{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2023:QCXC4YP5BHAR66TTM5GTCXA4TD","short_pith_number":"pith:QCXC4YP5","schema_version":"1.0","canonical_sha256":"80ae2e61fd09c11f7a73674d315c1c98c0b3e5ee84fdc7ed9389b868f370620b","source":{"kind":"arxiv","id":"2312.02819","version":1},"attestation_state":"computed","paper":{"title":"Deterministic Guidance Diffusion Model for Probabilistic Weather Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Donggeun Yoon, Donghyeon Cho, Doyi Kim, Minseok Seo, Yeji Choi","submitted_at":"2023-12-05T15:03:15Z","abstract_excerpt":"Weather forecasting requires not only accuracy but also the ability to perform probabilistic prediction. However, deterministic weather forecasting methods do not support probabilistic predictions, and conversely, probabilistic models tend to be less accurate. To address these challenges, in this paper, we introduce the \\textbf{\\textit{D}}eterministic \\textbf{\\textit{G}}uidance \\textbf{\\textit{D}}iffusion \\textbf{\\textit{M}}odel (DGDM) for probabilistic weather forecasting, integrating benefits of both deterministic and probabilistic approaches. During the forward process, both the determinist"},"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":"2312.02819","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.CV","submitted_at":"2023-12-05T15:03:15Z","cross_cats_sorted":[],"title_canon_sha256":"da93e068953b34bf10e7a522695d9e391a53578e2f84a877b6ba46aa463a9f33","abstract_canon_sha256":"8c22fed1e99eaaf49305f859984258beb720dece373e3e0c5c01e4614661f599"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:20:43.361000Z","signature_b64":"uOnEZ3ThNpi9tdCZ8bhIdee6QjtWZSs1GNC662gwAEnjvJG6Fp5TSyTM6/1UP+aQXvy9dvm7budfbdHWwSKLDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"80ae2e61fd09c11f7a73674d315c1c98c0b3e5ee84fdc7ed9389b868f370620b","last_reissued_at":"2026-07-05T07:20:43.360644Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:20:43.360644Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Deterministic Guidance Diffusion Model for Probabilistic Weather Forecasting","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"cs.CV","authors_text":"Donggeun Yoon, Donghyeon Cho, Doyi Kim, Minseok Seo, Yeji Choi","submitted_at":"2023-12-05T15:03:15Z","abstract_excerpt":"Weather forecasting requires not only accuracy but also the ability to perform probabilistic prediction. However, deterministic weather forecasting methods do not support probabilistic predictions, and conversely, probabilistic models tend to be less accurate. To address these challenges, in this paper, we introduce the \\textbf{\\textit{D}}eterministic \\textbf{\\textit{G}}uidance \\textbf{\\textit{D}}iffusion \\textbf{\\textit{M}}odel (DGDM) for probabilistic weather forecasting, integrating benefits of both deterministic and probabilistic approaches. During the forward process, both the determinist"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2312.02819","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/2312.02819/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":"2312.02819","created_at":"2026-07-05T07:20:43.360698+00:00"},{"alias_kind":"arxiv_version","alias_value":"2312.02819v1","created_at":"2026-07-05T07:20:43.360698+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2312.02819","created_at":"2026-07-05T07:20:43.360698+00:00"},{"alias_kind":"pith_short_12","alias_value":"QCXC4YP5BHAR","created_at":"2026-07-05T07:20:43.360698+00:00"},{"alias_kind":"pith_short_16","alias_value":"QCXC4YP5BHAR66TT","created_at":"2026-07-05T07:20:43.360698+00:00"},{"alias_kind":"pith_short_8","alias_value":"QCXC4YP5","created_at":"2026-07-05T07:20:43.360698+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.02661","citing_title":"Learning to Refine: Spectral-Decoupled Iterative Refinement Framework for Precipitation Nowcasting","ref_index":70,"is_internal_anchor":false},{"citing_arxiv_id":"2512.16768","citing_title":"On The Hidden Biases of Flow Matching Samplers","ref_index":46,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/QCXC4YP5BHAR66TTM5GTCXA4TD","json":"https://pith.science/pith/QCXC4YP5BHAR66TTM5GTCXA4TD.json","graph_json":"https://pith.science/api/pith-number/QCXC4YP5BHAR66TTM5GTCXA4TD/graph.json","events_json":"https://pith.science/api/pith-number/QCXC4YP5BHAR66TTM5GTCXA4TD/events.json","paper":"https://pith.science/paper/QCXC4YP5"},"agent_actions":{"view_html":"https://pith.science/pith/QCXC4YP5BHAR66TTM5GTCXA4TD","download_json":"https://pith.science/pith/QCXC4YP5BHAR66TTM5GTCXA4TD.json","view_paper":"https://pith.science/paper/QCXC4YP5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2312.02819&json=true","fetch_graph":"https://pith.science/api/pith-number/QCXC4YP5BHAR66TTM5GTCXA4TD/graph.json","fetch_events":"https://pith.science/api/pith-number/QCXC4YP5BHAR66TTM5GTCXA4TD/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/QCXC4YP5BHAR66TTM5GTCXA4TD/action/timestamp_anchor","attest_storage":"https://pith.science/pith/QCXC4YP5BHAR66TTM5GTCXA4TD/action/storage_attestation","attest_author":"https://pith.science/pith/QCXC4YP5BHAR66TTM5GTCXA4TD/action/author_attestation","sign_citation":"https://pith.science/pith/QCXC4YP5BHAR66TTM5GTCXA4TD/action/citation_signature","submit_replication":"https://pith.science/pith/QCXC4YP5BHAR66TTM5GTCXA4TD/action/replication_record"}},"created_at":"2026-07-05T07:20:43.360698+00:00","updated_at":"2026-07-05T07:20:43.360698+00:00"}