{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:EHP222EIHCDNIQAFIFC22TEIWB","short_pith_number":"pith:EHP222EI","schema_version":"1.0","canonical_sha256":"21dfad68883886d440054145ad4c88b07c61589a6f02e8380545d40eaf9ae1b5","source":{"kind":"arxiv","id":"2306.11869","version":1},"attestation_state":"computed","paper":{"title":"The Conditioning of Hybrid Variational Data Assimilation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"Amos S. Lawless, Nancy K. Nichols, Shaerdan Shataer","submitted_at":"2023-06-20T20:02:53Z","abstract_excerpt":"In variational assimilation, the most probable state of a dynamical system under Gaussian assumptions for the prior and likelihood can be found by solving a least-squares minimization problem . In recent years, we have seen the popularity of hybrid variational data assimilation methods for Numerical Weather Prediction. In these methods, the prior error covariance matrix is a weighted sum of a climatological part and a flow-dependent ensemble part, the latter being rank deficient. The nonlinear least squares problem of variational data assimilation is solved using iterative numerical methods, a"},"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":"2306.11869","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"math.NA","submitted_at":"2023-06-20T20:02:53Z","cross_cats_sorted":["cs.NA"],"title_canon_sha256":"f063bbe10817cf81c561d7804d94239eadf57210665772d8c41a3be7d8675d5a","abstract_canon_sha256":"dfd4ed17d48c1c09969984f1061064b66ab27c9e1c5ff65e941fa6f44af077d9"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T06:23:07.942642Z","signature_b64":"dLA3MmknzRmAdPxro23HXvRpbrS6putkJU7WVqGM49LdtfWqhqqBb23AxfMyIo1YWDRTej+9FSEpHZZVrhzjBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"21dfad68883886d440054145ad4c88b07c61589a6f02e8380545d40eaf9ae1b5","last_reissued_at":"2026-07-05T06:23:07.942175Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T06:23:07.942175Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"The Conditioning of Hybrid Variational Data Assimilation","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.NA"],"primary_cat":"math.NA","authors_text":"Amos S. Lawless, Nancy K. Nichols, Shaerdan Shataer","submitted_at":"2023-06-20T20:02:53Z","abstract_excerpt":"In variational assimilation, the most probable state of a dynamical system under Gaussian assumptions for the prior and likelihood can be found by solving a least-squares minimization problem . In recent years, we have seen the popularity of hybrid variational data assimilation methods for Numerical Weather Prediction. In these methods, the prior error covariance matrix is a weighted sum of a climatological part and a flow-dependent ensemble part, the latter being rank deficient. The nonlinear least squares problem of variational data assimilation is solved using iterative numerical methods, a"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2306.11869","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/2306.11869/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":"2306.11869","created_at":"2026-07-05T06:23:07.942229+00:00"},{"alias_kind":"arxiv_version","alias_value":"2306.11869v1","created_at":"2026-07-05T06:23:07.942229+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2306.11869","created_at":"2026-07-05T06:23:07.942229+00:00"},{"alias_kind":"pith_short_12","alias_value":"EHP222EIHCDN","created_at":"2026-07-05T06:23:07.942229+00:00"},{"alias_kind":"pith_short_16","alias_value":"EHP222EIHCDNIQAF","created_at":"2026-07-05T06:23:07.942229+00:00"},{"alias_kind":"pith_short_8","alias_value":"EHP222EI","created_at":"2026-07-05T06:23:07.942229+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/EHP222EIHCDNIQAFIFC22TEIWB","json":"https://pith.science/pith/EHP222EIHCDNIQAFIFC22TEIWB.json","graph_json":"https://pith.science/api/pith-number/EHP222EIHCDNIQAFIFC22TEIWB/graph.json","events_json":"https://pith.science/api/pith-number/EHP222EIHCDNIQAFIFC22TEIWB/events.json","paper":"https://pith.science/paper/EHP222EI"},"agent_actions":{"view_html":"https://pith.science/pith/EHP222EIHCDNIQAFIFC22TEIWB","download_json":"https://pith.science/pith/EHP222EIHCDNIQAFIFC22TEIWB.json","view_paper":"https://pith.science/paper/EHP222EI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2306.11869&json=true","fetch_graph":"https://pith.science/api/pith-number/EHP222EIHCDNIQAFIFC22TEIWB/graph.json","fetch_events":"https://pith.science/api/pith-number/EHP222EIHCDNIQAFIFC22TEIWB/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/EHP222EIHCDNIQAFIFC22TEIWB/action/timestamp_anchor","attest_storage":"https://pith.science/pith/EHP222EIHCDNIQAFIFC22TEIWB/action/storage_attestation","attest_author":"https://pith.science/pith/EHP222EIHCDNIQAFIFC22TEIWB/action/author_attestation","sign_citation":"https://pith.science/pith/EHP222EIHCDNIQAFIFC22TEIWB/action/citation_signature","submit_replication":"https://pith.science/pith/EHP222EIHCDNIQAFIFC22TEIWB/action/replication_record"}},"created_at":"2026-07-05T06:23:07.942229+00:00","updated_at":"2026-07-05T06:23:07.942229+00:00"}