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EarthAlign: Learning Language-Aligned Representations of Earth System Data

arXiv · · Significant research

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Summary

Researchers introduced EarthAlign, a large-scale dataset spanning 2000-2024 that combines expert-curated natural event and disaster data with normal-weather samples, aligned with 63-variable ERA5 meteorological data. Building on this dataset, they developed MeteoLIP, a dual-encoder framework designed to learn aligned meteorology-language representations through its MetTok component. MeteoLIP was evaluated on cross-modal retrieval, weather-condition classification, and physical variable estimation tasks, demonstrating improved performance over CLIP and SigLIP baselines. Why it matters: This work from MBZUAI provides a foundational approach and publicly available resources for interpreting Earth system conditions through language, which can significantly enhance environmental risk assessment and climate-sensitive decision-making in the region and globally.

Keywords

EarthAlign · MeteoLIP · Meteorology · Language Alignment · MBZUAI

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