{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2013:ICM2FU57CFX777K674XKHHVVVE","short_pith_number":"pith:ICM2FU57","schema_version":"1.0","canonical_sha256":"4099a2d3bf116ffffd5eff2ea39eb5a93b778ac583a42d8a5af7555ad7b3bfd3","source":{"kind":"arxiv","id":"1312.3763","version":1},"attestation_state":"computed","paper":{"title":"Comparison of BMA and EMOS statistical calibration methods for temperature and wind speed ensemble weather prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Andr\\'as Hor\\'anyi, D\\'ora Nemoda, S\\'andor Baran","submitted_at":"2013-12-13T10:38:52Z","abstract_excerpt":"The evolution of the weather can be described by deterministic numerical weather forecasting models. Multiple runs of these models with different initial conditions and/or model physics result in forecast ensembles which are used for estimating the distribution of future atmospheric variables. However, these ensembles are usually under-dispersive and uncalibrated, so post-processing is required.\n  In the present work we compare different versions of Bayesian Model Averaging (BMA) and Ensemble Model Output Statistics (EMOS) post-processing methods in order to calibrate 2m temperature and 10m wi"},"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":"1312.3763","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2013-12-13T10:38:52Z","cross_cats_sorted":[],"title_canon_sha256":"c5b6b9aad31e9e08830c04226d0c6fa25ddbbe1df0da65352cb45f3c58466b3a","abstract_canon_sha256":"477866448382515260c254ca7d954b80d9066e086eb6619b25413be0bbc2e696"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T01:36:39.011964Z","signature_b64":"782eH1RN7G1QvWwiIsoA1NHCp4wyPJiZWcnoAm4Fp8Rl431Q3L7BH83Fmnq6BJr3Uyg9TxTzabol/x8zBT+EDg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"4099a2d3bf116ffffd5eff2ea39eb5a93b778ac583a42d8a5af7555ad7b3bfd3","last_reissued_at":"2026-05-18T01:36:39.011277Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T01:36:39.011277Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Comparison of BMA and EMOS statistical calibration methods for temperature and wind speed ensemble weather prediction","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Andr\\'as Hor\\'anyi, D\\'ora Nemoda, S\\'andor Baran","submitted_at":"2013-12-13T10:38:52Z","abstract_excerpt":"The evolution of the weather can be described by deterministic numerical weather forecasting models. Multiple runs of these models with different initial conditions and/or model physics result in forecast ensembles which are used for estimating the distribution of future atmospheric variables. However, these ensembles are usually under-dispersive and uncalibrated, so post-processing is required.\n  In the present work we compare different versions of Bayesian Model Averaging (BMA) and Ensemble Model Output Statistics (EMOS) post-processing methods in order to calibrate 2m temperature and 10m wi"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1312.3763","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":""},"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":"1312.3763","created_at":"2026-05-18T01:36:39.011380+00:00"},{"alias_kind":"arxiv_version","alias_value":"1312.3763v1","created_at":"2026-05-18T01:36:39.011380+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1312.3763","created_at":"2026-05-18T01:36:39.011380+00:00"},{"alias_kind":"pith_short_12","alias_value":"ICM2FU57CFX7","created_at":"2026-05-18T12:27:46.883200+00:00"},{"alias_kind":"pith_short_16","alias_value":"ICM2FU57CFX777K6","created_at":"2026-05-18T12:27:46.883200+00:00"},{"alias_kind":"pith_short_8","alias_value":"ICM2FU57","created_at":"2026-05-18T12:27:46.883200+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/ICM2FU57CFX777K674XKHHVVVE","json":"https://pith.science/pith/ICM2FU57CFX777K674XKHHVVVE.json","graph_json":"https://pith.science/api/pith-number/ICM2FU57CFX777K674XKHHVVVE/graph.json","events_json":"https://pith.science/api/pith-number/ICM2FU57CFX777K674XKHHVVVE/events.json","paper":"https://pith.science/paper/ICM2FU57"},"agent_actions":{"view_html":"https://pith.science/pith/ICM2FU57CFX777K674XKHHVVVE","download_json":"https://pith.science/pith/ICM2FU57CFX777K674XKHHVVVE.json","view_paper":"https://pith.science/paper/ICM2FU57","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1312.3763&json=true","fetch_graph":"https://pith.science/api/pith-number/ICM2FU57CFX777K674XKHHVVVE/graph.json","fetch_events":"https://pith.science/api/pith-number/ICM2FU57CFX777K674XKHHVVVE/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/ICM2FU57CFX777K674XKHHVVVE/action/timestamp_anchor","attest_storage":"https://pith.science/pith/ICM2FU57CFX777K674XKHHVVVE/action/storage_attestation","attest_author":"https://pith.science/pith/ICM2FU57CFX777K674XKHHVVVE/action/author_attestation","sign_citation":"https://pith.science/pith/ICM2FU57CFX777K674XKHHVVVE/action/citation_signature","submit_replication":"https://pith.science/pith/ICM2FU57CFX777K674XKHHVVVE/action/replication_record"}},"created_at":"2026-05-18T01:36:39.011380+00:00","updated_at":"2026-05-18T01:36:39.011380+00:00"}