---
language:
- en
license: cc-by-nc-4.0
size_categories:
- 100B<n<1T
tags:
- climate
- synthetic
task_categories:
- zero-shot-image-classification
---

<div align="center">

# EarthAlign: Learning Language-Aligned Representations of Earth System Data

![EarthAlign](https://i.imgur.com/waxVImv.png)

**Fadillah Adamsyah Maani<sup>1</sup>, Numan Saeed<sup>1</sup>, Jiaqi Ma<sup>1</sup>, Dat Tien Nguyen<sup>1</sup>, Youssef Wehbe<sup>2</sup>, Duncan Watson-Parris<sup>3</sup>, Salman Khan<sup>1,2</sup>**

<sup>1</sup>Mohamed bin Zayed University of Artificial Intelligence (MBZUAI), <sup>2</sup>ADIA Lab, <sup>3</sup>University of California, San Diego (UC San Diego)

[![Webpage](https://img.shields.io/badge/🌐_Project-Webpage-4285F4?logoColor=white)](https://mbzuai-oryx.github.io/EarthAlign/)
[![Paper](https://img.shields.io/badge/arXiv-Paper-B31B1B?logo=arxiv&logoColor=white)](https://arxiv.org/abs/2610.14624)
[![GitHub](https://img.shields.io/badge/GitHub-Code-181717?logo=github&logoColor=white)](https://github.com/mbzuai-oryx/EarthAlign)

</div>

---

## 🌍 Overview

**EarthAlign** is a large-scale Earth system-language alignment dataset and benchmark for learning and evaluating multimodal representations of Earth system data. It contains **553,738 samples** spanning **2000-2024**, with broad geographic coverage and diverse natural phenomena and normal weather conditions. Each sample pairs temporal and geospatial information and multivariate ERA5 data with four metadata-grounded caption styles (natural-event-focused, impact-focused, physical framing, and atmospheric pattern) that describe the same Earth system condition from different perspectives.

Building on EarthAlign, **MeteoLIP** is a CLIP/SigLIP-based dual-encoder framework for aligning meteorological data with natural language. Its meteorology tokenizer, **MetTok**, aggregates variable-specific representations into compact meteorology tokens while incorporating temporal and geospatial information. MeteoLIP is evaluated on cross-modal retrieval, weather-condition classification, and physical variable estimation.

**This Hugging Face repository hosts the EarthAlign dataset and the pretrained MeteoLIP weights.** The code for preprocessing, training, evaluation, and linear probing is available in the [GitHub repository](https://github.com/mbzuai-oryx/EarthAlign).

---

## 📦 Repository Contents

```
MBZUAI/EarthAlign
├── EarthAlign/                        # EarthAlign dataset (~241 GB)
│   ├── webdatasets_train/             # Training data in WebDataset format (.tar shards, ~212 GB)
│   ├── data_era5_processed/           # Test/evaluation data (~27.6 GB, tar archives to extract)
│   │   ├── npz/                       #   Preprocessed ERA5 samples (.npz)
│   │   ├── json/                      #   Multi-style captions (.json)
│   │   └── info.json
│   └── data/
│       └── record_json/               # Full dataset records: per-source natural event and
│                                      #   weather-normal (negsamp) samples with captions and metadata
└── MeteoLIP/                          # Pretrained MeteoLIP weights (~17.2 GB)
    ├── Meteo-CLIP/
    │   ├── ViT-B-4_Full-MetTok/
    │   └── ViT-L-4_Full-MetTok/
    └── Meteo-SigLIP/
        ├── ViT-B-4_Full-MetTok/
        └── ViT-L-4_Full-MetTok/
```

### MeteoLIP Weights

| Model              | Meteorology Encoder | MetTok Configuration | Weights |
| ------------------ | ------------------- | -------------------- | ------- |
| Meteo-CLIP ViT-B   | ViT-B/4             | Full MetTok          | [Link](https://huggingface.co/datasets/MBZUAI/EarthAlign/tree/main/MeteoLIP/Meteo-CLIP/ViT-B-4_Full-MetTok) |
| Meteo-CLIP ViT-L   | ViT-L/4             | Full MetTok          | [Link](https://huggingface.co/datasets/MBZUAI/EarthAlign/tree/main/MeteoLIP/Meteo-CLIP/ViT-L-4_Full-MetTok) |
| Meteo-SigLIP ViT-B | ViT-B/4             | Full MetTok          | [Link](https://huggingface.co/datasets/MBZUAI/EarthAlign/tree/main/MeteoLIP/Meteo-SigLIP/ViT-B-4_Full-MetTok) |
| Meteo-SigLIP ViT-L | ViT-L/4             | Full MetTok          | [Link](https://huggingface.co/datasets/MBZUAI/EarthAlign/tree/main/MeteoLIP/Meteo-SigLIP/ViT-L-4_Full-MetTok) |

---

## ⚖️ License

This repository is released under [CC BY-NC 4.0](https://creativecommons.org/licenses/by-nc/4.0/). EarthAlign is built on ERA5 reanalysis data and natural event records from multiple public sources, each with its own license or terms of use. Please refer to the [natural event record sources](https://github.com/mbzuai-oryx/EarthAlign#natural-event-record-sources) for details.

---

## 🙏 Acknowledgement

This material is based on work supported by ADIA Lab, Abu Dhabi under a collaborative research project titled Climate Co-Pilot: A Multimodal AI Agent for Climate Resilience in the MENA Region.

Our codebase is built upon the [OpenCLIP](https://github.com/mlfoundations/open_clip) repository, and we thank sincerely its developers and contributors for making their work publicly available.

---

## 📜 Citation

If you find EarthAlign or MeteoLIP useful in your research, please consider citing:

```bibtex
@misc{maani2026earthalignlearninglanguagealignedrepresentations,
      title={EarthAlign: Learning Language-Aligned Representations of Earth System Data}, 
      author={Fadillah Adamsyah Maani and Numan Saeed and Jiaqi Ma and Dat Tien Nguyen and Youssef Wehbe and Duncan Watson-Parris and Salman Khan},
      year={2026},
      eprint={2610.14624},
      archivePrefix={arXiv},
      primaryClass={cs.CV},
      url={https://arxiv.org/abs/2610.14624}, 
}
```