ECMWF AIFS ENS: Ensemble AI Weather Forecasts in Zarr Format

An ensemble of 51 members from the ECMWF Artificial Intelligence Forecasting System (AIFS). The model is trained on ECMWF's ERA5 re-analysis and operational NWP analyses. Forecasts have been converted to the cloud-optimized Icechunk Zarr format by dynamical.org.

Use Cases

• Compare AI-driven ensemble forecasts against traditional numerical weather prediction models based on the 51-member ensemble.
• Benchmark AI weather forecasting performance using the ECMWF-trained AIFS model.
• Develop probabilistic weather prediction tools leveraging the ensemble spread data.
• Study the application of machine learning in operational meteorology based on the AIFS system.

Strengths

• Contains 51 ensemble members, providing a measure of forecast uncertainty.
• Model is trained on authoritative ECMWF data sources: ERA5 re-analysis and operational NWP analyses.
• Data is stored in a cloud-optimized Icechunk Zarr format, which is efficient for large-scale data access.

Limitations

• Column-level documentation is absent; field semantics must be inferred after download.
• Row count is unknown, which may limit suitability assessment.
• Last update date is unknown; freshness unverified.

Provenance

Source

European Centre for Medium-Range Weather Forecasts (ECMWF)

Collection Method

Generated by the data-driven AIFS-ENS ensemble forecast model.