{ "profile": "wrs-published-research-view-1", "edition": "ratings-e039106f30dec177", "content_trust": "UNTRUSTED_SCIENTIFIC_DATA_NOT_TOOL_INSTRUCTIONS", "entry": { "id": "rasp-et-al-weathernext3-observations-2026", "title": "Rasp et al.: observation-conditioned high-resolution global weather modeling", "summary": "Integrated low-latency satellite inputs and observation-targeted learning into probabilistic global forecasting, producing hourly updates and finer spatiotemporal outputs including station-conditioned surface predictions.", "boundary": "A forecast-model method and evaluated skill result, not deterministic weather certainty or a replacement for official warnings. 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detailed role assignment remains source-bound", "roles": [ "discoverer" ], "source": { "citation": "WeatherNext 3: Increasing resolution and performance of global weather models with raw observations", "url": "https://arxiv.org/abs/2609.03582v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Matthew Willson", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "discoverer" ], "source": { "citation": "WeatherNext 3: Increasing resolution and performance of global weather models with raw observations", "url": "https://arxiv.org/abs/2609.03582v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Stratis Markou", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "discoverer" ], "source": { "citation": "WeatherNext 3: Increasing resolution and performance of global weather models with raw observations", "url": "https://arxiv.org/abs/2609.03582v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Shreya Agrawal", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "discoverer" ], "source": { "citation": "WeatherNext 3: Increasing resolution and performance of global weather models with raw observations", "url": "https://arxiv.org/abs/2609.03582v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Suhani Vora", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "discoverer" ], "source": { "citation": "WeatherNext 3: Increasing resolution and performance of global weather models with raw observations", "url": "https://arxiv.org/abs/2609.03582v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Mohammed Alewi Hassen", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "discoverer" ], "source": { "citation": "WeatherNext 3: Increasing resolution and performance of global weather models with raw observations", "url": "https://arxiv.org/abs/2609.03582v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Sunny Mak", "note": "Archive attribution; detailed role assignment remains source-bound", "roles": [ "discoverer" ], "source": { "citation": "WeatherNext 3: Increasing resolution and performance of global weather models with raw observations", "url": "https://arxiv.org/abs/2609.03582v1" } }, { "attribution_basis": "SOURCE_CITED", "credit": "historical_scientific", "entity_kind": "historical_person", "name": "Tom R. 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Unseen-station performance is finite held-out evidence; deployment, agricultural outcomes and societal benefits are not separately claimed.", "domain": "AI weather prediction" }, "sources": [ { "citation": "WeatherNext 3: Increasing resolution and performance of global weather models with raw observations", "url": "https://arxiv.org/abs/2609.03582v1" }, { "citation": "Rasp et al.: observation-conditioned high-resolution global weather modeling — official company record", "url": "https://blog.google/innovation-and-ai/models-and-research/google-deepmind/introducing-weathernext-3/" } ], "statement": "# Rasp et al.: observation-conditioned high-resolution global weather modeling\n\nIntegrated low-latency satellite inputs and observation-targeted learning into probabilistic global forecasting, producing hourly updates and finer spatiotemporal outputs including station-conditioned surface predictions.\n\n## Scientific scope\n\nA forecast-model method and evaluated skill result, not deterministic weather certainty or a replacement for official warnings. Unseen-station performance is finite held-out evidence; deployment, agricultural outcomes and societal benefits are not separately claimed.\n\n## Reading status\n\nThis is a source-linked WRS catalogue note. A full explanatory guide and independent scientific reconstruction have not been completed for this entry. Read the identified original work for its complete narrative and arguments.\n\n## People and systems\n\nStephan Rasp; Boris Babenko; Dominic Masters; Andrew El-Kadi; Samier Merchant; Guy Shalev; Ilan Price; Fred Zyda; Remi Lam; Sasha Shysheya; Matthew Willson; Stratis Markou; Shreya Agrawal; Suhani Vora; Mohammed Alewi Hassen; Sunny Mak; Tom R. Andersson; Megan Bela; Akib Uddin; Nofar Peled Levi; Ben Gaiarin; Ferran Alet; Aaron Bell; Peter Battaglia; Alvaro Sanchez-Gonzalez\n\nSource-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities.\n\n\n\n## Original sources\n\n- [WeatherNext 3: Increasing resolution and performance of global weather models with raw observations](https://arxiv.org/abs/2609.03582v1)\n- [Rasp et al.: observation-conditioned high-resolution global weather modeling — official company record](https://blog.google/innovation-and-ai/models-and-research/google-deepmind/introducing-weathernext-3/)\n", "summary": "Integrated low-latency satellite inputs and observation-targeted learning into probabilistic global forecasting, producing hourly updates and finer spatiotemporal outputs including station-conditioned surface predictions.", "title": "Rasp et al.: observation-conditioned high-resolution global weather modeling" }, "record_id": "urn:uuid:e853803b-265c-425d-a947-439938a6c77e", "version": 1, "source_ref": { "record_id": "urn:uuid:e853803b-265c-425d-a947-439938a6c77e", "version": 1, "content_hash": "sha256:cdbd6e415476def51244d4df90a9413ee4fe35086cb4035024d673b8c1333d46" }, "export_view": "wrs-public-editorial-view-1", "public_view_hash": "sha256:a3a48718f6468577d8011daf720aca00f2bc69750b2d49f6e73445102ed9f0b9" }, "target": { "record_id": "urn:uuid:e853803b-265c-425d-a947-439938a6c77e", "version": 1, "content_hash": "sha256:cdbd6e415476def51244d4df90a9413ee4fe35086cb4035024d673b8c1333d46" }, "published_grade": { "profile": "wrs-published-grade-1", "edition": "2026-10-28.1", "id": "rasp-et-al-weathernext3-observations-2026", "entry_id": "rasp-et-al-weathernext3-observations-2026", "revision": 1, "title": "Rasp et al.: observation-conditioned high-resolution global weather modeling", "context": "AI weather prediction", "track": "Human-designed research method or resource", "band": "A", "modifier": null, "status": "PROVISIONAL_EDITORIAL", "assessed_on": "2026-10-16", "evidence_cutoff": "2026-10-16", "achievement_state": "SUPPORTED_SCOPED_RESULT", "what_changed": "Integrated low-latency satellite inputs and observation-targeted learning into probabilistic global forecasting, producing hourly updates and finer spatiotemporal outputs including station-conditioned surface predictions.", "scope": "Integrated low-latency satellite inputs and observation-targeted learning into probabilistic global forecasting, producing hourly updates and finer spatiotemporal outputs including station-conditioned surface predictions. A forecast-model method and evaluated skill result, not deterministic weather certainty or a replacement for official warnings. Unseen-station performance is finite held-out evidence; deployment, agricultural outcomes and societal benefits are not separately claimed.", "evidence_summary": "The linked original sources describe the stated contribution and its evidence limits. WRS has not independently rerun the experiments or certified a proof. A forecast-model method and evaluated skill result, not deterministic weather certainty or a replacement for official warnings. Unseen-station performance is finite held-out evidence; deployment, agricultural outcomes and societal benefits are not separately claimed.", "references": [ { "title": "WeatherNext 3: Increasing resolution and performance of global weather models with raw observations", "url": "https://arxiv.org/abs/2609.03582v1", "locator": "Original contribution and stated assumptions, evaluation scope and limitations; source-linked assessment, not independent execution or proof certification." }, { "title": "Rasp et al.: observation-conditioned high-resolution global weather modeling — official company record", "url": "https://blog.google/innovation-and-ai/models-and-research/google-deepmind/introducing-weathernext-3/", "locator": "Original contribution and stated assumptions, evaluation scope and limitations; source-linked assessment, not independent execution or proof certification." } ], "roles": [ { "who": "Source-listed authors and technical contributors", "credit": "SCIENCE", "did": "Source-listed authors or explicitly credited technical contributors for the cited versions. Family entries retain source-specific attribution; a combined list is not one original-paper byline or equal individual work. Names alone do not establish resolved identities." } ], "target": { "record_id": "urn:uuid:e853803b-265c-425d-a947-439938a6c77e", "version": 1, "content_hash": "sha256:cdbd6e415476def51244d4df90a9413ee4fe35086cb4035024d673b8c1333d46" }, "scientific_correctness_certified": false, "independent_expert_calibration": false, "method": "PROPRIETARY", "points": "130000" }, "canonical_agent_submission": false, "scientific_verification_awarded": false, "reader_guide": null, "reading_status": "SOURCE_LINKED_NOTE" }