{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:MQEOV7KZMZG7RJM7N73DEBIG4V","short_pith_number":"pith:MQEOV7KZ","schema_version":"1.0","canonical_sha256":"6408eafd59664df8a59f6ff6320506e55ff954fc43302f5083c41628e33bbf90","source":{"kind":"arxiv","id":"2605.20925","version":1},"attestation_state":"computed","paper":{"title":"Blending machine learning and physics-based approaches for weather and climate: a typology","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.ao-ph","authors_text":"Ben B. B. Booth, Benjamin J Shipway, Caroline Bain, David Walters, Elizabeth Kendon, Ian Boutle, Katherine L. Hill, Robin T. Clark, Simon B. Vosper","submitted_at":"2026-05-20T09:10:45Z","abstract_excerpt":"The integration of machine learning (ML) with traditional physics-based models is reshaping the landscape of weather and climate prediction. On their own, ML-based and physics-based approaches each have significant benefits - but also challenges. Deploying both these approaches side by side has the potential to accelerate the pull through of emerging science in a trusted and practical way. But there are many choices that can be made to how we \"blend\" ML and established physics-based modelling systems to get the optimal benefits. This paper aims to provide a typology of blended modelling approa"},"verification_status":{"content_addressed":true,"pith_receipt":true,"author_attested":false,"weak_author_claims":0,"strong_author_claims":0,"externally_anchored":false,"storage_verified":false,"citation_signatures":0,"replication_records":0,"graph_snapshot":true,"references_resolved":false,"formal_links_present":false},"canonical_record":{"source":{"id":"2605.20925","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.ao-ph","submitted_at":"2026-05-20T09:10:45Z","cross_cats_sorted":[],"title_canon_sha256":"82d9bac7e454ea68ce652d114fd40c82125140dfd7a46f2a5e22801b5e371709","abstract_canon_sha256":"8f4de69b95a33c5ca8db71c5de2174ad0a038eaa50305d37bba6b168c958ea3f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-21T01:05:28.498104Z","signature_b64":"O6nxqZqFzyPncgKgW4tf0NnGbN+J1VyTHIX9uYsXdC/Ct3JCaU3tXir3yaVCF6b6bF6yeVeMe7oCjfHEhhqtAw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6408eafd59664df8a59f6ff6320506e55ff954fc43302f5083c41628e33bbf90","last_reissued_at":"2026-05-21T01:05:28.497355Z","signature_status":"signed_v1","first_computed_at":"2026-05-21T01:05:28.497355Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Blending machine learning and physics-based approaches for weather and climate: a typology","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.ao-ph","authors_text":"Ben B. B. Booth, Benjamin J Shipway, Caroline Bain, David Walters, Elizabeth Kendon, Ian Boutle, Katherine L. Hill, Robin T. Clark, Simon B. Vosper","submitted_at":"2026-05-20T09:10:45Z","abstract_excerpt":"The integration of machine learning (ML) with traditional physics-based models is reshaping the landscape of weather and climate prediction. On their own, ML-based and physics-based approaches each have significant benefits - but also challenges. Deploying both these approaches side by side has the potential to accelerate the pull through of emerging science in a trusted and practical way. But there are many choices that can be made to how we \"blend\" ML and established physics-based modelling systems to get the optimal benefits. This paper aims to provide a typology of blended modelling approa"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.20925","kind":"arxiv","version":1},"verdict":{"id":null,"model_set":{},"created_at":null,"strongest_claim":"","one_line_summary":"","pipeline_version":null,"weakest_assumption":"","pith_extraction_headline":""},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.20925/integrity.json","findings":[],"available":true,"detectors_run":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938"},"references":{"count":0,"sample":[],"resolved_work":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","internal_anchors":0},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"author_claims":{"count":0,"strong_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"builder_version":"pith-number-builder-2026-05-17-v1"},"aliases":[{"alias_kind":"arxiv","alias_value":"2605.20925","created_at":"2026-05-21T01:05:28.497484+00:00"},{"alias_kind":"arxiv_version","alias_value":"2605.20925v1","created_at":"2026-05-21T01:05:28.497484+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.20925","created_at":"2026-05-21T01:05:28.497484+00:00"},{"alias_kind":"pith_short_12","alias_value":"MQEOV7KZMZG7","created_at":"2026-05-21T01:05:28.497484+00:00"},{"alias_kind":"pith_short_16","alias_value":"MQEOV7KZMZG7RJM7","created_at":"2026-05-21T01:05:28.497484+00:00"},{"alias_kind":"pith_short_8","alias_value":"MQEOV7KZ","created_at":"2026-05-21T01:05:28.497484+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/MQEOV7KZMZG7RJM7N73DEBIG4V","json":"https://pith.science/pith/MQEOV7KZMZG7RJM7N73DEBIG4V.json","graph_json":"https://pith.science/api/pith-number/MQEOV7KZMZG7RJM7N73DEBIG4V/graph.json","events_json":"https://pith.science/api/pith-number/MQEOV7KZMZG7RJM7N73DEBIG4V/events.json","paper":"https://pith.science/paper/MQEOV7KZ"},"agent_actions":{"view_html":"https://pith.science/pith/MQEOV7KZMZG7RJM7N73DEBIG4V","download_json":"https://pith.science/pith/MQEOV7KZMZG7RJM7N73DEBIG4V.json","view_paper":"https://pith.science/paper/MQEOV7KZ","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2605.20925&json=true","fetch_graph":"https://pith.science/api/pith-number/MQEOV7KZMZG7RJM7N73DEBIG4V/graph.json","fetch_events":"https://pith.science/api/pith-number/MQEOV7KZMZG7RJM7N73DEBIG4V/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/MQEOV7KZMZG7RJM7N73DEBIG4V/action/timestamp_anchor","attest_storage":"https://pith.science/pith/MQEOV7KZMZG7RJM7N73DEBIG4V/action/storage_attestation","attest_author":"https://pith.science/pith/MQEOV7KZMZG7RJM7N73DEBIG4V/action/author_attestation","sign_citation":"https://pith.science/pith/MQEOV7KZMZG7RJM7N73DEBIG4V/action/citation_signature","submit_replication":"https://pith.science/pith/MQEOV7KZMZG7RJM7N73DEBIG4V/action/replication_record"}},"created_at":"2026-05-21T01:05:28.497484+00:00","updated_at":"2026-05-21T01:05:28.497484+00:00"}