{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:ZXUHLWRIIZAI5UX62TUHRS4FRE","short_pith_number":"pith:ZXUHLWRI","schema_version":"1.0","canonical_sha256":"cde875da2846408ed2fed4e878cb858905680f4306089fb467d063cc150b062b","source":{"kind":"arxiv","id":"2407.15586","version":1},"attestation_state":"computed","paper":{"title":"Data driven weather forecasts trained and initialised directly from observations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"physics.ao-ph","authors_text":"Anthony McNally, Chris Burrows, Christian Lessig, Ethel Villeneuve, Eulalie Boucher, Ewan Pinnington, Florian Pinault, Marcin Chrust, Matthew Chantry, Mihai Alexe, Niels Bormann, Peter Lean, Sean Healy, Simon Lang","submitted_at":"2024-07-22T12:23:26Z","abstract_excerpt":"Skilful Machine Learned weather forecasts have challenged our approach to numerical weather prediction, demonstrating competitive performance compared to traditional physics-based approaches. Data-driven systems have been trained to forecast future weather by learning from long historical records of past weather such as the ECMWF ERA5. These datasets have been made freely available to the wider research community, including the commercial sector, which has been a major factor in the rapid rise of ML forecast systems and the levels of accuracy they have achieved. However, historical reanalyses "},"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":"2407.15586","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.ao-ph","submitted_at":"2024-07-22T12:23:26Z","cross_cats_sorted":["cs.LG"],"title_canon_sha256":"b5d83ffc1869c15cef7ca51d8d98eec0aeccc9edf19728025d774649e8e19920","abstract_canon_sha256":"ab55cedd48fffea92fbd2dd782355b563ca33cb3a3817f78fd360734befb5317"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T08:46:58.338456Z","signature_b64":"RmqHtMe/3IwkamfoSIZx4wVlWkENhisMAbm47Po3zL/Rybl/6ADqwYftgoRXsPh+vq9QFLKM8KtgTbfl5WWiDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"cde875da2846408ed2fed4e878cb858905680f4306089fb467d063cc150b062b","last_reissued_at":"2026-07-05T08:46:58.336111Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T08:46:58.336111Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Data driven weather forecasts trained and initialised directly from observations","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.LG"],"primary_cat":"physics.ao-ph","authors_text":"Anthony McNally, Chris Burrows, Christian Lessig, Ethel Villeneuve, Eulalie Boucher, Ewan Pinnington, Florian Pinault, Marcin Chrust, Matthew Chantry, Mihai Alexe, Niels Bormann, Peter Lean, Sean Healy, Simon Lang","submitted_at":"2024-07-22T12:23:26Z","abstract_excerpt":"Skilful Machine Learned weather forecasts have challenged our approach to numerical weather prediction, demonstrating competitive performance compared to traditional physics-based approaches. Data-driven systems have been trained to forecast future weather by learning from long historical records of past weather such as the ECMWF ERA5. These datasets have been made freely available to the wider research community, including the commercial sector, which has been a major factor in the rapid rise of ML forecast systems and the levels of accuracy they have achieved. However, historical reanalyses "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2407.15586","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/2407.15586/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":"2407.15586","created_at":"2026-07-05T08:46:58.337854+00:00"},{"alias_kind":"arxiv_version","alias_value":"2407.15586v1","created_at":"2026-07-05T08:46:58.337854+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2407.15586","created_at":"2026-07-05T08:46:58.337854+00:00"},{"alias_kind":"pith_short_12","alias_value":"ZXUHLWRIIZAI","created_at":"2026-07-05T08:46:58.337854+00:00"},{"alias_kind":"pith_short_16","alias_value":"ZXUHLWRIIZAI5UX6","created_at":"2026-07-05T08:46:58.337854+00:00"},{"alias_kind":"pith_short_8","alias_value":"ZXUHLWRI","created_at":"2026-07-05T08:46:58.337854+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":7,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2607.07879","citing_title":"Global reanalysis from observations alone with machine learning","ref_index":26,"is_internal_anchor":true},{"citing_arxiv_id":"2606.25076","citing_title":"Machine learning is revolutionizing weather forecasting -- the next step is a change in how we work","ref_index":14,"is_internal_anchor":false},{"citing_arxiv_id":"2605.28153","citing_title":"Skillful high-resolution weather forecasting independent of physical models","ref_index":22,"is_internal_anchor":false},{"citing_arxiv_id":"2606.19093","citing_title":"AIFS-DOP: End-to-End Medium-Range Weather Prediction from Observations Alone with Machine Learning","ref_index":16,"is_internal_anchor":false},{"citing_arxiv_id":"2601.17636","citing_title":"HealDA: Highlighting the importance of initial errors in end-to-end AI weather forecasts","ref_index":29,"is_internal_anchor":false},{"citing_arxiv_id":"2604.03289","citing_title":"Toward Artificial Intelligence Enabled Earth System Coupling","ref_index":78,"is_internal_anchor":false},{"citing_arxiv_id":"2605.06337","citing_title":"Earth-o1: A Grid-free Observation-native Atmospheric World Model","ref_index":13,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/ZXUHLWRIIZAI5UX62TUHRS4FRE","json":"https://pith.science/pith/ZXUHLWRIIZAI5UX62TUHRS4FRE.json","graph_json":"https://pith.science/api/pith-number/ZXUHLWRIIZAI5UX62TUHRS4FRE/graph.json","events_json":"https://pith.science/api/pith-number/ZXUHLWRIIZAI5UX62TUHRS4FRE/events.json","paper":"https://pith.science/paper/ZXUHLWRI"},"agent_actions":{"view_html":"https://pith.science/pith/ZXUHLWRIIZAI5UX62TUHRS4FRE","download_json":"https://pith.science/pith/ZXUHLWRIIZAI5UX62TUHRS4FRE.json","view_paper":"https://pith.science/paper/ZXUHLWRI","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2407.15586&json=true","fetch_graph":"https://pith.science/api/pith-number/ZXUHLWRIIZAI5UX62TUHRS4FRE/graph.json","fetch_events":"https://pith.science/api/pith-number/ZXUHLWRIIZAI5UX62TUHRS4FRE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ZXUHLWRIIZAI5UX62TUHRS4FRE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ZXUHLWRIIZAI5UX62TUHRS4FRE/action/storage_attestation","attest_author":"https://pith.science/pith/ZXUHLWRIIZAI5UX62TUHRS4FRE/action/author_attestation","sign_citation":"https://pith.science/pith/ZXUHLWRIIZAI5UX62TUHRS4FRE/action/citation_signature","submit_replication":"https://pith.science/pith/ZXUHLWRIIZAI5UX62TUHRS4FRE/action/replication_record"}},"created_at":"2026-07-05T08:46:58.337854+00:00","updated_at":"2026-07-05T08:46:58.337854+00:00"}