{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2025:2CLFAQ7F33VFG32DU66K7BBYEV","short_pith_number":"pith:2CLFAQ7F","schema_version":"1.0","canonical_sha256":"d0965043e5deea536f43a7bcaf843825508a9f9b2abd6394bba51f525087ddb4","source":{"kind":"arxiv","id":"2510.25045","version":2},"attestation_state":"computed","paper":{"title":"Evaluating Extreme Precipitation Forecasts: A Threshold-Weighted, Spatial Verification Approach for Comparing an AI Weather Prediction Model Against a High-Resolution NWP Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.ao-ph","authors_text":"Nicholas Loveday, Tracy Hertneky","submitted_at":"2025-10-29T00:09:45Z","abstract_excerpt":"Recent advances in AI-based weather prediction have led to the development of artificial intelligence weather prediction (AIWP) models with competitive forecast skill compared to traditional NWP models, but with substantially reduced computational cost. There is a strong need for appropriate methods to evaluate their ability to predict extreme weather events, particularly when spatial coherence is important, and grid resolutions differ between models.\n  We introduce a verification framework that combines spatial verification methods and proper scoring rules. Specifically, the framework extends"},"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":"2510.25045","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.ao-ph","submitted_at":"2025-10-29T00:09:45Z","cross_cats_sorted":[],"title_canon_sha256":"e7dedbbef6524da1f7e471408945c37c34e16afd18d619486470d4c7590b1175","abstract_canon_sha256":"d1b738ea421da5f4b8f706ffa8cdbda38f95b1962ad124642811f4cfb013488a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-06-01T01:02:24.642276Z","signature_b64":"SqW/Tm2KHfsbfUpJtSTDx1Z7xuJYGTwR9IhAbPmF+5zeyQQrXKttZ8BFi4Iyjc67OquHMj/Oe6Cuux2EiBqHCQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d0965043e5deea536f43a7bcaf843825508a9f9b2abd6394bba51f525087ddb4","last_reissued_at":"2026-06-01T01:02:24.641103Z","signature_status":"signed_v1","first_computed_at":"2026-06-01T01:02:24.641103Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Evaluating Extreme Precipitation Forecasts: A Threshold-Weighted, Spatial Verification Approach for Comparing an AI Weather Prediction Model Against a High-Resolution NWP Model","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.ao-ph","authors_text":"Nicholas Loveday, Tracy Hertneky","submitted_at":"2025-10-29T00:09:45Z","abstract_excerpt":"Recent advances in AI-based weather prediction have led to the development of artificial intelligence weather prediction (AIWP) models with competitive forecast skill compared to traditional NWP models, but with substantially reduced computational cost. There is a strong need for appropriate methods to evaluate their ability to predict extreme weather events, particularly when spatial coherence is important, and grid resolutions differ between models.\n  We introduce a verification framework that combines spatial verification methods and proper scoring rules. Specifically, the framework extends"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2510.25045","kind":"arxiv","version":2},"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/2510.25045/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":"2510.25045","created_at":"2026-06-01T01:02:24.641248+00:00"},{"alias_kind":"arxiv_version","alias_value":"2510.25045v2","created_at":"2026-06-01T01:02:24.641248+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2510.25045","created_at":"2026-06-01T01:02:24.641248+00:00"},{"alias_kind":"pith_short_12","alias_value":"2CLFAQ7F33VF","created_at":"2026-06-01T01:02:24.641248+00:00"},{"alias_kind":"pith_short_16","alias_value":"2CLFAQ7F33VFG32D","created_at":"2026-06-01T01:02:24.641248+00:00"},{"alias_kind":"pith_short_8","alias_value":"2CLFAQ7F","created_at":"2026-06-01T01:02:24.641248+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":0,"internal_anchor_count":0,"sample":[]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/2CLFAQ7F33VFG32DU66K7BBYEV","json":"https://pith.science/pith/2CLFAQ7F33VFG32DU66K7BBYEV.json","graph_json":"https://pith.science/api/pith-number/2CLFAQ7F33VFG32DU66K7BBYEV/graph.json","events_json":"https://pith.science/api/pith-number/2CLFAQ7F33VFG32DU66K7BBYEV/events.json","paper":"https://pith.science/paper/2CLFAQ7F"},"agent_actions":{"view_html":"https://pith.science/pith/2CLFAQ7F33VFG32DU66K7BBYEV","download_json":"https://pith.science/pith/2CLFAQ7F33VFG32DU66K7BBYEV.json","view_paper":"https://pith.science/paper/2CLFAQ7F","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2510.25045&json=true","fetch_graph":"https://pith.science/api/pith-number/2CLFAQ7F33VFG32DU66K7BBYEV/graph.json","fetch_events":"https://pith.science/api/pith-number/2CLFAQ7F33VFG32DU66K7BBYEV/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/2CLFAQ7F33VFG32DU66K7BBYEV/action/timestamp_anchor","attest_storage":"https://pith.science/pith/2CLFAQ7F33VFG32DU66K7BBYEV/action/storage_attestation","attest_author":"https://pith.science/pith/2CLFAQ7F33VFG32DU66K7BBYEV/action/author_attestation","sign_citation":"https://pith.science/pith/2CLFAQ7F33VFG32DU66K7BBYEV/action/citation_signature","submit_replication":"https://pith.science/pith/2CLFAQ7F33VFG32DU66K7BBYEV/action/replication_record"}},"created_at":"2026-06-01T01:02:24.641248+00:00","updated_at":"2026-06-01T01:02:24.641248+00:00"}