ECMWF Artificial Intelligence/Integrated Forecasting System Ensemble (AIFS Ensemble)

Description

The ECMWF AIFS Ensemble is a groundbreaking global weather prediction system that leverages advanced artificial intelligence for ensemble forecasting. Unlike traditional NWP ensembles, AIFS employs deep learning architectures—primarily transformer neural networks—trained on decades of global reanalysis and forecast data, as well as real-time observations. These models are capable of capturing nonlinear relationships, teleconnections, and rare/extreme events that can be missed by classical statistical perturbations.

Each ensemble member is generated using stochastic variations in initial conditions and (in some members) in model weights or inputs, resulting in a physically realistic spread of potential atmospheric scenarios. The AI core is coupled with physical constraint layers to ensure meteorological plausibility and mass/energy conservation. Hybrid techniques blend neural forecasts with ECMWF's operational post-processing and bias correction, delivering skillful, probabilistic weather guidance at global scale.

The AIFS ensemble supports new research into forecast uncertainty, climate extremes, and rapid scenario generation for data-driven applications.

Detail

• Product page: https://www.ecmwf.int/en/forecasts/datasets/open-data
• Ensemble structure: 51 members per run (member 0 = control, 1-50 = perturbed), supporting probabilistic and quantile analysis.
• Grid & Frequency: 0.25° (~28 km) global grid; 4 cycles per day (00, 06, 12, 18 UTC).
• Forecast range: Up to 15 days (0-144h every 3h, then to 360h every 6h).
• Parameters: Wide suite of meteorological variables at the surface and pressure levels.
• Primary Use Cases: Probabilistic forecasting, risk quantification, ensemble/ML benchmarking, energy and resource planning, insurance, hydrology, climate/extremes research, open data science.

Parameters: