Deep Convective Cloud Air Parcels from Idealized Simulation

Roman Bardakov from Stockholm University provides a dataset of 500 files describing idealized deep convective cloud air parcels. The data includes values for coordinate, velocity components, thermodynamic variables, and turbulence metrics for each parcel at every simulation time step. The dataset is listed as Open Access on the paperswithcode platform.

Use Cases

• Analyze the evolution of buoyancy and vertical velocity in convective parcels based on the BUOY and W columns.
• Study microphysical processes in clouds based on water content variables like QT, QV, QC, and QR.
• Investigate turbulence and mixing in convective clouds based on resolved and subgrid-scale TKE (KRES, SGS_TKE).
• Validate or develop parameterizations for cloud models using the comprehensive set of thermodynamic variables like potential temperature (PTIL), pressure (P), and temperature (T).

Strengths

• Contains data for 500 distinct simulation files, suggesting a substantial collection of model runs.
• Provides a comprehensive set of 15+ physical variables per parcel per time step, including dynamics, thermodynamics, and microphysics.

Limitations

• Row count and total data size are unknown, which may limit suitability assessment for large-scale analysis.
• Column-level documentation is absent; field semantics must be inferred from the description after download.
• Last update date is unknown; freshness unverified.

Provenance

Source

Roman Bardakov, Stockholm University, via paperswithcode.

Collection Method

Output from an idealized numerical simulation of deep convective cloud air parcels.