{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2026:2IFIJ3ZBXK4PSY6GNSZVTHWUFK","short_pith_number":"pith:2IFIJ3ZB","canonical_record":{"source":{"id":"2605.11639","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.ao-ph","submitted_at":"2026-05-12T07:00:50Z","cross_cats_sorted":["math.ST","stat.TH"],"title_canon_sha256":"8c9f830321f8f976e8ddd0724df9b6d15682faaa833cd42499fa1861dc1385ae","abstract_canon_sha256":"19d6ce163ecb2104221b4339e3cb48c40fe0342fa206a5541a4bde70b0f5683b"},"schema_version":"1.0"},"canonical_sha256":"d20a84ef21bab8f963c66cb3599ed42a9546ff9c123eae76e0dcfdf503eea00f","source":{"kind":"arxiv","id":"2605.11639","version":2},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.11639","created_at":"2026-05-25T02:01:23Z"},{"alias_kind":"arxiv_version","alias_value":"2605.11639v2","created_at":"2026-05-25T02:01:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.11639","created_at":"2026-05-25T02:01:23Z"},{"alias_kind":"pith_short_12","alias_value":"2IFIJ3ZBXK4P","created_at":"2026-05-25T02:01:23Z"},{"alias_kind":"pith_short_16","alias_value":"2IFIJ3ZBXK4PSY6G","created_at":"2026-05-25T02:01:23Z"},{"alias_kind":"pith_short_8","alias_value":"2IFIJ3ZB","created_at":"2026-05-25T02:01:23Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2026:2IFIJ3ZBXK4PSY6GNSZVTHWUFK","target":"record","payload":{"canonical_record":{"source":{"id":"2605.11639","kind":"arxiv","version":2},"metadata":{"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.ao-ph","submitted_at":"2026-05-12T07:00:50Z","cross_cats_sorted":["math.ST","stat.TH"],"title_canon_sha256":"8c9f830321f8f976e8ddd0724df9b6d15682faaa833cd42499fa1861dc1385ae","abstract_canon_sha256":"19d6ce163ecb2104221b4339e3cb48c40fe0342fa206a5541a4bde70b0f5683b"},"schema_version":"1.0"},"canonical_sha256":"d20a84ef21bab8f963c66cb3599ed42a9546ff9c123eae76e0dcfdf503eea00f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-25T02:01:23.536217Z","signature_b64":"waBc6ImMYk5va0a0R9lecwJC6rs4OM0G0GW+ibbsL0nQW5Dlq0AP3gJPT3UDNyGVPOljMNoTqo2GYdvEobPqAQ==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"d20a84ef21bab8f963c66cb3599ed42a9546ff9c123eae76e0dcfdf503eea00f","last_reissued_at":"2026-05-25T02:01:23.535460Z","signature_status":"signed_v1","first_computed_at":"2026-05-25T02:01:23.535460Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2605.11639","source_version":2,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-25T02:01:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"SG/EbfNJAG/u/TW23Bi1tSM+JVCU8TifXoad1/qe3Cyo5u1ppIA5Q1M4Mw5VFxVCBGuCFM7FnXn5u4xhx1nfCA==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T04:48:16.136656Z"},"content_sha256":"9ee8a22d63112c10190868bee22519efd167c9fbf86d2a2035058fb05d8bc78c","schema_version":"1.0","event_id":"sha256:9ee8a22d63112c10190868bee22519efd167c9fbf86d2a2035058fb05d8bc78c"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2026:2IFIJ3ZBXK4PSY6GNSZVTHWUFK","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Enabling High-Accuracy Data Assimilation with Limited Ensembles via Machine Learning-Based Covariance Correction","license":"http://creativecommons.org/licenses/by/4.0/","headline":"A multilayer perceptron predicts the covariance gap between small and large ensembles and scales the small-ensemble matrix to raise EnKF analysis accuracy.","cross_cats":["math.ST","stat.TH"],"primary_cat":"physics.ao-ph","authors_text":"Guangyao Wang, Li Zhao, Seungnam Kim, Zeng Liu, Zhaokuan Lu, Zhilin Li, Zhou Yao","submitted_at":"2026-05-12T07:00:50Z","abstract_excerpt":"Data assimilation (DA) integrates numerical model forecasts with observations to achieve the optimal state estimation. Ensemble-based methods, such as the ensemble Kalman filter (EnKF), are widely used for state estimation for high-dimensional and nonlinear dynamic systems. However, their performance strongly depends on the ensemble size, therefore causing a tradeoff problem between analysis accuracy and computational cost. To address this problem, this study presents a machine learning-based EnKF framework that maintains high accuracy with a relatively small ensemble size. Specifically, a mul"},"claims":{"count":4,"items":[{"kind":"strongest_claim","text":"the proposed algorithm can significantly outperform the standard EnKF with the same limited ensemble size, by achieving notably higher analysis accuracy while remaining computationally efficient","source":"verdict.strongest_claim","status":"machine_extracted","claim_id":"C1","attestation":"unclaimed"},{"kind":"weakest_assumption","text":"the latter being assumed to be an accurate approximation of the underlying truth (large-ensemble covariance treated as ground truth for training the MLP)","source":"verdict.weakest_assumption","status":"machine_extracted","claim_id":"C2","attestation":"unclaimed"},{"kind":"one_line_summary","text":"An MLP predicts the covariance difference between limited and large ensembles and corrects the EnKF forecast covariance via element-wise scaling, yielding higher accuracy than standard EnKF on Lorenz-63 and Lorenz-96.","source":"verdict.one_line_summary","status":"machine_extracted","claim_id":"C3","attestation":"unclaimed"},{"kind":"headline","text":"A multilayer perceptron predicts the covariance gap between small and large ensembles and scales the small-ensemble matrix to raise EnKF analysis accuracy.","source":"verdict.pith_extraction.headline","status":"machine_extracted","claim_id":"C4","attestation":"unclaimed"}],"snapshot_sha256":"63591b9e720a02957730319e9683e30c1ae31629fb061ace1f81b8af1acc1d91"},"source":{"id":"2605.11639","kind":"arxiv","version":2},"verdict":{"id":"1eb0e814-6fb9-471b-9eb7-c912f5804c85","model_set":{"reader":"grok-4.3"},"created_at":"2026-05-13T01:24:27.315521Z","strongest_claim":"the proposed algorithm can significantly outperform the standard EnKF with the same limited ensemble size, by achieving notably higher analysis accuracy while remaining computationally efficient","one_line_summary":"An MLP predicts the covariance difference between limited and large ensembles and corrects the EnKF forecast covariance via element-wise scaling, yielding higher accuracy than standard EnKF on Lorenz-63 and Lorenz-96.","pipeline_version":"pith-pipeline@v0.9.0","weakest_assumption":"the latter being assumed to be an accurate approximation of the underlying truth (large-ensemble covariance treated as ground truth for training the MLP)","pith_extraction_headline":"A multilayer perceptron predicts the covariance gap between small and large ensembles and scales the small-ensemble matrix to raise EnKF analysis accuracy."},"integrity":{"clean":true,"summary":{"advisory":0,"critical":0,"by_detector":{},"informational":0},"endpoint":"/pith/2605.11639/integrity.json","findings":[],"available":true,"detectors_run":[{"name":"doi_title_agreement","ran_at":"2026-05-21T00:31:32.061250Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"doi_compliance","ran_at":"2026-05-20T14:16:33.762327Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"claim_evidence","ran_at":"2026-05-20T04:02:00.378616Z","status":"completed","version":"1.0.0","findings_count":0},{"name":"ai_meta_artifact","ran_at":"2026-05-19T11:40:35.683866Z","status":"completed","version":"1.0.0","findings_count":0}],"snapshot_sha256":"61c411e766c10e64becfbdd7276edf160f5fc679cf2bd39fd5ec53dd5927e2a8"},"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"},"verdict_id":"1eb0e814-6fb9-471b-9eb7-c912f5804c85"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-05-25T02:01:23Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"43o06fKz2rVoaFdIBfNZdi9Gh4b31rBy6/gHfq5Whmm5zcKXb3zkTd4OtSgs8JrpO37aMSUYh804YotNLsjLAg==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-06-03T04:48:16.137158Z"},"content_sha256":"43e6379ad581139ea665c9b042d6ec44d1e7a10eb9d0300223b73a36d711204f","schema_version":"1.0","event_id":"sha256:43e6379ad581139ea665c9b042d6ec44d1e7a10eb9d0300223b73a36d711204f"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/2IFIJ3ZBXK4PSY6GNSZVTHWUFK/bundle.json","state_url":"https://pith.science/pith/2IFIJ3ZBXK4PSY6GNSZVTHWUFK/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/2IFIJ3ZBXK4PSY6GNSZVTHWUFK/bundle.json","status":"primary"}],"public_keys":[{"key_id":"pith-v1-2026-05","algorithm":"ed25519","format":"raw","public_key_b64":"stVStoiQhXFxp4s2pdzPNoqVNBMojDU/fJ2db5S3CbM=","public_key_hex":"b2d552b68890857171a78b36a5dccf368a953413288c353f7c9d9d6f94b709b3","fingerprint_sha256_b32_first128bits":"RVFV5Z2OI2J3ZUO7ERDEBCYNKS","fingerprint_sha256_hex":"8d4b5ee74e4693bcd1df2446408b0d54","rotates_at":null,"url":"https://pith.science/pith-signing-key.json","notes":"Pith uses this Ed25519 key to sign canonical record SHA-256 digests. Verify with: ed25519_verify(public_key, message=canonical_sha256_bytes, signature=base64decode(signature_b64))."}],"merge_version":"pith-open-graph-merge-v1","built_at":"2026-06-03T04:48:16Z","links":{"resolver":"https://pith.science/pith/2IFIJ3ZBXK4PSY6GNSZVTHWUFK","bundle":"https://pith.science/pith/2IFIJ3ZBXK4PSY6GNSZVTHWUFK/bundle.json","state":"https://pith.science/pith/2IFIJ3ZBXK4PSY6GNSZVTHWUFK/state.json","well_known_bundle":"https://pith.science/.well-known/pith/2IFIJ3ZBXK4PSY6GNSZVTHWUFK/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2026:2IFIJ3ZBXK4PSY6GNSZVTHWUFK","merge_version":"pith-open-graph-merge-v1","event_count":2,"valid_event_count":2,"invalid_event_count":0,"equivocation_count":0,"current":{"canonical_record":{"metadata":{"abstract_canon_sha256":"19d6ce163ecb2104221b4339e3cb48c40fe0342fa206a5541a4bde70b0f5683b","cross_cats_sorted":["math.ST","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.ao-ph","submitted_at":"2026-05-12T07:00:50Z","title_canon_sha256":"8c9f830321f8f976e8ddd0724df9b6d15682faaa833cd42499fa1861dc1385ae"},"schema_version":"1.0","source":{"id":"2605.11639","kind":"arxiv","version":2}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2605.11639","created_at":"2026-05-25T02:01:23Z"},{"alias_kind":"arxiv_version","alias_value":"2605.11639v2","created_at":"2026-05-25T02:01:23Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2605.11639","created_at":"2026-05-25T02:01:23Z"},{"alias_kind":"pith_short_12","alias_value":"2IFIJ3ZBXK4P","created_at":"2026-05-25T02:01:23Z"},{"alias_kind":"pith_short_16","alias_value":"2IFIJ3ZBXK4PSY6G","created_at":"2026-05-25T02:01:23Z"},{"alias_kind":"pith_short_8","alias_value":"2IFIJ3ZB","created_at":"2026-05-25T02:01:23Z"}],"graph_snapshots":[{"event_id":"sha256:43e6379ad581139ea665c9b042d6ec44d1e7a10eb9d0300223b73a36d711204f","target":"graph","created_at":"2026-05-25T02:01:23Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"graph_snapshot":{"author_claims":{"count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57","strong_count":0},"builder_version":"pith-number-builder-2026-05-17-v1","claims":{"count":4,"items":[{"attestation":"unclaimed","claim_id":"C1","kind":"strongest_claim","source":"verdict.strongest_claim","status":"machine_extracted","text":"the proposed algorithm can significantly outperform the standard EnKF with the same limited ensemble size, by achieving notably higher analysis accuracy while remaining computationally efficient"},{"attestation":"unclaimed","claim_id":"C2","kind":"weakest_assumption","source":"verdict.weakest_assumption","status":"machine_extracted","text":"the latter being assumed to be an accurate approximation of the underlying truth (large-ensemble covariance treated as ground truth for training the MLP)"},{"attestation":"unclaimed","claim_id":"C3","kind":"one_line_summary","source":"verdict.one_line_summary","status":"machine_extracted","text":"An MLP predicts the covariance difference between limited and large ensembles and corrects the EnKF forecast covariance via element-wise scaling, yielding higher accuracy than standard EnKF on Lorenz-63 and Lorenz-96."},{"attestation":"unclaimed","claim_id":"C4","kind":"headline","source":"verdict.pith_extraction.headline","status":"machine_extracted","text":"A multilayer perceptron predicts the covariance gap between small and large ensembles and scales the small-ensemble matrix to raise EnKF analysis accuracy."}],"snapshot_sha256":"63591b9e720a02957730319e9683e30c1ae31629fb061ace1f81b8af1acc1d91"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[{"findings_count":0,"name":"doi_title_agreement","ran_at":"2026-05-21T00:31:32.061250Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"doi_compliance","ran_at":"2026-05-20T14:16:33.762327Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"claim_evidence","ran_at":"2026-05-20T04:02:00.378616Z","status":"completed","version":"1.0.0"},{"findings_count":0,"name":"ai_meta_artifact","ran_at":"2026-05-19T11:40:35.683866Z","status":"completed","version":"1.0.0"}],"endpoint":"/pith/2605.11639/integrity.json","findings":[],"snapshot_sha256":"61c411e766c10e64becfbdd7276edf160f5fc679cf2bd39fd5ec53dd5927e2a8","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Data assimilation (DA) integrates numerical model forecasts with observations to achieve the optimal state estimation. Ensemble-based methods, such as the ensemble Kalman filter (EnKF), are widely used for state estimation for high-dimensional and nonlinear dynamic systems. However, their performance strongly depends on the ensemble size, therefore causing a tradeoff problem between analysis accuracy and computational cost. To address this problem, this study presents a machine learning-based EnKF framework that maintains high accuracy with a relatively small ensemble size. Specifically, a mul","authors_text":"Guangyao Wang, Li Zhao, Seungnam Kim, Zeng Liu, Zhaokuan Lu, Zhilin Li, Zhou Yao","cross_cats":["math.ST","stat.TH"],"headline":"A multilayer perceptron predicts the covariance gap between small and large ensembles and scales the small-ensemble matrix to raise EnKF analysis accuracy.","license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.ao-ph","submitted_at":"2026-05-12T07:00:50Z","title":"Enabling High-Accuracy Data Assimilation with Limited Ensembles via Machine Learning-Based Covariance Correction"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2605.11639","kind":"arxiv","version":2},"verdict":{"created_at":"2026-05-13T01:24:27.315521Z","id":"1eb0e814-6fb9-471b-9eb7-c912f5804c85","model_set":{"reader":"grok-4.3"},"one_line_summary":"An MLP predicts the covariance difference between limited and large ensembles and corrects the EnKF forecast covariance via element-wise scaling, yielding higher accuracy than standard EnKF on Lorenz-63 and Lorenz-96.","pipeline_version":"pith-pipeline@v0.9.0","pith_extraction_headline":"A multilayer perceptron predicts the covariance gap between small and large ensembles and scales the small-ensemble matrix to raise EnKF analysis accuracy.","strongest_claim":"the proposed algorithm can significantly outperform the standard EnKF with the same limited ensemble size, by achieving notably higher analysis accuracy while remaining computationally efficient","weakest_assumption":"the latter being assumed to be an accurate approximation of the underlying truth (large-ensemble covariance treated as ground truth for training the MLP)"}},"verdict_id":"1eb0e814-6fb9-471b-9eb7-c912f5804c85"}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:9ee8a22d63112c10190868bee22519efd167c9fbf86d2a2035058fb05d8bc78c","target":"record","created_at":"2026-05-25T02:01:23Z","signer":{"key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signer_id":"pith.science","signer_type":"pith_registry"},"payload":{"attestation_state":"computed","canonical_record":{"metadata":{"abstract_canon_sha256":"19d6ce163ecb2104221b4339e3cb48c40fe0342fa206a5541a4bde70b0f5683b","cross_cats_sorted":["math.ST","stat.TH"],"license":"http://creativecommons.org/licenses/by/4.0/","primary_cat":"physics.ao-ph","submitted_at":"2026-05-12T07:00:50Z","title_canon_sha256":"8c9f830321f8f976e8ddd0724df9b6d15682faaa833cd42499fa1861dc1385ae"},"schema_version":"1.0","source":{"id":"2605.11639","kind":"arxiv","version":2}},"canonical_sha256":"d20a84ef21bab8f963c66cb3599ed42a9546ff9c123eae76e0dcfdf503eea00f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"d20a84ef21bab8f963c66cb3599ed42a9546ff9c123eae76e0dcfdf503eea00f","first_computed_at":"2026-05-25T02:01:23.535460Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-05-25T02:01:23.535460Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"waBc6ImMYk5va0a0R9lecwJC6rs4OM0G0GW+ibbsL0nQW5Dlq0AP3gJPT3UDNyGVPOljMNoTqo2GYdvEobPqAQ==","signature_status":"signed_v1","signed_at":"2026-05-25T02:01:23.536217Z","signed_message":"canonical_sha256_bytes"},"source_id":"2605.11639","source_kind":"arxiv","source_version":2}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:9ee8a22d63112c10190868bee22519efd167c9fbf86d2a2035058fb05d8bc78c","sha256:43e6379ad581139ea665c9b042d6ec44d1e7a10eb9d0300223b73a36d711204f"],"state_sha256":"4669fa669650e7e6f189521eebe09f5cb70527a2501ca0f82868f4273144b67a"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"JNqjgKvts32/b1RpKvoaAeqfO5X9V6qJpe3PNAVNzTbwuee3DyHJuAP3pkb4XSblM93SDU7Qs/qOwqKej9XzAA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-06-03T04:48:16.139590Z","bundle_sha256":"6c6c4007081178638f6bc05a16c2b2ea90be134c44afc0edd5a8857f778c4d5c"}}