Climate data management is undergoing a fundamental transformation as research institutions move from metadata-based catalogues toward similarity-based retrieval systems. Instead of requiring researchers to specify exactly what they are looking for, organisations are deploying vector databases that answer the question driving scientific discovery: "show me climate states similar to this example." This shift introduces major changes to data infrastructure, ensemble analysis, and analogue identification workflows, while requiring new capabilities in embedding generation, approximate nearest neighbour indexing, and hybrid search integration.
This white paper explores how vector databases transform petabyte-scale climate archives from passive storage into active research accelerators, the technical frameworks required for production deployment, and the operational implications for uncertainty quantification and climate projection. It also outlines how CBS Group applies systems engineering, Technical Assurance, and data science methodologies to help clients unlock hidden value from existing climate data infrastructure. Download the full white paper for a deep dive into similarity-based retrieval and its role in accelerating climate science, or visit the website for case studies and further research https://vectorsdb4climate.cbslab.app
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