{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2024:XTUV6PWJOCFYDOTM3TKEBZWSSJ","short_pith_number":"pith:XTUV6PWJ","canonical_record":{"source":{"id":"2402.06666","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.ao-ph","submitted_at":"2024-02-06T21:28:42Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"5ecae6d9a2eebb0a3e50c99ee5edfde5194700af5485faace3122338ccabbba1","abstract_canon_sha256":"81a8d08e561912befedc37ce63e557bd1b9b32bce027b82639ec9adfaa70ec20"},"schema_version":"1.0"},"canonical_sha256":"bce95f3ec9708b81ba6cdcd440e6d292481dc181669f891d23a4f5911df4db64","source":{"kind":"arxiv","id":"2402.06666","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.06666","created_at":"2026-07-05T07:43:36Z"},{"alias_kind":"arxiv_version","alias_value":"2402.06666v1","created_at":"2026-07-05T07:43:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.06666","created_at":"2026-07-05T07:43:36Z"},{"alias_kind":"pith_short_12","alias_value":"XTUV6PWJOCFY","created_at":"2026-07-05T07:43:36Z"},{"alias_kind":"pith_short_16","alias_value":"XTUV6PWJOCFYDOTM","created_at":"2026-07-05T07:43:36Z"},{"alias_kind":"pith_short_8","alias_value":"XTUV6PWJ","created_at":"2026-07-05T07:43:36Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2024:XTUV6PWJOCFYDOTM3TKEBZWSSJ","target":"record","payload":{"canonical_record":{"source":{"id":"2402.06666","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.ao-ph","submitted_at":"2024-02-06T21:28:42Z","cross_cats_sorted":["cs.AI","cs.LG"],"title_canon_sha256":"5ecae6d9a2eebb0a3e50c99ee5edfde5194700af5485faace3122338ccabbba1","abstract_canon_sha256":"81a8d08e561912befedc37ce63e557bd1b9b32bce027b82639ec9adfaa70ec20"},"schema_version":"1.0"},"canonical_sha256":"bce95f3ec9708b81ba6cdcd440e6d292481dc181669f891d23a4f5911df4db64","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:43:36.518298Z","signature_b64":"HAcBf/x48KsNwysK31mO0j8fGlYS7+tpxaBRGdtL0Qj/hYMKEgzBSDH7K8uZQBl/gaeeUE52aqkCWd1SU8JDDw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"bce95f3ec9708b81ba6cdcd440e6d292481dc181669f891d23a4f5911df4db64","last_reissued_at":"2026-07-05T07:43:36.517882Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:43:36.517882Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2402.06666","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:43:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"Pg08wIveB0/ODPIYM3thMrNCzEsV3rnMOnKW4284VozVyyv4UVnpmm+3J88KTTaedXR5D8d4VWVVQneHGvybBg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T20:41:47.908850Z"},"content_sha256":"9a0bc14496bfd9cf46e9a0221d49d163d62eaffcdece9d93f3f3e1a7c3bd16cd","schema_version":"1.0","event_id":"sha256:9a0bc14496bfd9cf46e9a0221d49d163d62eaffcdece9d93f3f3e1a7c3bd16cd"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2024:XTUV6PWJOCFYDOTM3TKEBZWSSJ","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Weather Prediction with Diffusion Guided by Realistic Forecast Processes","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI","cs.LG"],"primary_cat":"physics.ao-ph","authors_text":"Alexandra Anderson-Frey, Chengqian Ma, Yutong He, Zhanxiang Hua","submitted_at":"2024-02-06T21:28:42Z","abstract_excerpt":"Weather forecasting remains a crucial yet challenging domain, where recently developed models based on deep learning (DL) have approached the performance of traditional numerical weather prediction (NWP) models. However, these DL models, often complex and resource-intensive, face limitations in flexibility post-training and in incorporating NWP predictions, leading to reliability concerns due to potential unphysical predictions. In response, we introduce a novel method that applies diffusion models (DM) for weather forecasting. In particular, our method can achieve both direct and iterative fo"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.06666","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/2402.06666/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T07:43:36Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"3QPcPFZyWLKmlb9gyKcjsLkN3VDYXAqugVJz1gZ35gFDpiSGQWtetGFTHa5qUUWJ41rRe/HFVECCdNu8+HQ7Dw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-09T20:41:47.909368Z"},"content_sha256":"08695fab3f3b7acaceb075f4b21c6eb604717928170fa9578aa4618a287b0a72","schema_version":"1.0","event_id":"sha256:08695fab3f3b7acaceb075f4b21c6eb604717928170fa9578aa4618a287b0a72"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/XTUV6PWJOCFYDOTM3TKEBZWSSJ/bundle.json","state_url":"https://pith.science/pith/XTUV6PWJOCFYDOTM3TKEBZWSSJ/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/XTUV6PWJOCFYDOTM3TKEBZWSSJ/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-08-09T20:41:47Z","links":{"resolver":"https://pith.science/pith/XTUV6PWJOCFYDOTM3TKEBZWSSJ","bundle":"https://pith.science/pith/XTUV6PWJOCFYDOTM3TKEBZWSSJ/bundle.json","state":"https://pith.science/pith/XTUV6PWJOCFYDOTM3TKEBZWSSJ/state.json","well_known_bundle":"https://pith.science/.well-known/pith/XTUV6PWJOCFYDOTM3TKEBZWSSJ/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2024:XTUV6PWJOCFYDOTM3TKEBZWSSJ","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"81a8d08e561912befedc37ce63e557bd1b9b32bce027b82639ec9adfaa70ec20","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.ao-ph","submitted_at":"2024-02-06T21:28:42Z","title_canon_sha256":"5ecae6d9a2eebb0a3e50c99ee5edfde5194700af5485faace3122338ccabbba1"},"schema_version":"1.0","source":{"id":"2402.06666","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2402.06666","created_at":"2026-07-05T07:43:36Z"},{"alias_kind":"arxiv_version","alias_value":"2402.06666v1","created_at":"2026-07-05T07:43:36Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2402.06666","created_at":"2026-07-05T07:43:36Z"},{"alias_kind":"pith_short_12","alias_value":"XTUV6PWJOCFY","created_at":"2026-07-05T07:43:36Z"},{"alias_kind":"pith_short_16","alias_value":"XTUV6PWJOCFYDOTM","created_at":"2026-07-05T07:43:36Z"},{"alias_kind":"pith_short_8","alias_value":"XTUV6PWJ","created_at":"2026-07-05T07:43:36Z"}],"graph_snapshots":[{"event_id":"sha256:08695fab3f3b7acaceb075f4b21c6eb604717928170fa9578aa4618a287b0a72","target":"graph","created_at":"2026-07-05T07:43:36Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2402.06666/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Weather forecasting remains a crucial yet challenging domain, where recently developed models based on deep learning (DL) have approached the performance of traditional numerical weather prediction (NWP) models. However, these DL models, often complex and resource-intensive, face limitations in flexibility post-training and in incorporating NWP predictions, leading to reliability concerns due to potential unphysical predictions. In response, we introduce a novel method that applies diffusion models (DM) for weather forecasting. In particular, our method can achieve both direct and iterative fo","authors_text":"Alexandra Anderson-Frey, Chengqian Ma, Yutong He, Zhanxiang Hua","cross_cats":["cs.AI","cs.LG"],"headline":"","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.ao-ph","submitted_at":"2024-02-06T21:28:42Z","title":"Weather Prediction with Diffusion Guided by Realistic Forecast Processes"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2402.06666","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9a0bc14496bfd9cf46e9a0221d49d163d62eaffcdece9d93f3f3e1a7c3bd16cd","target":"record","created_at":"2026-07-05T07:43:36Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"81a8d08e561912befedc37ce63e557bd1b9b32bce027b82639ec9adfaa70ec20","cross_cats_sorted":["cs.AI","cs.LG"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.ao-ph","submitted_at":"2024-02-06T21:28:42Z","title_canon_sha256":"5ecae6d9a2eebb0a3e50c99ee5edfde5194700af5485faace3122338ccabbba1"},"schema_version":"1.0","source":{"id":"2402.06666","kind":"arxiv","version":1}},"canonical_sha256":"bce95f3ec9708b81ba6cdcd440e6d292481dc181669f891d23a4f5911df4db64","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"bce95f3ec9708b81ba6cdcd440e6d292481dc181669f891d23a4f5911df4db64","first_computed_at":"2026-07-05T07:43:36.517882Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T07:43:36.517882Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"HAcBf/x48KsNwysK31mO0j8fGlYS7+tpxaBRGdtL0Qj/hYMKEgzBSDH7K8uZQBl/gaeeUE52aqkCWd1SU8JDDw==","signature_status":"signed_v1","signed_at":"2026-07-05T07:43:36.518298Z","signed_message":"canonical_sha256_bytes"},"source_id":"2402.06666","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9a0bc14496bfd9cf46e9a0221d49d163d62eaffcdece9d93f3f3e1a7c3bd16cd","sha256:08695fab3f3b7acaceb075f4b21c6eb604717928170fa9578aa4618a287b0a72"],"state_sha256":"a93b057e250cfcff8bfb2b864800dacceaf221d9c3401cbfaa9b76633fc058b5"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"cqwgUG/UJ6/UH977vciYRxWNg4v+gDYOjGGVsFjVTEL0vGBGs4jD7Xx2qgOt0Gvo74iIsFBd2mYokghaXQ9iCw==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-09T20:41:47.915681Z","bundle_sha256":"2b5f0a7aa1a7b821f666ecc6352ad82f943a4d1742f2c779d12d506fc5c092af"}}