{"id":"W4381736486","doi":"10.1175/waf-d-23-0018.1","title":"Improved Analog Ensemble Formulation for 3-Hourly Precipitation Forecasts","year":2023,"lang":"en","type":"article","venue":"Weather and Forecasting","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"BC Hydro (Canada); University of British Columbia","funders":"University of British Columbia Graduate School; Natural Sciences and Engineering Research Council of Canada; University of British Columbia; Alliance de recherche numérique du Canada; Mitacs; BC Hydro; Western Canada Research Grid","keywords":"Quantitative precipitation forecast; Precipitation; Forecast skill; Meteorology; Consensus forecast; Ensemble average; Forecast verification; Numerical weather prediction; Ensemble forecasting; Environmental science; Terrain; Model output statistics; Computer science; Climatology; Statistics; Mathematics; Geography; Geology","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0004038327,0.00009505365,0.0001231387,0.00009038377,0.0002867832,0.00005358184,0.00005068046,0.00005839306,0.0001232166],"category_scores_gemma":[0.0001682061,0.00007391002,0.00004911808,0.000214106,0.00001502389,0.0001910062,0.000006744305,0.00004772257,0.00001980477],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000002567262,"about_ca_system_score_gemma":0.000008353337,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0001065588,"about_ca_topic_score_gemma":0.0004144323,"domain_scores_codex":[0.9992249,0.00002496312,0.0002012393,0.000201167,0.00008413547,0.0002636142],"domain_scores_gemma":[0.9992417,0.0004996664,0.00007119562,0.0000698057,0.00004396724,0.00007373247],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.000135047,0.000009652307,0.1675797,0.00004894198,0.00002812599,0.000001224227,0.001096243,0.0182178,0.001041153,0.00185724,0.0001660197,0.8098188],"study_design_scores_gemma":[0.000334005,0.0001969779,0.1985959,0.000005980837,0.00001053211,0.000001509552,0.00009186416,0.7763464,0.00002533399,0.02387868,0.0004122451,0.0001005604],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9772171,0.00007873525,0.01483693,0.00008525921,0.0001446893,0.0003883436,0.00006401781,0.0001076782,0.007077212],"genre_scores_gemma":[0.9955565,0.000006577668,0.003439504,0.00005988895,0.0001289645,0.000008811377,0.0002857434,0.000005140031,0.0005088358],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8097183,"threshold_uncertainty_score":0.3013963,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06260555144751738,"score_gpt":0.2546607492810206,"score_spread":0.1920551978335032,"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."}}