{"id":"W2990103402","doi":"10.1175/mwr-d-19-0193.1","title":"Improving Radar Echo Lagrangian Extrapolation Nowcasting by Blending Numerical Model Wind Information: Statistical Performance of 16 Typhoon Cases","year":2019,"lang":"en","type":"article","venue":"Monthly Weather Review","topic":"Meteorological Phenomena and Simulations","field":"Earth and Planetary Sciences","cited_by":42,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Ministry of Science and Technology, Taiwan","keywords":"Nowcasting; Extrapolation; Typhoon; Meteorology; Radar; Environmental science; Precipitation; Weather radar; Quantitative precipitation estimation; Eye; Numerical weather prediction; Computer science; Quantitative precipitation forecast; Remote sensing; Climatology; Tropical cyclone; Geology; Mathematics; Geography; Statistics; Telecommunications","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":["insufficient_payload"],"consensus_categories":[],"category_scores_codex":[0.0003753613,0.0001350719,0.0003012874,0.0000404728,0.00009304873,0.00001855423,0.0001247676,0.0000494568,0.002770636],"category_scores_gemma":[0.000101592,0.0001001857,0.00005755667,0.000182156,0.00002761818,0.0005631843,0.00001048668,0.0001243736,0.0001977721],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00001076512,"about_ca_system_score_gemma":0.00002861192,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0002092532,"about_ca_topic_score_gemma":0.00001109352,"domain_scores_codex":[0.9988528,0.00006988281,0.0004586798,0.0001561611,0.0002425115,0.0002200023],"domain_scores_gemma":[0.9992922,0.0002177065,0.0001947856,0.0001642065,0.00004145231,0.00008966041],"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":[0.0001087214,0.0000549344,0.1482125,0.003106713,0.00004172756,0.000003292397,0.000336328,0.08919039,0.000257169,0.0003077626,0.0009902078,0.7573903],"study_design_scores_gemma":[0.0002516645,0.0002589968,0.01280137,0.0004149622,0.0000545885,0.000005154757,0.00003109823,0.980935,0.00001735628,0.0001059825,0.004901379,0.0002224552],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.879178,0.06111253,0.02241056,0.0001910444,0.0002302986,0.001488946,0.0007808413,0.00009780074,0.03450998],"genre_scores_gemma":[0.9943452,0.001192257,0.003724322,0.0003030008,0.0000193,0.000001416025,0.0003173292,0.000003710988,0.00009344373],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8917446,"threshold_uncertainty_score":0.998141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01903519397995298,"score_gpt":0.2272134588997547,"score_spread":0.2081782649198018,"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."}}