{"id":"W2538865650","doi":"10.1175/mwr-d-16-0106.1","title":"An Ensemble Kalman Filter for Numerical Weather Prediction Based on Variational Data Assimilation: VarEnKF","year":2016,"lang":"en","type":"article","venue":"Monthly Weather Review","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":30,"is_retracted":false,"has_abstract":true,"ca_institutions":"Environment and Climate Change Canada","funders":"","keywords":"Data assimilation; Initialization; Ensemble Kalman filter; Ensemble forecasting; Kalman filter; Numerical weather prediction; Computer science; Ensemble learning; Perturbation (astronomy); Algorithm; Filter (signal processing); Extended Kalman filter; Meteorology; Machine learning; Artificial intelligence; Physics","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":[0.001219739,0.00061251,0.0009095256,0.0005920728,0.0005544238,0.0007264934,0.001526083,0.0008070032,0.001612278],"category_scores_gemma":[0.002696324,0.0005619622,0.000737033,0.000717832,0.0003959047,0.001149224,0.0007944177,0.001454968,0.0006305697],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009408121,"about_ca_system_score_gemma":0.001739039,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.03965269,"about_ca_topic_score_gemma":0.03135707,"domain_scores_codex":[0.9994383,0.0001456033,0.00003259507,0.0001351141,0.0001941851,0.00005405934],"domain_scores_gemma":[0.9993621,0.0002702093,0.00004360113,0.00006351119,0.0002416472,0.00001901445],"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":[0.00006750976,0.00003125294,0.001476491,0.00005240406,0.00008882983,0.00003320075,0.00005848136,0.8799378,0.003910353,0.01068424,0.001715312,0.1019441],"study_design_scores_gemma":[0.000002996797,0.000003480003,0.000103292,0.000002408017,0.000002938522,0.000002727212,0.000001714342,0.9984187,0.0003620121,0.0006182942,0.0004769604,0.000004464401],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.007252306,0.0001688555,0.9911301,0.00006027098,0.00005186277,0.00001811538,0.00008136625,0.0004367687,0.0008003465],"genre_scores_gemma":[0.3506508,0.0004505897,0.6440894,0.0001007767,0.0001107371,0.0002076205,0.0007893534,0.0002293412,0.003371378],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.03965269,"threshold_uncertainty_score":0.07884377,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07305333429897244,"score_gpt":0.2855627367202275,"score_spread":0.2125094024212551,"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."}}