{"id":"W3127522619","doi":"10.1175/mwr-d-20-0086.1","title":"A Sequential Non-Gaussian Approach for Precipitation Data Assimilation","year":2021,"lang":"en","type":"article","venue":"Monthly Weather Review","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"Canadian Network for Research and Innovation in Machining Technology, Natural Sciences and Engineering Research Council of Canada","keywords":"Data assimilation; Quantitative precipitation forecast; Ensemble forecasting; Precipitation; Ensemble Kalman filter; Kalman filter; Computer science; Ensemble learning; Ensemble average; Meteorology; Environmental science; Algorithm; Climatology; Artificial intelligence; Geology; Physics","routes":{"ca_aff":true,"ca_fund":true,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0005270549,0.0001061301,0.000230276,0.00001896308,0.0001274846,0.00004850341,0.0002282338,0.00004893328,0.001614565],"category_scores_gemma":[0.0001741829,0.00007840078,0.00007142319,0.0001841715,0.00001982399,0.0002470755,0.00001934861,0.0000644707,0.00005842074],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000003651149,"about_ca_system_score_gemma":0.00004956941,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00003485024,"about_ca_topic_score_gemma":0.0001115648,"domain_scores_codex":[0.9988984,0.0001342983,0.0002725691,0.0003634081,0.0001596257,0.0001716364],"domain_scores_gemma":[0.9991959,0.0001126987,0.00008722352,0.0004714487,0.00005687923,0.00007579596],"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":[0.0000897815,0.000319917,0.05498787,0.004137421,0.0002568789,0.00001642005,0.0005084655,0.0476351,0.0001231559,0.001164969,0.02336625,0.8673938],"study_design_scores_gemma":[0.0006133546,0.0001407738,0.09443817,0.0003640378,0.0003015419,0.000003906082,0.00005575007,0.6255734,0.000012219,0.003027196,0.2750682,0.0004014153],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"review","genre_gemma":"empirical","genre_scores_codex":[0.01106398,0.3959348,0.256599,0.007146919,0.001077391,0.006534741,0.0039536,0.0002505732,0.317439],"genre_scores_gemma":[0.8125848,0.01012234,0.1264566,0.004783158,0.000661385,0.00007888877,0.0429727,0.00002150972,0.002318545],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.8669924,"threshold_uncertainty_score":0.9992981,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1049152335265262,"score_gpt":0.3052831789188068,"score_spread":0.2003679453922806,"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."}}