{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2022:NET3TUWNBV66WLKZBJR4MPDJIZ","short_pith_number":"pith:NET3TUWN","schema_version":"1.0","canonical_sha256":"6927b9d2cd0d7deb2d590a63c63c694640e8c6f04bcfd34cfa7b35045789bb20","source":{"kind":"arxiv","id":"2301.01254","version":1},"attestation_state":"computed","paper":{"title":"Machine learning prediction of the MJO extends beyond one month","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["math.DS"],"primary_cat":"physics.ao-ph","authors_text":"Daisuke Takasuka, Hiroaki Miura, Kengo Nakai, Takuya Jinno, Tamaki Suematsu, Tsuyoshi Yoneda, Yoshitaka Saiki","submitted_at":"2022-12-29T06:41:07Z","abstract_excerpt":"The prediction of the Madden-Julian Oscillation (MJO), a massive tropical weather event with vast global socio-economic impacts, has been infamously difficult with physics-based weather prediction models. Here we construct a machine learning model using reservoir computing technique that forecasts the real-time multivariate MJO index (RMM), a macroscopic variable that represents the state of the MJO. The training data was refined by developing a novel filter that extracts the recurrency of MJO signals from the raw atmospheric data and selecting a suitable time-delay coordinate of the RMM. The "},"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":"2301.01254","kind":"arxiv","version":1},"metadata":{"license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","primary_cat":"physics.ao-ph","submitted_at":"2022-12-29T06:41:07Z","cross_cats_sorted":["math.DS"],"title_canon_sha256":"4597d9f60f4a0f30dd38016af74b6bbdf6de69289faee351d3a77c8ae72eda70","abstract_canon_sha256":"b5117a60f1e007c209ee18e9c22d7fc4d4b61f55dd12d882f560aca51b4b8670"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T05:30:22.375002Z","signature_b64":"OB26N1kNSIJBLG8n/UtRicACViMTqjIaebN/pA9ot+rwPlpA5igCVZgfnfCExgFTnUcsFNywjhopUVuA0IX5BA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"6927b9d2cd0d7deb2d590a63c63c694640e8c6f04bcfd34cfa7b35045789bb20","last_reissued_at":"2026-07-05T05:30:22.374639Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T05:30:22.374639Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"Machine learning prediction of the MJO extends beyond one month","license":"http://creativecommons.org/licenses/by-nc-sa/4.0/","headline":"","cross_cats":["math.DS"],"primary_cat":"physics.ao-ph","authors_text":"Daisuke Takasuka, Hiroaki Miura, Kengo Nakai, Takuya Jinno, Tamaki Suematsu, Tsuyoshi Yoneda, Yoshitaka Saiki","submitted_at":"2022-12-29T06:41:07Z","abstract_excerpt":"The prediction of the Madden-Julian Oscillation (MJO), a massive tropical weather event with vast global socio-economic impacts, has been infamously difficult with physics-based weather prediction models. Here we construct a machine learning model using reservoir computing technique that forecasts the real-time multivariate MJO index (RMM), a macroscopic variable that represents the state of the MJO. The training data was refined by developing a novel filter that extracts the recurrency of MJO signals from the raw atmospheric data and selecting a suitable time-delay coordinate of the RMM. The "},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2301.01254","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/2301.01254/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":"2301.01254","created_at":"2026-07-05T05:30:22.374697+00:00"},{"alias_kind":"arxiv_version","alias_value":"2301.01254v1","created_at":"2026-07-05T05:30:22.374697+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2301.01254","created_at":"2026-07-05T05:30:22.374697+00:00"},{"alias_kind":"pith_short_12","alias_value":"NET3TUWNBV66","created_at":"2026-07-05T05:30:22.374697+00:00"},{"alias_kind":"pith_short_16","alias_value":"NET3TUWNBV66WLKZ","created_at":"2026-07-05T05:30:22.374697+00:00"},{"alias_kind":"pith_short_8","alias_value":"NET3TUWN","created_at":"2026-07-05T05:30:22.374697+00:00"}],"events":[],"event_summary":{},"paper_claims":[],"inbound_citations":{"count":1,"internal_anchor_count":1,"sample":[{"citing_arxiv_id":"2506.23195","citing_title":"Propagation of the Madden-Julian oscillation as a deterministic chaotic phenomenon","ref_index":53,"is_internal_anchor":true}]},"formal_canon":{"evidence_count":0,"sample":[],"anchors":[]},"links":{"html":"https://pith.science/pith/NET3TUWNBV66WLKZBJR4MPDJIZ","json":"https://pith.science/pith/NET3TUWNBV66WLKZBJR4MPDJIZ.json","graph_json":"https://pith.science/api/pith-number/NET3TUWNBV66WLKZBJR4MPDJIZ/graph.json","events_json":"https://pith.science/api/pith-number/NET3TUWNBV66WLKZBJR4MPDJIZ/events.json","paper":"https://pith.science/paper/NET3TUWN"},"agent_actions":{"view_html":"https://pith.science/pith/NET3TUWNBV66WLKZBJR4MPDJIZ","download_json":"https://pith.science/pith/NET3TUWNBV66WLKZBJR4MPDJIZ.json","view_paper":"https://pith.science/paper/NET3TUWN","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2301.01254&json=true","fetch_graph":"https://pith.science/api/pith-number/NET3TUWNBV66WLKZBJR4MPDJIZ/graph.json","fetch_events":"https://pith.science/api/pith-number/NET3TUWNBV66WLKZBJR4MPDJIZ/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/NET3TUWNBV66WLKZBJR4MPDJIZ/action/timestamp_anchor","attest_storage":"https://pith.science/pith/NET3TUWNBV66WLKZBJR4MPDJIZ/action/storage_attestation","attest_author":"https://pith.science/pith/NET3TUWNBV66WLKZBJR4MPDJIZ/action/author_attestation","sign_citation":"https://pith.science/pith/NET3TUWNBV66WLKZBJR4MPDJIZ/action/citation_signature","submit_replication":"https://pith.science/pith/NET3TUWNBV66WLKZBJR4MPDJIZ/action/replication_record"}},"created_at":"2026-07-05T05:30:22.374697+00:00","updated_at":"2026-07-05T05:30:22.374697+00:00"}