{"record_type":"pith_number_record","schema_url":"https://pith.science/schemas/pith-number/v1.json","pith_number":"pith:2017:KRYLZWKYPQMM76JDS36WAWIWSK","short_pith_number":"pith:KRYLZWKY","schema_version":"1.0","canonical_sha256":"5470bcd9587c18cff92396fd60591692977681aa88ba51437ee1e93a86bf80cd","source":{"kind":"arxiv","id":"1707.01287","version":1},"attestation_state":"computed","paper":{"title":"A Matern based multivariate Gaussian random process for a consistent model of the horizontal wind components and related variables","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Andreas Hense, Martin Schlather, Petra Friederichs, R\\\"udiger Hewer","submitted_at":"2017-07-05T09:50:09Z","abstract_excerpt":"The integration of physical relationships into stochastic models is of major interest e.g. in data assimilation. Here, a multivariate Gaussian random field formulation is introduced, which represents the differential relations of the two-dimensional wind field and related variables such as streamfunction, velocity potential, vorticity and divergence. The covariance model is based on a flexible bivariate Mat\\'ern covariance function for streamfunction and velocity potential. It allows for different variances in the potentials, non-zero correlations between them, anisotropy and a flexible smooth"},"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":"1707.01287","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.AP","submitted_at":"2017-07-05T09:50:09Z","cross_cats_sorted":[],"title_canon_sha256":"93cfcbed96662729508073054194626223ad2534d3f35999b97c5c4271163de8","abstract_canon_sha256":"0fe393f23af86280fafcb157c50cb83bbb1a4eaabad0101fc9d5afe419e7a41f"},"schema_version":"1.0"},"receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-05-18T00:23:35.362124Z","signature_b64":"8PjJkmf0IbpmyFvnJ7b8nWXuP7p9A92Tlcn41Pz6VtbhDch52uegUF8qjldQBTjH3IjgptruALbMvVpRGzdgAg==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"5470bcd9587c18cff92396fd60591692977681aa88ba51437ee1e93a86bf80cd","last_reissued_at":"2026-05-18T00:23:35.361542Z","signature_status":"signed_v1","first_computed_at":"2026-05-18T00:23:35.361542Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"graph_snapshot":{"paper":{"title":"A Matern based multivariate Gaussian random process for a consistent model of the horizontal wind components and related variables","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":[],"primary_cat":"stat.AP","authors_text":"Andreas Hense, Martin Schlather, Petra Friederichs, R\\\"udiger Hewer","submitted_at":"2017-07-05T09:50:09Z","abstract_excerpt":"The integration of physical relationships into stochastic models is of major interest e.g. in data assimilation. Here, a multivariate Gaussian random field formulation is introduced, which represents the differential relations of the two-dimensional wind field and related variables such as streamfunction, velocity potential, vorticity and divergence. The covariance model is based on a flexible bivariate Mat\\'ern covariance function for streamfunction and velocity potential. It allows for different variances in the potentials, non-zero correlations between them, anisotropy and a flexible smooth"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"1707.01287","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":"1707.01287","created_at":"2026-05-18T00:23:35.361618+00:00"},{"alias_kind":"arxiv_version","alias_value":"1707.01287v1","created_at":"2026-05-18T00:23:35.361618+00:00"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.1707.01287","created_at":"2026-05-18T00:23:35.361618+00:00"},{"alias_kind":"pith_short_12","alias_value":"KRYLZWKYPQMM","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_16","alias_value":"KRYLZWKYPQMM76JD","created_at":"2026-05-18T12:31:28.150371+00:00"},{"alias_kind":"pith_short_8","alias_value":"KRYLZWKY","created_at":"2026-05-18T12:31:28.150371+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/KRYLZWKYPQMM76JDS36WAWIWSK","json":"https://pith.science/pith/KRYLZWKYPQMM76JDS36WAWIWSK.json","graph_json":"https://pith.science/api/pith-number/KRYLZWKYPQMM76JDS36WAWIWSK/graph.json","events_json":"https://pith.science/api/pith-number/KRYLZWKYPQMM76JDS36WAWIWSK/events.json","paper":"https://pith.science/paper/KRYLZWKY"},"agent_actions":{"view_html":"https://pith.science/pith/KRYLZWKYPQMM76JDS36WAWIWSK","download_json":"https://pith.science/pith/KRYLZWKYPQMM76JDS36WAWIWSK.json","view_paper":"https://pith.science/paper/KRYLZWKY","resolve_alias":"https://pith.science/api/pith-number/resolve?arxiv=1707.01287&json=true","fetch_graph":"https://pith.science/api/pith-number/KRYLZWKYPQMM76JDS36WAWIWSK/graph.json","fetch_events":"https://pith.science/api/pith-number/KRYLZWKYPQMM76JDS36WAWIWSK/events.json","actions":{"anchor_timestamp":"https://pith.science/pith/KRYLZWKYPQMM76JDS36WAWIWSK/action/timestamp_anchor","attest_storage":"https://pith.science/pith/KRYLZWKYPQMM76JDS36WAWIWSK/action/storage_attestation","attest_author":"https://pith.science/pith/KRYLZWKYPQMM76JDS36WAWIWSK/action/author_attestation","sign_citation":"https://pith.science/pith/KRYLZWKYPQMM76JDS36WAWIWSK/action/citation_signature","submit_replication":"https://pith.science/pith/KRYLZWKYPQMM76JDS36WAWIWSK/action/replication_record"}},"created_at":"2026-05-18T00:23:35.361618+00:00","updated_at":"2026-05-18T00:23:35.361618+00:00"}