightness temperatures from polar orbiters to form a coherent latent representation of Earth System state dynamics and physical processes, the team wrote, and is capable of producing skillful predictions of relevant weather parameters up to five days into the future. Integrating artificial intelligence methods with physics-driven weather prediction modeling is a promising venue for more precise forecasting. Testing to date indicates that AI-powered forecasting can outperform historical models, but so far those models have relied on reanalysis data. Observations on the ground were essential for training the models, and it remains to be seen just how impressive the technologys forecasting abilities will be when its forced to go off-script.Daily NewsletterYou May Also Like By AJ Dellinger Published February 24, 2025 By AJ Dellinger Published February 21, 2025 By Thomas Maxwell Published February 20, 2025 By Lucas Ropek Published February 20, 2025 By Margherita Bassi Published February 15, 2025 By Lucas Ropek Published February 14, 2025