{"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":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0007065539,0.0001622613,0.0002511069,0.00003860489,0.0001634573,0.00003278032,0.0003573005,0.00007018542,0.01242757],"category_scores_gemma":[0.0001804546,0.00009086332,0.00007837164,0.0001301236,0.00002724684,0.000336991,0.000009466636,0.00006028104,0.0002603134],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.000009242595,"about_ca_system_score_gemma":0.00003866854,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001937628,"about_ca_topic_score_gemma":0.00002893806,"domain_scores_codex":[0.9984608,0.0002158868,0.0003329733,0.0004686786,0.0002885858,0.0002330382],"domain_scores_gemma":[0.998474,0.0005117973,0.0001066758,0.0007078573,0.0000547849,0.0001448996],"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":[0.0004045203,0.0005857483,0.1874536,0.0004280304,0.0001297365,0.000007055976,0.00008570384,0.04716482,0.000093169,0.00245675,0.04786814,0.7133228],"study_design_scores_gemma":[0.0006279547,0.0005873889,0.1859791,0.0003349492,0.00009049312,7.211567e-7,0.000001566278,0.4605554,0.000002716249,0.002953607,0.3486325,0.0002335884],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004353388,0.02643849,0.8167267,0.02759597,0.001639718,0.006229231,0.017618,0.0006474845,0.098751],"genre_scores_gemma":[0.9718395,0.0006393572,0.01354668,0.00753309,0.0006717847,0.00006115625,0.004729467,0.00001991093,0.0009590645],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.9674861,"threshold_uncertainty_score":0.9884752,"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."}}