This repository provides a comprehensive survey of large foundation models and task-specific models for weather and climate data understanding. It targets researchers and practitioners in meteorology, climate science, and AI, offering a curated list of resources, insights into model architectures, and future research directions to advance data-driven weather and climate analysis.

How It Works

The survey categorizes models based on their architectures (e.g., Transformers, Graph Neural Networks, Diffusion Models, GANs) and the types of data they handle (time-series, spatio-temporal, text). It systematically reviews recent advances, highlighting key papers, code implementations, and datasets, aiming to provide a structured overview of the rapidly evolving field of AI in weather and climate.

Quick Start & Requirements

This is a survey repository, not a software package. No installation or specific requirements are needed to view the content. Links to papers and code are provided for individual models.

Highlighted Details

• Comprehensive coverage of various data types including time series, textual data, and spatio-temporal series.
• Up-to-date resources with links to the latest papers, code implementations, and datasets.
• Practical insights into challenges, opportunities, and future research directions in AI for weather and climate.
• Community-driven with an open invitation for contributions and collaborations.

Maintenance & Community

The repository is actively maintained, with recent updates noted for January 2025. It welcomes contributions and provides contact information for collaboration opportunities. The survey paper itself has been accepted by NeurIPS 2024.

Licensing & Compatibility

The repository itself does not have a specific license as it is a curated list of resources. Individual models linked within the survey will have their own licenses, which users must consult.

Limitations & Caveats

As a survey, the repository's content is limited by the knowledge cutoff date of its last update. While comprehensive, it may not include the absolute latest research published after its last update.