{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2020:FB4XGFJGFQQ4UJZ626HG4UI45X","short_pith_number":"pith:FB4XGFJG","schema_version":"1.0","canonical_sha256":"28797315262c21ca273ed78e6e511cedcc1493814383042e2f12787ded51ee5c","source":{"kind":"arxiv","id":"2008.08626","version":2},"attestation_state":"computed","paper":{"title":"Data-driven medium-range weather prediction with a Resnet pretrained on climate simulations: A new model for WeatherBench","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.ao-ph","authors_text":"Nils Thuerey, Stephan Rasp","submitted_at":"2020-08-19T18:40:00Z","abstract_excerpt":"Numerical weather prediction has traditionally been based on physical models of the atmosphere. Recently, however, the rise of deep learning has created increased interest in purely data-driven medium-range weather forecasting with first studies exploring the feasibility of such an approach. To accelerate progress in this area, the WeatherBench benchmark challenge was defined. Here, we train a deep residual convolutional neural network (Resnet) to predict geopotential, temperature and precipitation at 5.625 degree resolution up to 5 days ahead. To avoid overfitting and improve forecast skill, "},"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":"2008.08626","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.ao-ph","submitted_at":"2020-08-19T18:40:00Z","cross_cats_sorted":[],"title_canon_sha256":"2d3e29a7b4b2b5f1531abc4c9e5493d80bf199b29eaa1bede7960af1bdff736c","abstract_canon_sha256":"99337e31f2734308ea67a9acdd1781ef21ca9066ee2feb8be4b4f1fc69587cc1"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T02:23:11.731994Z","signature_b64":"wzUJFBmGc+j6yUpuK3CdKKmRv6kjaKZfighkPc3LutND+L3xSGiAT+Vep024p/Cbp9FLENTVMBR2vTrxD5oLCw==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"28797315262c21ca273ed78e6e511cedcc1493814383042e2f12787ded51ee5c","last_reissued_at":"2026-07-05T02:23:11.731493Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T02:23:11.731493Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Data-driven medium-range weather prediction with a Resnet pretrained on climate simulations: A new model for WeatherBench","license":"http://creativecommons.org/licenses/by/4.0/","headline":"","cross_cats":[],"primary_cat":"physics.ao-ph","authors_text":"Nils Thuerey, Stephan Rasp","submitted_at":"2020-08-19T18:40:00Z","abstract_excerpt":"Numerical weather prediction has traditionally been based on physical models of the atmosphere. Recently, however, the rise of deep learning has created increased interest in purely data-driven medium-range weather forecasting with first studies exploring the feasibility of such an approach. To accelerate progress in this area, the WeatherBench benchmark challenge was defined. Here, we train a deep residual convolutional neural network (Resnet) to predict geopotential, temperature and precipitation at 5.625 degree resolution up to 5 days ahead. To avoid overfitting and improve forecast skill, "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2008.08626","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/2008.08626/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":"2008.08626","created_at":"2026-07-05T02:23:11.731556+00:00"},{"alias_kind":"arxiv_version","alias_value":"2008.08626v2","created_at":"2026-07-05T02:23:11.731556+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2008.08626","created_at":"2026-07-05T02:23:11.731556+00:00"},{"alias_kind":"pith_short_12","alias_value":"FB4XGFJGFQQ4","created_at":"2026-07-05T02:23:11.731556+00:00"},{"alias_kind":"pith_short_16","alias_value":"FB4XGFJGFQQ4UJZ6","created_at":"2026-07-05T02:23:11.731556+00:00"},{"alias_kind":"pith_short_8","alias_value":"FB4XGFJG","created_at":"2026-07-05T02:23:11.731556+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":2,"internal_anchor_count":0,"sample":[{"citing_arxiv_id":"2606.06348","citing_title":"Performance Evaluation of GraphCast for Medium-Range Weather Forecasting over Brazil","ref_index":31,"is_internal_anchor":false},{"citing_arxiv_id":"2202.11214","citing_title":"FourCastNet: A Global Data-driven High-resolution Weather Model using Adaptive Fourier Neural Operators","ref_index":16,"is_internal_anchor":false}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/FB4XGFJGFQQ4UJZ626HG4UI45X","json":"https://pith.science/pith/FB4XGFJGFQQ4UJZ626HG4UI45X.json","graph_json":"https://pith.science/api/pith-number/FB4XGFJGFQQ4UJZ626HG4UI45X/graph.json","events_json":"https://pith.science/api/pith-number/FB4XGFJGFQQ4UJZ626HG4UI45X/events.json","paper":"https://pith.science/paper/FB4XGFJG"},"agent_actions":{"view_html":"https://pith.science/pith/FB4XGFJGFQQ4UJZ626HG4UI45X","download_json":"https://pith.science/pith/FB4XGFJGFQQ4UJZ626HG4UI45X.json","view_paper":"https://pith.science/paper/FB4XGFJG","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2008.08626&json=true","fetch_graph":"https://pith.science/api/pith-number/FB4XGFJGFQQ4UJZ626HG4UI45X/graph.json","fetch_events":"https://pith.science/api/pith-number/FB4XGFJGFQQ4UJZ626HG4UI45X/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/FB4XGFJGFQQ4UJZ626HG4UI45X/action/timestamp_anchor","attest_storage":"https://pith.science/pith/FB4XGFJGFQQ4UJZ626HG4UI45X/action/storage_attestation","attest_author":"https://pith.science/pith/FB4XGFJGFQQ4UJZ626HG4UI45X/action/author_attestation","sign_citation":"https://pith.science/pith/FB4XGFJGFQQ4UJZ626HG4UI45X/action/citation_signature","submit_replication":"https://pith.science/pith/FB4XGFJGFQQ4UJZ626HG4UI45X/action/replication_record"}},"created_at":"2026-07-05T02:23:11.731556+00:00","updated_at":"2026-07-05T02:23:11.731556+00:00"}