ESSD Benchmark: Postprocessing Output Data for Weather Forecasts

ESSD benchmark output data provides results from multiple statistical and machine learning postprocessing methods applied to ensemble weather forecasts. The dataset is supplementary material for a 2023 Earth System Science Data discussion paper authored by Demaeyer et al. and is associated with the EUPPBench project.

Use Cases

• Benchmarking postprocessing algorithm performance based on the described methods like ANET and EMOS.
• Comparing neural network and statistical calibration techniques for ensemble forecasts.
• Evaluating methods for accounting for systematic errors in weather prediction models.

Strengths

• Dataset is linked to a peer-reviewed scientific discussion paper (ESSD, 2023).
• Includes outputs from eight distinct postprocessing methods, enabling comparative analysis.

Limitations

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

Provenance

Source

EUPP-benchmark project, associated with the Karlsruhe Institute of Technology.

Collection Method

Outputs generated by applying postprocessing methods (e.g., ANET, EMOS, DRN) to ensemble weather forecast data.