{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:E42RBOAKUNEKXZB2GPUQKIGJSL","short_pith_number":"pith:E42RBOAK","schema_version":"1.0","canonical_sha256":"273510b80aa348abe43a33e90520c992e16db6d607a874f1399b3d435fcc6c75","source":{"kind":"arxiv","id":"2403.03929","version":1},"attestation_state":"computed","paper":{"title":"Extreme Precipitation Nowcasting using Transformer-based Generative Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Ankush Roy, Cristian Meo, Junzhe Yin, Justin Dauwels, Mircea Lic\\u{a}, Remko Uijlenhoet, Ruben Imhoff, Yanbo Wang, Zeineb Bou Che","submitted_at":"2024-03-06T18:39:41Z","abstract_excerpt":"This paper presents an innovative approach to extreme precipitation nowcasting by employing Transformer-based generative models, namely NowcastingGPT with Extreme Value Loss (EVL) regularization. Leveraging a comprehensive dataset from the Royal Netherlands Meteorological Institute (KNMI), our study focuses on predicting short-term precipitation with high accuracy. We introduce a novel method for computing EVL without assuming fixed extreme representations, addressing the limitations of current models in capturing extreme weather events. We present both qualitative and quantitative analyses, d"},"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":"2403.03929","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"cs.LG","submitted_at":"2024-03-06T18:39:41Z","cross_cats_sorted":["cs.AI"],"title_canon_sha256":"f8ede771aa02ab275c08fd6875213e7095f7d51a5e70abb06c22ac7d78162773","abstract_canon_sha256":"22e3d98c568428fe550d29fe36cb876674e33062818d7545c83265b29afdfb0a"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T07:53:03.775247Z","signature_b64":"62F6iozcq82h+Cv2rk3LKF/AKO4JIpNjsOlsRaIYBCZZ4vauTS/z+pl3SURJZJoFSsadNLCMRG8OCGVnC5E2Cg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"273510b80aa348abe43a33e90520c992e16db6d607a874f1399b3d435fcc6c75","last_reissued_at":"2026-07-05T07:53:03.774628Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T07:53:03.774628Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Extreme Precipitation Nowcasting using Transformer-based Generative Models","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":["cs.AI"],"primary_cat":"cs.LG","authors_text":"Ankush Roy, Cristian Meo, Junzhe Yin, Justin Dauwels, Mircea Lic\\u{a}, Remko Uijlenhoet, Ruben Imhoff, Yanbo Wang, Zeineb Bou Che","submitted_at":"2024-03-06T18:39:41Z","abstract_excerpt":"This paper presents an innovative approach to extreme precipitation nowcasting by employing Transformer-based generative models, namely NowcastingGPT with Extreme Value Loss (EVL) regularization. Leveraging a comprehensive dataset from the Royal Netherlands Meteorological Institute (KNMI), our study focuses on predicting short-term precipitation with high accuracy. We introduce a novel method for computing EVL without assuming fixed extreme representations, addressing the limitations of current models in capturing extreme weather events. We present both qualitative and quantitative analyses, d"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2403.03929","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/2403.03929/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":"2403.03929","created_at":"2026-07-05T07:53:03.774712+00:00"},{"alias_kind":"arxiv_version","alias_value":"2403.03929v1","created_at":"2026-07-05T07:53:03.774712+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2403.03929","created_at":"2026-07-05T07:53:03.774712+00:00"},{"alias_kind":"pith_short_12","alias_value":"E42RBOAKUNEK","created_at":"2026-07-05T07:53:03.774712+00:00"},{"alias_kind":"pith_short_16","alias_value":"E42RBOAKUNEKXZB2","created_at":"2026-07-05T07:53:03.774712+00:00"},{"alias_kind":"pith_short_8","alias_value":"E42RBOAK","created_at":"2026-07-05T07:53:03.774712+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2605.10046","citing_title":"PixelFlowCast: Latent-Free Precipitation Nowcasting via Pixel Mean Flows","ref_index":36,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/E42RBOAKUNEKXZB2GPUQKIGJSL","json":"https://pith.science/pith/E42RBOAKUNEKXZB2GPUQKIGJSL.json","graph_json":"https://pith.science/api/pith-number/E42RBOAKUNEKXZB2GPUQKIGJSL/graph.json","events_json":"https://pith.science/api/pith-number/E42RBOAKUNEKXZB2GPUQKIGJSL/events.json","paper":"https://pith.science/paper/E42RBOAK"},"agent_actions":{"view_html":"https://pith.science/pith/E42RBOAKUNEKXZB2GPUQKIGJSL","download_json":"https://pith.science/pith/E42RBOAKUNEKXZB2GPUQKIGJSL.json","view_paper":"https://pith.science/paper/E42RBOAK","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2403.03929&json=true","fetch_graph":"https://pith.science/api/pith-number/E42RBOAKUNEKXZB2GPUQKIGJSL/graph.json","fetch_events":"https://pith.science/api/pith-number/E42RBOAKUNEKXZB2GPUQKIGJSL/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/E42RBOAKUNEKXZB2GPUQKIGJSL/action/timestamp_anchor","attest_storage":"https://pith.science/pith/E42RBOAKUNEKXZB2GPUQKIGJSL/action/storage_attestation","attest_author":"https://pith.science/pith/E42RBOAKUNEKXZB2GPUQKIGJSL/action/author_attestation","sign_citation":"https://pith.science/pith/E42RBOAKUNEKXZB2GPUQKIGJSL/action/citation_signature","submit_replication":"https://pith.science/pith/E42RBOAKUNEKXZB2GPUQKIGJSL/action/replication_record"}},"created_at":"2026-07-05T07:53:03.774712+00:00","updated_at":"2026-07-05T07:53:03.774712+00:00"}