{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:VZP3IPK45JMO2RFHPQ4LH74FEJ","short_pith_number":"pith:VZP3IPK4","schema_version":"1.0","canonical_sha256":"ae5fb43d5cea58ed44a77c38b3ff85226c8f62f0f902ac68592b7e9f545c615e","source":{"kind":"arxiv","id":"2401.11960","version":1},"attestation_state":"computed","paper":{"title":"Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Hao Chen, Keyan Chen, Lei Bai, Wanli Ouyang, Wenyuan Li, Zhengxia Zou, Zhengyi Wang, Zhenwei Shi, Zili Liu","submitted_at":"2024-01-22T14:02:56Z","abstract_excerpt":"Downscaling (DS) of meteorological variables involves obtaining high-resolution states from low-resolution meteorological fields and is an important task in weather forecasting. Previous methods based on deep learning treat downscaling as a super-resolution task in computer vision and utilize high-resolution gridded meteorological fields as supervision to improve resolution at specific grid scales. However, this approach has struggled to align with the continuous distribution characteristics of meteorological fields, leading to an inherent systematic bias between the downscaled results and the"},"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":"2401.11960","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"cs.CV","submitted_at":"2024-01-22T14:02:56Z","cross_cats_sorted":["eess.IV"],"title_canon_sha256":"9f7ecfb7131adee98078cfacb43c5db8464024251733f423d544309bc9c9ea51","abstract_canon_sha256":"7267b1d66d6f3dabe1b84251b32d138d064db13754ffabea83648e108471e73b"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:36:12.954996Z","signature_b64":"n+ZPREjioM1iEFvfPVljVTSo0LyhN691AMe8kVSCBvZeuXPUuQu3g4UnVYWnA+wGzWbf66wdLxbBu3M4xLHgBw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"ae5fb43d5cea58ed44a77c38b3ff85226c8f62f0f902ac68592b7e9f545c615e","last_reissued_at":"2026-07-05T07:36:12.954476Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:36:12.954476Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Observation-Guided Meteorological Field Downscaling at Station Scale: A Benchmark and a New Method","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["eess.IV"],"primary_cat":"cs.CV","authors_text":"Hao Chen, Keyan Chen, Lei Bai, Wanli Ouyang, Wenyuan Li, Zhengxia Zou, Zhengyi Wang, Zhenwei Shi, Zili Liu","submitted_at":"2024-01-22T14:02:56Z","abstract_excerpt":"Downscaling (DS) of meteorological variables involves obtaining high-resolution states from low-resolution meteorological fields and is an important task in weather forecasting. Previous methods based on deep learning treat downscaling as a super-resolution task in computer vision and utilize high-resolution gridded meteorological fields as supervision to improve resolution at specific grid scales. However, this approach has struggled to align with the continuous distribution characteristics of meteorological fields, leading to an inherent systematic bias between the downscaled results and the"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2401.11960","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/2401.11960/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":"2401.11960","created_at":"2026-07-05T07:36:12.954533+00:00"},{"alias_kind":"arxiv_version","alias_value":"2401.11960v1","created_at":"2026-07-05T07:36:12.954533+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2401.11960","created_at":"2026-07-05T07:36:12.954533+00:00"},{"alias_kind":"pith_short_12","alias_value":"VZP3IPK45JMO","created_at":"2026-07-05T07:36:12.954533+00:00"},{"alias_kind":"pith_short_16","alias_value":"VZP3IPK45JMO2RFH","created_at":"2026-07-05T07:36:12.954533+00:00"},{"alias_kind":"pith_short_8","alias_value":"VZP3IPK4","created_at":"2026-07-05T07:36:12.954533+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.09511","citing_title":"WindINR: Latent-State INR for Fast Local Wind Query and Correction in Complex Terrain","ref_index":16,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/VZP3IPK45JMO2RFHPQ4LH74FEJ","json":"https://pith.science/pith/VZP3IPK45JMO2RFHPQ4LH74FEJ.json","graph_json":"https://pith.science/api/pith-number/VZP3IPK45JMO2RFHPQ4LH74FEJ/graph.json","events_json":"https://pith.science/api/pith-number/VZP3IPK45JMO2RFHPQ4LH74FEJ/events.json","paper":"https://pith.science/paper/VZP3IPK4"},"agent_actions":{"view_html":"https://pith.science/pith/VZP3IPK45JMO2RFHPQ4LH74FEJ","download_json":"https://pith.science/pith/VZP3IPK45JMO2RFHPQ4LH74FEJ.json","view_paper":"https://pith.science/paper/VZP3IPK4","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2401.11960&json=true","fetch_graph":"https://pith.science/api/pith-number/VZP3IPK45JMO2RFHPQ4LH74FEJ/graph.json","fetch_events":"https://pith.science/api/pith-number/VZP3IPK45JMO2RFHPQ4LH74FEJ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/VZP3IPK45JMO2RFHPQ4LH74FEJ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/VZP3IPK45JMO2RFHPQ4LH74FEJ/action/storage_attestation","attest_author":"https://pith.science/pith/VZP3IPK45JMO2RFHPQ4LH74FEJ/action/author_attestation","sign_citation":"https://pith.science/pith/VZP3IPK45JMO2RFHPQ4LH74FEJ/action/citation_signature","submit_replication":"https://pith.science/pith/VZP3IPK45JMO2RFHPQ4LH74FEJ/action/replication_record"}},"created_at":"2026-07-05T07:36:12.954533+00:00","updated_at":"2026-07-05T07:36:12.954533+00:00"}