Machine learning reshapes weather prediction
Machine learning is remaking how meteorologists generate weather forecasts. The Finnish Meteorological Institute has developed Aila, an AI model intended to work alongside conventional forecasting approaches and enhance prediction accuracy for regional conditions, especially across Finland and the Nordic region. The system operates as a complementary tool to physics-based numerical weather prediction.
Conventional forecasting relies on mathematical representations of atmospheric physics. Data-driven neural networks take a different path: they learn patterns from historical weather records and analysis data, then produce rapid forecasts after an initial training phase that demands significant computational power.
Aila's training drew on the ERA5 reanalysis dataset from the European Centre for Medium-Range Weather Forecasts (ECMWF), supplemented by analysis data from ECMWF's Integrated Forecasting System (IFS) and the regional MEPS model. This combination teaches the neural network to recognize how weather patterns evolve and to tailor predictions to Northern European conditions.
Extreme cold became the proving ground
January 2026 offered a rigorous test. Finland experienced unusually cold conditions, with temperatures in parts of Lapland dropping below -20°C. Such extreme cold and the atmospheric inversion patterns that accompany it pose difficulties for traditional forecasting systems.
Aila and competing AI systems, including the Norwegian Meteorological Institute's Bris model, delivered strong results forecasting the cold snap. When measured against actual station observations, Aila added value particularly for predictions one to two days ahead, outperforming conventional numerical models. Gaps remain in uncertainty quantification, the most severe cold extremes, variable regional phenomena like rainfall, coastal forecasting, and extended outlooks covering three to ten days.
The vision is for AI forecasting tools to augment meteorologists' capabilities, especially when weather patterns shift rapidly or present complex regional variations.
Open release accelerates global development
Aila has undergone testing at the Finnish Meteorological Institute for roughly a year. The institute is now broadening access by releasing the model weights—the learned computational parameters—on the Hugging Face platform. This transparency enables comparison, validation, and continued refinement while fostering wider participation in AI weather forecasting research.
The model rests on Anemoi, an open software framework built by ECMWF in partnership with national weather services. Anemoi supplies a modular, adaptable base for constructing, training, and running machine learning weather systems in operational settings.
Aila employs a graph neural network with a stretched-grid design. The stretched grid permits global modeling while concentrating forecast precision over Northern Europe rather than distributing it uniformly worldwide.
Training Aila demands substantial GPU resources—the kind found in high-performance computing centers built for AI. The project leveraged GPU capacity from the LUMI supercomputer, accessed by Finnish Meteorological Institute researchers through Finland's national LUMI Extreme Scale allocation. Such infrastructure enables training of neural networks at Aila's scale and sophistication.
Research meets operations in a European ecosystem
The Finnish Meteorological Institute's AI weather team numbers around ten specialists. The effort merges the institute's research capabilities with its day-to-day forecasting operations. On the international stage, the work connects to ECMWF's Machine Learning Project and Nordic partnerships, including collaboration with Norway's Bris model, whose architecture forms the basis for Aila.
Aila exemplifies a larger shift in which AI, extensive observational and analytical datasets, and high-performance computing are unlocking new possibilities for environmental modeling and prediction. The Finnish Meteorological Institute participates in the Academy of Finland's FAME Flagship program, through which Aila integrates cutting-edge AI research, supercomputing, cross-border teamwork, and the practical advancement of forecasting services.
In 2025, the Anemoi community received the European Meteorological Society (EMS) Technology Achievement Award. The recognition underscored Anemoi's scientific and technical value and illustrated how transparent European cooperation can speed the transition to advanced weather forecasting. Aila stands as proof that collaborative research can transition from the laboratory into real-world forecasting operations.
