{"id":"W4412526239","doi":"10.1175/waf-d-24-0139.1","title":"Leveraging Data-Driven Weather Models for Improving Numerical Weather Prediction Skill through Large-Scale Spectral Nudging","year":2025,"lang":"en","type":"article","venue":"Weather and Forecasting","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Numerical weather prediction; Model output statistics; Weather prediction; Scale (ratio); Meteorology; Weather forecasting; Weather Research and Forecasting Model; North American Mesoscale Model; Computer science; Environmental science; Global Forecast System; Geography; Cartography","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008609388,0.0005720314,0.0004766065,0.0005354038,0.000344202,0.000978331,0.001097195,0.0005789781,0.001353528],"category_scores_gemma":[0.002943693,0.0004107587,0.0005258792,0.0003624928,0.0005137887,0.001841661,0.001325672,0.001118604,0.0003089519],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004519278,"about_ca_system_score_gemma":0.0007144096,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008713023,"about_ca_topic_score_gemma":0.008571457,"domain_scores_codex":[0.9998195,0.00005048195,0.00001603497,0.00005157728,0.00004458638,0.00001787163],"domain_scores_gemma":[0.9989223,0.0004022578,0.0001333756,0.0002040436,0.0002310854,0.0001069146],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00006386331,0.0001021404,0.003728003,0.00002792337,0.00004718138,0.00003524285,0.00004873337,0.9587121,0.005584366,0.002013798,0.0008878859,0.02874875],"study_design_scores_gemma":[0.000002724539,0.000004715625,0.0001428453,0.000001240192,0.000001715614,0.000001274525,0.000002622021,0.9988757,0.0003454954,0.0004847873,0.0001341145,0.000002686787],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2332416,0.0002956995,0.7575623,0.0009213897,0.0002509686,0.00008973909,0.000338675,0.003209045,0.004090559],"genre_scores_gemma":[0.9246236,0.00007521868,0.07417314,0.000114147,0.0000661122,0.00003913627,0.0002403437,0.0001093001,0.000558998],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008713023,"threshold_uncertainty_score":0.01732463,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05532480573029311,"score_gpt":0.2566401483073936,"score_spread":0.2013153425771005,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}