Datasets:

Rose-STL-Lab
/

Zephyrus

pretty_name: ZephyrusBench
license: mit
language:
  - en
task_categories:
  - question-answering
  - text-generation
tags:
  - weather
  - climate
  - earth-science
  - benchmark
  - agent
  - reasoning
size_categories:
  - 1K<n<10K

ZephyrusBench

ZephyrusBench is a weather-science benchmark released with the paper Zephyrus: An Agentic Framework for Weather Science. It contains 2,230 question-answer pairs across 49 tasks spanning geospatial reasoning, temporal reasoning, forecasting, simulation, climatology, and scientific question answering.

Accepted at the International Conference on Learning Representations, 2026.

Paper and Resources

Paper: arXiv
Poster: ICLR 2026 Poster
Code: Rose-STL-Lab/Zephyrus
Dataset: Rose-STL-Lab/Zephyrus on Hugging Face

Dataset Summary

Domain: weather science and climate-related reasoning
Language: English
Format: one or more JSON files
Primary use: benchmarking inference and evaluation pipelines in the Zephyrus codebase

The full Zephyrus stack pairs the benchmark with:

WeatherBench 2 data access
Natural Earth geolocation utilities
forecasting tools
simulation tools
climatology tools

Usage

Use With the Zephyrus Repository

Setup the Code Execution Server.
Download the dataset files from this Hugging Face repo.
Place them in a local directory.
Point dataset_path in configs/paths/default.yaml to that directory.
Run inference or evaluation from the Zephyrus repository.

For full setup instructions, including WeatherBench 2, Natural Earth, caches, and model/server configuration, see the project README.

Citation

If you use ZephyrusBench, please cite:

@inproceedings{
  varambally2026zephyrus,
  title={Zephyrus: An Agentic Framework for Weather Science},
  author={Sumanth Varambally and Marshall Fisher and Jas Thakker and Yiwei Chen and Zhirui Xia and Yasaman Jafari and Ruijia Niu and Manas Jain and Veeramakali Vignesh Manivannan and Zachary Novack and Luyu Han and Srikar Eranky and Salva R{\"u}hling Cachay and Taylor Berg-Kirkpatrick and Duncan Watson-Parris and Yian Ma and Rose Yu},
  booktitle={The Fourteenth International Conference on Learning Representations},
  year={2026},
  url={https://openreview.net/forum?id=aVeaNahsID}
}