{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2024:YAHSPD2QJDWTNYXQORMZJZ3XLT","short_pith_number":"pith:YAHSPD2Q","schema_version":"1.0","canonical_sha256":"c00f278f5048ed36e2f0745994e7775cd948b6f75eef119f99fe324f978cec9e","source":{"kind":"arxiv","id":"2406.13627","version":2},"attestation_state":"computed","paper":{"title":"Can AI be enabled to dynamical downscaling? A Latent Diffusion Model to mimic km-scale COSMO5.0\\_CLM9 simulations","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["physics.ao-ph"],"primary_cat":"cs.LG","authors_text":"Elena Tomasi, Gabriele Franch, Marco Cristoforetti","submitted_at":"2024-06-19T15:20:28Z","abstract_excerpt":"Downscaling techniques are one of the most prominent applications of Deep Learning (DL) in Earth System Modeling. A robust DL downscaling model can generate high-resolution fields from coarse-scale numerical model simulations, saving the timely and resourceful applications of regional/local models. Additionally, generative DL models have the potential to provide uncertainty information, by generating ensemble-like scenario pools, a task that is computationally prohibitive for traditional numerical simulations. In this study, we apply a Latent Diffusion Model (LDM) to downscale ERA5 data over I"},"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":"2406.13627","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by-sa/4.0/","primary_cat":"cs.LG","submitted_at":"2024-06-19T15:20:28Z","cross_cats_sorted":["physics.ao-ph"],"title_canon_sha256":"f053cd129141e6f448ab5e49a30767bfb1eac977f0594300b724108646a39e6a","abstract_canon_sha256":"6229262cf60b69e1e1569495d477883e70e85b58b89693b165efc98110ba3532"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T11:59:52.485801Z","signature_b64":"ZWpbXWE3Pd4erRl2pmsFTbleasnlogksdwy1Qfgfu/hyH4q+ZvyR3evfJjyM+dwzVp1l5RL2lp760LwJc2EbDQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"c00f278f5048ed36e2f0745994e7775cd948b6f75eef119f99fe324f978cec9e","last_reissued_at":"2026-07-05T11:59:52.485275Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T11:59:52.485275Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Can AI be enabled to dynamical downscaling? A Latent Diffusion Model to mimic km-scale COSMO5.0\\_CLM9 simulations","license":"http://creativecommons.org/licenses/by-sa/4.0/","headline":"","cross_cats":["physics.ao-ph"],"primary_cat":"cs.LG","authors_text":"Elena Tomasi, Gabriele Franch, Marco Cristoforetti","submitted_at":"2024-06-19T15:20:28Z","abstract_excerpt":"Downscaling techniques are one of the most prominent applications of Deep Learning (DL) in Earth System Modeling. A robust DL downscaling model can generate high-resolution fields from coarse-scale numerical model simulations, saving the timely and resourceful applications of regional/local models. Additionally, generative DL models have the potential to provide uncertainty information, by generating ensemble-like scenario pools, a task that is computationally prohibitive for traditional numerical simulations. In this study, we apply a Latent Diffusion Model (LDM) to downscale ERA5 data over I"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2406.13627","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/2406.13627/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":"2406.13627","created_at":"2026-07-05T11:59:52.485350+00:00"},{"alias_kind":"arxiv_version","alias_value":"2406.13627v2","created_at":"2026-07-05T11:59:52.485350+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2406.13627","created_at":"2026-07-05T11:59:52.485350+00:00"},{"alias_kind":"pith_short_12","alias_value":"YAHSPD2QJDWT","created_at":"2026-07-05T11:59:52.485350+00:00"},{"alias_kind":"pith_short_16","alias_value":"YAHSPD2QJDWTNYXQ","created_at":"2026-07-05T11:59:52.485350+00:00"},{"alias_kind":"pith_short_8","alias_value":"YAHSPD2Q","created_at":"2026-07-05T11:59:52.485350+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.14798","citing_title":"MODS: Multi-source Observations Conditional Diffusion Model for Meteorological State Downscaling","ref_index":2021,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/YAHSPD2QJDWTNYXQORMZJZ3XLT","json":"https://pith.science/pith/YAHSPD2QJDWTNYXQORMZJZ3XLT.json","graph_json":"https://pith.science/api/pith-number/YAHSPD2QJDWTNYXQORMZJZ3XLT/graph.json","events_json":"https://pith.science/api/pith-number/YAHSPD2QJDWTNYXQORMZJZ3XLT/events.json","paper":"https://pith.science/paper/YAHSPD2Q"},"agent_actions":{"view_html":"https://pith.science/pith/YAHSPD2QJDWTNYXQORMZJZ3XLT","download_json":"https://pith.science/pith/YAHSPD2QJDWTNYXQORMZJZ3XLT.json","view_paper":"https://pith.science/paper/YAHSPD2Q","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2406.13627&json=true","fetch_graph":"https://pith.science/api/pith-number/YAHSPD2QJDWTNYXQORMZJZ3XLT/graph.json","fetch_events":"https://pith.science/api/pith-number/YAHSPD2QJDWTNYXQORMZJZ3XLT/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/YAHSPD2QJDWTNYXQORMZJZ3XLT/action/timestamp_anchor","attest_storage":"https://pith.science/pith/YAHSPD2QJDWTNYXQORMZJZ3XLT/action/storage_attestation","attest_author":"https://pith.science/pith/YAHSPD2QJDWTNYXQORMZJZ3XLT/action/author_attestation","sign_citation":"https://pith.science/pith/YAHSPD2QJDWTNYXQORMZJZ3XLT/action/citation_signature","submit_replication":"https://pith.science/pith/YAHSPD2QJDWTNYXQORMZJZ3XLT/action/replication_record"}},"created_at":"2026-07-05T11:59:52.485350+00:00","updated_at":"2026-07-05T11:59:52.485350+00:00"}