{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2026:LVXP4JO5OJOBVCPSD3IYAWNUV5","short_pith_number":"pith:LVXP4JO5","schema_version":"1.0","canonical_sha256":"5d6efe25dd725c1a89f21ed18059b4af5a89e2fd36f3e5bb8efb7f92573c5ef8","source":{"kind":"arxiv","id":"2607.16929","version":1},"attestation_state":"computed","paper":{"title":"C3DIR: A Deep Learning 3-Dimensional Cloud Property Retrieval Scheme for Passive Satellite Imagers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","eess.IV"],"primary_cat":"physics.ao-ph","authors_text":"Charles H. White, Imme Ebert-Uphoff, John M. Haynes, Yoo-Jeong Noh","submitted_at":"2026-07-18T18:46:49Z","abstract_excerpt":"We develop the Cloud 3-Dimensional Imager Retrieval (C3DIR), a deep learning model that estimates 3-D cloud properties for multiple passive satellite imagers trained to match retrievals from the Earth Cloud Aerosol and Radiation Explorer(EarthCARE) ACM-CAP product. This work is aimed towards moving AI/ML 3-D cloud algorithms closer towards operational use. C3DIR predicts the occurrence water content of ice, cloud liquid, and rain along the imager line-of-sight and uses a voxel-level collocation approach to account for the misaligned viewing geometries of passive imagers and active profiling in"},"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":"2607.16929","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"physics.ao-ph","submitted_at":"2026-07-18T18:46:49Z","cross_cats_sorted":["cs.CV","eess.IV"],"title_canon_sha256":"7168197524eb7e93acd157e2528979f18a707bb18ba79e2e0b8a71de0490ac33","abstract_canon_sha256":"58617b635ee87d0b97876429d68f9c3ca9f806a11ab829d933106dbb47bd835d"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-21T01:21:06.662857Z","signature_b64":"TiudXrSBy8HfB7X6xL4IzGmAbvm2em+21pHHkIK1fiMs3Mkyw9n2BT4QhfCSX+zCY8ILZAiUEQDBxAeNbOGCDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5d6efe25dd725c1a89f21ed18059b4af5a89e2fd36f3e5bb8efb7f92573c5ef8","last_reissued_at":"2026-07-21T01:21:06.662016Z","signature_status":"signed_v1","first_computed_at":"2026-07-21T01:21:06.662016Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"C3DIR: A Deep Learning 3-Dimensional Cloud Property Retrieval Scheme for Passive Satellite Imagers","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.CV","eess.IV"],"primary_cat":"physics.ao-ph","authors_text":"Charles H. White, Imme Ebert-Uphoff, John M. Haynes, Yoo-Jeong Noh","submitted_at":"2026-07-18T18:46:49Z","abstract_excerpt":"We develop the Cloud 3-Dimensional Imager Retrieval (C3DIR), a deep learning model that estimates 3-D cloud properties for multiple passive satellite imagers trained to match retrievals from the Earth Cloud Aerosol and Radiation Explorer(EarthCARE) ACM-CAP product. This work is aimed towards moving AI/ML 3-D cloud algorithms closer towards operational use. C3DIR predicts the occurrence water content of ice, cloud liquid, and rain along the imager line-of-sight and uses a voxel-level collocation approach to account for the misaligned viewing geometries of passive imagers and active profiling in"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2607.16929","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/2607.16929/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":"2607.16929","created_at":"2026-07-21T01:21:06.662445+00:00"},{"alias_kind":"arxiv_version","alias_value":"2607.16929v1","created_at":"2026-07-21T01:21:06.662445+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2607.16929","created_at":"2026-07-21T01:21:06.662445+00:00"},{"alias_kind":"pith_short_12","alias_value":"LVXP4JO5OJOB","created_at":"2026-07-21T01:21:06.662445+00:00"},{"alias_kind":"pith_short_16","alias_value":"LVXP4JO5OJOBVCPS","created_at":"2026-07-21T01:21:06.662445+00:00"},{"alias_kind":"pith_short_8","alias_value":"LVXP4JO5","created_at":"2026-07-21T01:21:06.662445+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/LVXP4JO5OJOBVCPSD3IYAWNUV5","json":"https://pith.science/pith/LVXP4JO5OJOBVCPSD3IYAWNUV5.json","graph_json":"https://pith.science/api/pith-number/LVXP4JO5OJOBVCPSD3IYAWNUV5/graph.json","events_json":"https://pith.science/api/pith-number/LVXP4JO5OJOBVCPSD3IYAWNUV5/events.json","paper":"https://pith.science/paper/LVXP4JO5"},"agent_actions":{"view_html":"https://pith.science/pith/LVXP4JO5OJOBVCPSD3IYAWNUV5","download_json":"https://pith.science/pith/LVXP4JO5OJOBVCPSD3IYAWNUV5.json","view_paper":"https://pith.science/paper/LVXP4JO5","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=2607.16929&json=true","fetch_graph":"https://pith.science/api/pith-number/LVXP4JO5OJOBVCPSD3IYAWNUV5/graph.json","fetch_events":"https://pith.science/api/pith-number/LVXP4JO5OJOBVCPSD3IYAWNUV5/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/LVXP4JO5OJOBVCPSD3IYAWNUV5/action/timestamp_anchor","attest_storage":"https://pith.science/pith/LVXP4JO5OJOBVCPSD3IYAWNUV5/action/storage_attestation","attest_author":"https://pith.science/pith/LVXP4JO5OJOBVCPSD3IYAWNUV5/action/author_attestation","sign_citation":"https://pith.science/pith/LVXP4JO5OJOBVCPSD3IYAWNUV5/action/citation_signature","submit_replication":"https://pith.science/pith/LVXP4JO5OJOBVCPSD3IYAWNUV5/action/replication_record"}},"created_at":"2026-07-21T01:21:06.662445+00:00","updated_at":"2026-07-21T01:21:06.662445+00:00"}