{"bundle_type":"pith_open_graph_bundle","bundle_version":"1.0","pith_number":"pith:2022:GTUJFPS2YFMJ34T6AAYJTNVNJ2","short_pith_number":"pith:GTUJFPS2","canonical_record":{"source":{"id":"2203.12297","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2022-03-23T09:45:12Z","cross_cats_sorted":["cs.LG","stat.AP"],"title_canon_sha256":"d6ab6ebf1ab800e3bf53c6204a01a9a68a3e6b2daf19f726a369adbb3c2cb377","abstract_canon_sha256":"21ab4d11ca1b49a5c769410ad8742658ee6ee91678e2a5420600d52a9a804a76"},"schema_version":"1.0"},"canonical_sha256":"34e892be5ac1589df27e003099b6ad4e88bfbd09ac953dcab7e4c18f69b38a9f","source":{"kind":"arxiv","id":"2203.12297","version":1},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.12297","created_at":"2026-07-05T04:07:57Z"},{"alias_kind":"arxiv_version","alias_value":"2203.12297v1","created_at":"2026-07-05T04:07:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.12297","created_at":"2026-07-05T04:07:57Z"},{"alias_kind":"pith_short_12","alias_value":"GTUJFPS2YFMJ","created_at":"2026-07-05T04:07:57Z"},{"alias_kind":"pith_short_16","alias_value":"GTUJFPS2YFMJ34T6","created_at":"2026-07-05T04:07:57Z"},{"alias_kind":"pith_short_8","alias_value":"GTUJFPS2","created_at":"2026-07-05T04:07:57Z"}],"events":[{"event_type":"record_created","subject_pith_number":"pith:2022:GTUJFPS2YFMJ34T6AAYJTNVNJ2","target":"record","payload":{"canonical_record":{"source":{"id":"2203.12297","kind":"arxiv","version":1},"metadata":{"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2022-03-23T09:45:12Z","cross_cats_sorted":["cs.LG","stat.AP"],"title_canon_sha256":"d6ab6ebf1ab800e3bf53c6204a01a9a68a3e6b2daf19f726a369adbb3c2cb377","abstract_canon_sha256":"21ab4d11ca1b49a5c769410ad8742658ee6ee91678e2a5420600d52a9a804a76"},"schema_version":"1.0"},"canonical_sha256":"34e892be5ac1589df27e003099b6ad4e88bfbd09ac953dcab7e4c18f69b38a9f","receipt":{"kind":"pith_receipt","key_id":"pith-v1-2026-05","algorithm":"ed25519","signed_at":"2026-07-05T04:07:57.988279Z","signature_b64":"O7pOKs5C+M3HHXnNUk2JSHBYnWWFGFYrQOIYz1hN651CdCfuqVKueu3klqm7rO1mT35JEFt3OJDZmBzUWpWWDA==","signed_message":"canonical_sha256_bytes","builder_version":"pith-number-builder-2026-05-17-v1","receipt_version":"0.3","canonical_sha256":"34e892be5ac1589df27e003099b6ad4e88bfbd09ac953dcab7e4c18f69b38a9f","last_reissued_at":"2026-07-05T04:07:57.987834Z","signature_status":"signed_v1","first_computed_at":"2026-07-05T04:07:57.987834Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"source_kind":"arxiv","source_id":"2203.12297","source_version":1,"attestation_state":"computed"},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:07:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"I973yx6VksQKyujbo1qr503x9vui+F83zqLmWWkKGSL1lhg0Dt/64HQPdwOCwAQbPRztCrB/yeZmIzmAjQFKBQ==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T18:56:23.519968Z"},"content_sha256":"88f3ce9ad5ed3f4474736cb3671f67f629610d43fe36a481c5f3d4a76311fe25","schema_version":"1.0","event_id":"sha256:88f3ce9ad5ed3f4474736cb3671f67f629610d43fe36a481c5f3d4a76311fe25"},{"event_type":"graph_snapshot","subject_pith_number":"pith:2022:GTUJFPS2YFMJ34T6AAYJTNVNJ2","target":"graph","payload":{"graph_snapshot":{"paper":{"title":"Increasing the accuracy and resolution of precipitation forecasts using deep generative models","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","headline":"","cross_cats":["cs.LG","stat.AP"],"primary_cat":"stat.ML","authors_text":"Ilan Price, Stephan Rasp","submitted_at":"2022-03-23T09:45:12Z","abstract_excerpt":"Accurately forecasting extreme rainfall is notoriously difficult, but is also ever more crucial for society as climate change increases the frequency of such extremes. Global numerical weather prediction models often fail to capture extremes, and are produced at too low a resolution to be actionable, while regional, high-resolution models are hugely expensive both in computation and labour. In this paper we explore the use of deep generative models to simultaneously correct and downscale (super-resolve) global ensemble forecasts over the Continental US. Specifically, using fine-grained radar o"},"claims":{"count":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.12297","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/2203.12297/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"},"verdict_id":null},"signer":{"signer_id":"pith.science","signer_type":"pith_registry","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54"},"created_at":"2026-07-05T04:07:57Z","supersedes":[],"prev_event":null,"signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"dVGyHKXKCdikQef3AfvKap1HYDouRgDaG9aW4MJk86k2A/1u9vj83ImKck53DjRkFnf4peN/PQ/xBFXxFnssBw==","signed_message":"open_graph_event_sha256_bytes","signed_at":"2026-08-13T18:56:23.520884Z"},"content_sha256":"f192378301155c26ef2ae95f78223d6c0a99620e2ebe3fe25161c5e284a94112","schema_version":"1.0","event_id":"sha256:f192378301155c26ef2ae95f78223d6c0a99620e2ebe3fe25161c5e284a94112"}],"timestamp_proofs":[],"mirror_hints":[{"mirror_type":"https","name":"Pith Resolver","base_url":"https://pith.science","bundle_url":"https://pith.science/pith/GTUJFPS2YFMJ34T6AAYJTNVNJ2/bundle.json","state_url":"https://pith.science/pith/GTUJFPS2YFMJ34T6AAYJTNVNJ2/state.json","well_known_bundle_url":"https://pith.science/.well-known/pith/GTUJFPS2YFMJ34T6AAYJTNVNJ2/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":"2026-08-13T18:56:23Z","links":{"resolver":"https://pith.science/pith/GTUJFPS2YFMJ34T6AAYJTNVNJ2","bundle":"https://pith.science/pith/GTUJFPS2YFMJ34T6AAYJTNVNJ2/bundle.json","state":"https://pith.science/pith/GTUJFPS2YFMJ34T6AAYJTNVNJ2/state.json","well_known_bundle":"https://pith.science/.well-known/pith/GTUJFPS2YFMJ34T6AAYJTNVNJ2/bundle.json"},"state":{"state_type":"pith_open_graph_state","state_version":"1.0","pith_number":"pith:2022:GTUJFPS2YFMJ34T6AAYJTNVNJ2","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":"21ab4d11ca1b49a5c769410ad8742658ee6ee91678e2a5420600d52a9a804a76","cross_cats_sorted":["cs.LG","stat.AP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2022-03-23T09:45:12Z","title_canon_sha256":"d6ab6ebf1ab800e3bf53c6204a01a9a68a3e6b2daf19f726a369adbb3c2cb377"},"schema_version":"1.0","source":{"id":"2203.12297","kind":"arxiv","version":1}},"source_aliases":[{"alias_kind":"arxiv","alias_value":"2203.12297","created_at":"2026-07-05T04:07:57Z"},{"alias_kind":"arxiv_version","alias_value":"2203.12297v1","created_at":"2026-07-05T04:07:57Z"},{"alias_kind":"doi","alias_value":"10.48550/arxiv.2203.12297","created_at":"2026-07-05T04:07:57Z"},{"alias_kind":"pith_short_12","alias_value":"GTUJFPS2YFMJ","created_at":"2026-07-05T04:07:57Z"},{"alias_kind":"pith_short_16","alias_value":"GTUJFPS2YFMJ34T6","created_at":"2026-07-05T04:07:57Z"},{"alias_kind":"pith_short_8","alias_value":"GTUJFPS2","created_at":"2026-07-05T04:07:57Z"}],"graph_snapshots":[{"event_id":"sha256:f192378301155c26ef2ae95f78223d6c0a99620e2ebe3fe25161c5e284a94112","target":"graph","created_at":"2026-07-05T04:07:57Z","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":0,"items":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"formal_canon":{"evidence_count":0,"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"integrity":{"available":true,"clean":true,"detectors_run":[],"endpoint":"/pith/2203.12297/integrity.json","findings":[],"snapshot_sha256":"c28c3603d3b5d939e8dc4c7e95fa8dfce3d595e45f758748cecf8e644a296938","summary":{"advisory":0,"by_detector":{},"critical":0,"informational":0}},"paper":{"abstract_excerpt":"Accurately forecasting extreme rainfall is notoriously difficult, but is also ever more crucial for society as climate change increases the frequency of such extremes. Global numerical weather prediction models often fail to capture extremes, and are produced at too low a resolution to be actionable, while regional, high-resolution models are hugely expensive both in computation and labour. In this paper we explore the use of deep generative models to simultaneously correct and downscale (super-resolve) global ensemble forecasts over the Continental US. Specifically, using fine-grained radar o","authors_text":"Ilan Price, Stephan Rasp","cross_cats":["cs.LG","stat.AP"],"headline":"","license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2022-03-23T09:45:12Z","title":"Increasing the accuracy and resolution of precipitation forecasts using deep generative models"},"references":{"count":0,"internal_anchors":0,"resolved_work":0,"sample":[],"snapshot_sha256":"258153158e38e3291e3d48162225fcdb2d5a3ed65a07baac614ab91432fd4f57"},"source":{"id":"2203.12297","kind":"arxiv","version":1},"verdict":{"created_at":null,"id":null,"model_set":{},"one_line_summary":"","pipeline_version":null,"pith_extraction_headline":"","strongest_claim":"","weakest_assumption":""}},"verdict_id":null}}],"author_attestations":[],"timestamp_anchors":[],"storage_attestations":[],"citation_signatures":[],"replication_records":[],"corrections":[],"mirror_hints":[],"record_created":{"event_id":"sha256:88f3ce9ad5ed3f4474736cb3671f67f629610d43fe36a481c5f3d4a76311fe25","target":"record","created_at":"2026-07-05T04:07:57Z","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":"21ab4d11ca1b49a5c769410ad8742658ee6ee91678e2a5420600d52a9a804a76","cross_cats_sorted":["cs.LG","stat.AP"],"license":"http://arxiv.org/licenses/nonexclusive-distrib/1.0/","primary_cat":"stat.ML","submitted_at":"2022-03-23T09:45:12Z","title_canon_sha256":"d6ab6ebf1ab800e3bf53c6204a01a9a68a3e6b2daf19f726a369adbb3c2cb377"},"schema_version":"1.0","source":{"id":"2203.12297","kind":"arxiv","version":1}},"canonical_sha256":"34e892be5ac1589df27e003099b6ad4e88bfbd09ac953dcab7e4c18f69b38a9f","receipt":{"algorithm":"ed25519","builder_version":"pith-number-builder-2026-05-17-v1","canonical_sha256":"34e892be5ac1589df27e003099b6ad4e88bfbd09ac953dcab7e4c18f69b38a9f","first_computed_at":"2026-07-05T04:07:57.987834Z","key_id":"pith-v1-2026-05","kind":"pith_receipt","last_reissued_at":"2026-07-05T04:07:57.987834Z","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","receipt_version":"0.3","signature_b64":"O7pOKs5C+M3HHXnNUk2JSHBYnWWFGFYrQOIYz1hN651CdCfuqVKueu3klqm7rO1mT35JEFt3OJDZmBzUWpWWDA==","signature_status":"signed_v1","signed_at":"2026-07-05T04:07:57.988279Z","signed_message":"canonical_sha256_bytes"},"source_id":"2203.12297","source_kind":"arxiv","source_version":1}}},"equivocations":[],"invalid_events":[],"applied_event_ids":["sha256:88f3ce9ad5ed3f4474736cb3671f67f629610d43fe36a481c5f3d4a76311fe25","sha256:f192378301155c26ef2ae95f78223d6c0a99620e2ebe3fe25161c5e284a94112"],"state_sha256":"ccaea022d521f788ef9a85badcbe8243d8fc9ef7a6454849d06003ab4f2c7313"},"bundle_signature":{"signature_status":"signed_v1","algorithm":"ed25519","key_id":"pith-v1-2026-05","public_key_fingerprint":"8d4b5ee74e4693bcd1df2446408b0d54","signature_b64":"nVgmC4trCEieRBUv3Ngc+ja5xQ0PZq9g26G6n4WuWY6NItqHkJt2qfmVsxw2zJOG2ZuByMmqJVVobQNxvtzOBA==","signed_message":"bundle_sha256_bytes","signed_at":"2026-08-13T18:56:23.532901Z","bundle_sha256":"8f7e4223ffa26451f7aaeb81cb6806b2dd1c9180bd7bd709618890202f1626fb"}}