{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:5QBIHIUHV53EN63ZOS4DF467XL","short_pith_number":"pith:5QBIHIUH","schema_version":"1.0","canonical_sha256":"ec0283a287af7646fb7974b832f3dfbaec990a32057bc3bd96a486e2aae3f3b3","source":{"kind":"arxiv","id":"2501.18122","version":1},"attestation_state":"computed","paper":{"title":"VQLTI: Long-Term Tropical Cyclone Intensity Forecasting with Physical Constraints","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Bin Li, Kang Chen, Lei Bai, Lei Liu, Tao Han, Xinyu Wang","submitted_at":"2025-01-30T03:52:37Z","abstract_excerpt":"Tropical cyclone (TC) intensity forecasting is crucial for early disaster warning and emergency decision-making. Numerous researchers have explored deep-learning methods to address computational and post-processing issues in operational forecasting. Regrettably, they exhibit subpar long-term forecasting capabilities. We use two strategies to enhance long-term forecasting. (1) By enhancing the matching between TC intensity and spatial information, we can improve long-term forecasting performance. (2) Incorporating physical knowledge and physical constraints can help mitigate the accumulation of"},"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":"2501.18122","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.LG","submitted_at":"2025-01-30T03:52:37Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"eb918328d4898e862d855ea9dfaafed981bbf0a4fdeb9191ef0014b0e1812038","abstract_canon_sha256":"9669403f834eb4ea379d1b1ba84e6e010a5c6e35737e66c754d667fa8de5b178"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T10:07:07.419276Z","signature_b64":"neVPe7lkf2STYHe5CvkTBIQyAY/TxdSXevIp75tzKUD+QT4+BfXTJkFwWqL4cVX6F+m+yui8fl1WDzYpTq2KDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ec0283a287af7646fb7974b832f3dfbaec990a32057bc3bd96a486e2aae3f3b3","last_reissued_at":"2026-07-05T10:07:07.418741Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T10:07:07.418741Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"VQLTI: Long-Term Tropical Cyclone Intensity Forecasting with Physical Constraints","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Bin Li, Kang Chen, Lei Bai, Lei Liu, Tao Han, Xinyu Wang","submitted_at":"2025-01-30T03:52:37Z","abstract_excerpt":"Tropical cyclone (TC) intensity forecasting is crucial for early disaster warning and emergency decision-making. Numerous researchers have explored deep-learning methods to address computational and post-processing issues in operational forecasting. Regrettably, they exhibit subpar long-term forecasting capabilities. We use two strategies to enhance long-term forecasting. (1) By enhancing the matching between TC intensity and spatial information, we can improve long-term forecasting performance. (2) Incorporating physical knowledge and physical constraints can help mitigate the accumulation of"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2501.18122","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/2501.18122/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":"2501.18122","created_at":"2026-07-05T10:07:07.418813+00:00"},{"alias_kind":"arxiv_version","alias_value":"2501.18122v1","created_at":"2026-07-05T10:07:07.418813+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2501.18122","created_at":"2026-07-05T10:07:07.418813+00:00"},{"alias_kind":"pith_short_12","alias_value":"5QBIHIUHV53E","created_at":"2026-07-05T10:07:07.418813+00:00"},{"alias_kind":"pith_short_16","alias_value":"5QBIHIUHV53EN63Z","created_at":"2026-07-05T10:07:07.418813+00:00"},{"alias_kind":"pith_short_8","alias_value":"5QBIHIUH","created_at":"2026-07-05T10:07:07.418813+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2508.16168","citing_title":"FuXi-TC: A generative framework integrating deep learning and physics-based models for improved tropical cyclone forecasts","ref_index":25,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/5QBIHIUHV53EN63ZOS4DF467XL","json":"https://pith.science/pith/5QBIHIUHV53EN63ZOS4DF467XL.json","graph_json":"https://pith.science/api/pith-number/5QBIHIUHV53EN63ZOS4DF467XL/graph.json","events_json":"https://pith.science/api/pith-number/5QBIHIUHV53EN63ZOS4DF467XL/events.json","paper":"https://pith.science/paper/5QBIHIUH"},"agent_actions":{"view_html":"https://pith.science/pith/5QBIHIUHV53EN63ZOS4DF467XL","download_json":"https://pith.science/pith/5QBIHIUHV53EN63ZOS4DF467XL.json","view_paper":"https://pith.science/paper/5QBIHIUH","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2501.18122&json=true","fetch_graph":"https://pith.science/api/pith-number/5QBIHIUHV53EN63ZOS4DF467XL/graph.json","fetch_events":"https://pith.science/api/pith-number/5QBIHIUHV53EN63ZOS4DF467XL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/5QBIHIUHV53EN63ZOS4DF467XL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/5QBIHIUHV53EN63ZOS4DF467XL/action/storage_attestation","attest_author":"https://pith.science/pith/5QBIHIUHV53EN63ZOS4DF467XL/action/author_attestation","sign_citation":"https://pith.science/pith/5QBIHIUHV53EN63ZOS4DF467XL/action/citation_signature","submit_replication":"https://pith.science/pith/5QBIHIUHV53EN63ZOS4DF467XL/action/replication_record"}},"created_at":"2026-07-05T10:07:07.418813+00:00","updated_at":"2026-07-05T10:07:07.418813+00:00"}