| --- |
| license: cc-by-4.0 |
| task_categories: |
| - time-series-forecasting |
| tags: |
| - climate |
| - weather |
| - atmospheric-science |
| - geospatial |
| - timeseries |
| - simulation |
| pretty_name: STRATA-SCREAM (sdecadal) |
| --- |
| ## Dataset Description: |
| Global, storm-resolving atmospheric simulation output generated by the SCREAM (Simple Cloud-Resolving E3SM Atmosphere Model) physics model. The dataset provides high-resolution global atmospheric state fields suitable for training and evaluating machine-learning weather and climate emulators. The data was produced by an external collaborator and is publicly available at NERSC under CC-BY-4.0. This release is a reformatted, ML-ready version: files have been converted from NetCDF (.nc) to Zarr (.zarr) and subsampled. No underlying data values were altered. This dataset is ready for commercial and non-commercial use. This repository contains the [sdecadal] simulation store. It is part of a three-part release; the other simulation periods are available in the companion repositories (STRATA-SCREAM-sdy1, STRATA-SCREAM-sdy2). |
| ## Dataset Owner(s): |
| E3SM Project (U.S. Department of Energy) |
| ## Dataset Creation Date: |
| 2025-09-22 |
| ## Version: |
| v1.0 |
| Previous Version(s): Original raw SCREAM simulation output data in .nc format: [https://portal.nersc.gov/archive/home/n/ndk/www/STRATA2026](https://portal.nersc.gov/archive/home/n/ndk/www/STRATA2026) |
| ## License/Terms of Use: |
| Creative Commons Attribution 4.0 International (CC-BY-4.0) |
| ## Intended Usage: |
| Training and evaluating machine-learning weather and climate emulators using high-resolution global atmospheric simulation data. |
| ## Dataset Characterization |
| **Data Collection Method** |
| Synthetic |
| **Labeling Method** |
| Not Applicable |
| ## Dataset Format |
| Modality: geospatial, timeseries |
| Format: Zarr (.zarr) |
| ## Dataset Quantification |
| Record Count: 2,017 timesteps (10-minute temporal resolution) |
| Spatial Grid: cubed-sphere ne1024pg2, 25,165,824 columns (~4.9 km), 32 vertical levels |
| Feature Count: 29 variables |
| Simulation: F20TR-SCREAMv1 |
| ## Ethical Considerations: |
| NVIDIA believes Trustworthy AI is a shared responsibility and we have established policies and practices to enable development for a wide array of AI applications. Developers should work with their internal developer teams to ensure this dataset meets requirements for the relevant industry and use case and addresses unforeseen product misuse. |
| Please report quality, risk, security vulnerabilities or NVIDIA AI Concerns [here](https://www.nvidia.com/en-us/support/submit-security-vulnerability/). |
