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license: cc-by-nc-4.0
language:
- en
tags:
- climate
- synthetic
size_categories:
- 100B<n<1T
---
<div align="center">
# EarthAlign: Learning Language-Aligned Representations of Earth System Data
**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)
[](https://mbzuai-oryx.github.io/EarthAlign/)
[](https://arxiv.org/abs/2610.14624)
[](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},
}
```
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