{"id":"W2917589794","doi":"10.1175/jhm-d-18-0140.1","title":"Using a Particle Filter to Estimate the Spatial Distribution of the Snowpack Water Equivalent","year":2019,"lang":"en","type":"article","venue":"Journal of Hydrometeorology","topic":"Cryospheric studies and observations","field":"Earth and Planetary Sciences","cited_by":35,"is_retracted":false,"has_abstract":true,"ca_institutions":"GDG Environnement; Ministère des Ressources naturelles et des Forêts; Université de Sherbrooke","funders":"Hydro-Québec","keywords":"Snow; Environmental science; Spatial coherence; Multivariate interpolation; Water equivalent; Snowpack; Spatial distribution; Particle (ecology); Interpolation (computer graphics); Ensemble Kalman filter; Meteorology; Atmospheric sciences; Mathematics; Coherence (philosophical gambling strategy); Statistics; Physics; Geology; Kalman filter; Bilinear interpolation; Extended Kalman filter","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008004,0.0004463166,0.0004516726,0.0007251004,0.000464075,0.0006767861,0.0006827189,0.0005025433,0.0006897292],"category_scores_gemma":[0.00165074,0.0002601108,0.0005235608,0.0005536453,0.0003508312,0.0003632426,0.0003246472,0.0004243515,0.0001581012],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001164507,"about_ca_system_score_gemma":0.001856559,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.2560078,"about_ca_topic_score_gemma":0.1511361,"domain_scores_codex":[0.9998003,0.00004571267,0.00001199885,0.00005244123,0.00005643168,0.00003313084],"domain_scores_gemma":[0.9993788,0.0002644068,0.00005890933,0.000063748,0.0002042026,0.00002992751],"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":[0.00015131,0.00008074707,0.0153261,0.00001940001,0.0001008531,0.00006303142,0.00003751036,0.9461833,0.004206123,0.0008649774,0.0006316504,0.03233502],"study_design_scores_gemma":[0.000006982174,0.000005527407,0.001753606,9.666376e-7,0.000003210264,0.000002026921,0.000003484918,0.9974734,0.0005884601,0.00007347237,0.00008643194,0.000002538245],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4204418,0.00008913821,0.5769182,0.0001153743,0.00006010039,0.00006626458,0.0004845466,0.001023499,0.0008011025],"genre_scores_gemma":[0.9067469,0.00003509398,0.09182765,0.0000203177,0.00001459156,0.00004171916,0.00059543,0.00003136261,0.0006867767],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.2560078,"threshold_uncertainty_score":0.5090352,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03011993772127534,"score_gpt":0.2761729693544115,"score_spread":0.2460530316331362,"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."}}