{"paper":{"title":"Toward a Scientific Discovery Engine for Weather and Climate Data: A Visual Analytics Workbench for Embedding-Based Exploration","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A visual analytics workbench links embedding spaces of weather data back to physical observations so researchers can identify and retrieve similar meteorological events across large archives.","cross_cats":["cs.AI","cs.CV","cs.IR"],"primary_cat":"physics.data-an","authors_text":"Charlie Becker, David John Gagne, John Clyne, John Schreck, Kirsten J. Mayer, Matt Rehme, Nihanth W. Cherukuru","submitted_at":"2026-05-01T17:03:33Z","abstract_excerpt":"Earth system science is producing increasingly large, high-dimensional datasets from both physics-based and AI-driven models. While embedding-based representations make these data searchable and serve as foundational building blocks for AI-driven discovery engines, nearest neighbors in latent spaces are not automatically scientifically meaningful. They may reflect real meteorological structures, or simply artifacts of preprocessing, geography, or model bias. Researchers therefore need visual tools to inspect latent space organization, trace search results back to physical evidence, and evaluat"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"This enables a discovery workflow in which scientists characterize a phenomenon of interest in a well-understood dataset, identifying its signature in latent space, and then use that signature to probe larger, less-labeled archives or ensembles for similar events.","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"That visual inspection and metadata linking will reliably allow domain scientists to distinguish physically meaningful structures in the embedding space from preprocessing or bias artifacts without additional quantitative validation.","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"A visual analytics workbench enables scientists to explore, query, and verify embedding-based similarity searches on weather and climate data by tracing results back to physical evidence.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A visual analytics workbench links embedding spaces of weather data back to physical observations so researchers can identify and retrieve similar meteorological events across large archives.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"d8de981a1c6f2fadf58cf69204f28d802e7db085883f270b88777810ba9c39ed"},"source":{"id":"2605.00972","kind":"arxiv","version":2},"verdict":{"id":"9f57abc2-7644-440c-bbd0-0db6a270fa38","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-09T14:52:20.303610Z","strongest_claim":"This enables a discovery workflow in which scientists characterize a phenomenon of interest in a well-understood dataset, identifying its signature in latent space, and then use that signature to probe larger, less-labeled archives or ensembles for similar events.","one_line_summary":"A visual analytics workbench enables scientists to explore, query, and verify embedding-based similarity searches on weather and climate data by tracing results back to physical evidence.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"That visual inspection and metadata linking will reliably allow domain scientists to distinguish physically meaningful structures in the embedding space from preprocessing or bias artifacts without additional quantitative validation.","pith_extraction_headline":"A visual analytics workbench links embedding spaces of weather data back to physical observations so researchers can identify and retrieve similar meteorological events across large archives."},"integrity":{"clean":false,"summary":{"advisory":2,"critical":0,"by_detector":{"doi_compliance":{"total":2,"advisory":2,"critical":0,"informational":0}},"informational":0},"endpoint":"/pith/2605.00972/integrity.json","findings":[{"note":"DOI in the printed bibliography is fragmented by whitespace or line breaks. 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