RaFSIP: Weather Model Data for Secondary Ice Production Parameterization

Processed data from Weather Research and Forecasting (WRF) model simulations presented in the paper 'RaFSIP: Parameterizing ice multiplication in models using a machine learning approach'. The repository contains microphysics routines, scripts, and processed data by Paraskevi Georgakaki and Athanasios Nenes from École Polytechnique Fédérale de Lausanne. A preprint is available on Authorea.

Use Cases

• Training machine learning models for secondary ice production based on WRF simulation outputs.
• Benchmarking new microphysics parameterization schemes against the RaFSIP data.
• Analyzing cloud ice processes using the provided processed model data.
• Developing streamlined SIP representations for large-scale climate models.

Strengths

• Data is associated with a peer-reviewed scientific paper and preprint, providing academic context.
• Includes both processed data and the scripts used for analysis, which may aid reproducibility.

Limitations

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

Provenance

Source

Weather Research and Forecasting (WRF) model simulations from the RaFSIP study.

Collection Method

Data processed from numerical weather model outputs.