GIK-ECMWF-PAR provides lightweight parquet reference files for ECMWF IFS ensemble forecast GRIB files stored on AWS S3. The dataset, created by E4DRR, contains tables of byte-range references that enable Dask-based parallel analysis without downloading the full 3-4 GB raw data files. It was last updated on April 8, 2026.
Use Cases
- Perform parallel ensemble forecast analysis based on virtual reference pointers to raw GRIB data.
- Enable lightweight data access for large-scale climate model experiments based on byte-range references.
- Conduct efficient data subsetting and extraction for specific forecast variables based on the S3 reference structure.
Strengths
- Reduces data access footprint from 3-4 GB GRIB files to ~140 KB parquet reference files.
- Enables Dask-based parallel analysis workflows without downloading full raw datasets.
- References point directly to authoritative ECMWF IFS ensemble forecast data on AWS S3.
Limitations
- Column-level documentation is absent; field semantics must be inferred after download.
- Row count and specific file formats are unknown, which may limit suitability assessment.
- Data may reflect geographic or temporal bias inherent to the ECMWF source models.
Provenance
- Source
- ECMWF IFS ensemble forecasts on AWS S3 (s3://ecmwf-forecasts/).
- Collection Method
- Virtual reference files created by E4DRR, containing pointers to byte ranges within raw GRIB files.
- Time Range
- null
- Freshness
- Last updated 2026-04-08 18:09:27.
- Geography
- null