WxC-Bench: A Novel Dataset for Weather and Climate Downstream Tasks

Area of Science:

• Meteorology and Climate Science
• Artificial Intelligence
• Data Science

Background:

• High-quality, accessible machine learning (ML)-ready datasets are crucial for advancing AI in scientific applications like weather and climate analysis.
• A significant gap exists in curated, pre-processed ML-ready datasets, hindering the development of novel deep learning models for atmospheric research.
• Data modality variations across different spatial and temporal scales pose challenges for creating generalizable AI models.

Purpose of the Study:

• Introduce WxC-Bench, a multi-modal dataset designed to facilitate the development of generalizable AI models for weather and climate research.
• Support the creation of AI models capable of analyzing diverse atmospheric processes across various scales.
• Provide a standardized resource for benchmarking and advancing AI applications in meteorology.

Main Methods:

• Developed WxC-Bench, a multi-modal dataset encompassing data relevant to atmospheric processes from meso-β to synoptic scales.
• Included diverse downstream use-cases such as turbulence detection, hurricane monitoring, weather analog identification, gravity wave parameterization, and natural language report generation.
• Performed technical validation with baseline analyses to demonstrate dataset utility.

Main Results:

• WxC-Bench offers a comprehensive, multi-modal dataset tailored for AI model development in weather and climate.
• The dataset covers a wide range of atmospheric phenomena and scales, enabling research on diverse applications.
• Baseline analyses confirm the dataset's suitability for evaluating and advancing AI models.

Conclusions:

• WxC-Bench addresses the critical need for curated, ML-ready datasets in weather and climate AI research.
• The dataset promotes the development of more generalizable and robust AI models for atmospheric science.
• Publicly available code and dataset on Hugging Face encourage community engagement and further research.