{"id":"https://openalex.org/W3135755053","doi":"https://doi.org/10.1080/00401706.2021.1895890","title":"Computer Model Emulation with High-Dimensional Functional Output in Large-Scale Observing System Uncertainty Experiments","display_name":"Computer Model Emulation with High-Dimensional Functional Output in Large-Scale Observing System Uncertainty Experiments","publication_year":2021,"publication_date":"2021-03-02","ids":{"openalex":"https://openalex.org/W3135755053","doi":"https://doi.org/10.1080/00401706.2021.1895890","mag":"3135755053"},"language":"en","primary_location":{"id":"doi:10.1080/00401706.2021.1895890","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00401706.2021.1895890","pdf_url":null,"source":{"id":"https://openalex.org/S985303","display_name":"Technometrics","issn_l":"0040-1706","issn":["0040-1706","1537-2723"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Technometrics","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5054766234","display_name":"Pulong Ma","orcid":"https://orcid.org/0000-0002-9847-1868"},"institutions":[{"id":"https://openalex.org/I1343558604","display_name":"Statistical and Applied Mathematical Sciences Institute","ror":"https://ror.org/01shctp43","country_code":"US","type":"facility","lineage":["https://openalex.org/I1311060795","https://openalex.org/I1343558604"]},{"id":"https://openalex.org/I170897317","display_name":"Duke University","ror":"https://ror.org/00py81415","country_code":"US","type":"education","lineage":["https://openalex.org/I170897317"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Pulong Ma","raw_affiliation_strings":["Department of Statistical Sciences, Duke University, Durham, NC","Statistical and Applied Mathematical Sciences Institute, Durham, NC"],"raw_orcid":"https://orcid.org/0000-0002-9847-1868","affiliations":[{"raw_affiliation_string":"Department of Statistical Sciences, Duke University, Durham, NC","institution_ids":["https://openalex.org/I170897317"]},{"raw_affiliation_string":"Statistical and Applied Mathematical Sciences Institute, Durham, NC","institution_ids":["https://openalex.org/I1343558604"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5102809851","display_name":"Anirban Mondal","orcid":"https://orcid.org/0000-0002-4100-2366"},"institutions":[{"id":"https://openalex.org/I58956616","display_name":"Case Western Reserve University","ror":"https://ror.org/051fd9666","country_code":"US","type":"education","lineage":["https://openalex.org/I58956616"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Anirban Mondal","raw_affiliation_strings":["Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University, Cleveland, OH"],"raw_orcid":"https://orcid.org/0000-0002-4100-2366","affiliations":[{"raw_affiliation_string":"Department of Mathematics, Applied Mathematics, and Statistics, Case Western Reserve University, Cleveland, OH","institution_ids":["https://openalex.org/I58956616"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5023576768","display_name":"Bledar A. Konomi","orcid":"https://orcid.org/0000-0003-2020-8493"},"institutions":[{"id":"https://openalex.org/I63135867","display_name":"University of Cincinnati","ror":"https://ror.org/01e3m7079","country_code":"US","type":"education","lineage":["https://openalex.org/I63135867"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Bledar A. Konomi","raw_affiliation_strings":["Division of Statistics and Data Science, Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Division of Statistics and Data Science, Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH","institution_ids":["https://openalex.org/I63135867"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5006075787","display_name":"Jonathan Hobbs","orcid":"https://orcid.org/0000-0003-1679-0898"},"institutions":[{"id":"https://openalex.org/I1334627681","display_name":"Jet Propulsion Laboratory","ror":"https://ror.org/027k65916","country_code":"US","type":"facility","lineage":["https://openalex.org/I122411786","https://openalex.org/I1334627681","https://openalex.org/I4210124779"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Jonathan Hobbs","raw_affiliation_strings":["Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Jet Propulsion Laboratory, California Institute of Technology, Pasadena, CA","institution_ids":["https://openalex.org/I1334627681"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5080111960","display_name":"Joon Jin Song","orcid":"https://orcid.org/0000-0002-1385-4924"},"institutions":[{"id":"https://openalex.org/I157394403","display_name":"Baylor University","ror":"https://ror.org/005781934","country_code":"US","type":"education","lineage":["https://openalex.org/I157394403"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Joon Jin Song","raw_affiliation_strings":["Department of Statistical Science, Baylor University, Waco, TX"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Department of Statistical Science, Baylor University, Waco, TX","institution_ids":["https://openalex.org/I157394403"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5043233797","display_name":"Emily L. Kang","orcid":"https://orcid.org/0000-0001-9433-6223"},"institutions":[{"id":"https://openalex.org/I63135867","display_name":"University of Cincinnati","ror":"https://ror.org/01e3m7079","country_code":"US","type":"education","lineage":["https://openalex.org/I63135867"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Emily L. Kang","raw_affiliation_strings":["Division of Statistics and Data Science, Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH"],"raw_orcid":"https://orcid.org/0000-0001-9433-6223","affiliations":[{"raw_affiliation_string":"Division of Statistics and Data Science, Department of Mathematical Sciences, University of Cincinnati, Cincinnati, OH","institution_ids":["https://openalex.org/I63135867"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5054766234"],"corresponding_institution_ids":["https://openalex.org/I1343558604","https://openalex.org/I170897317"],"apc_list":null,"apc_paid":null,"fwci":1.1279,"has_fulltext":false,"cited_by_count":17,"citation_normalized_percentile":{"value":0.76238694,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":90,"max":99},"biblio":{"volume":"64","issue":"1","first_page":"65","last_page":"79"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11588","display_name":"Atmospheric and Environmental Gas Dynamics","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11588","display_name":"Atmospheric and Environmental Gas Dynamics","score":0.9994000196456909,"subfield":{"id":"https://openalex.org/subfields/2306","display_name":"Global and Planetary Change"},"field":{"id":"https://openalex.org/fields/23","display_name":"Environmental Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12814","display_name":"Gaussian Processes and Bayesian Inference","score":0.9933000206947327,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T12019","display_name":"Calibration and Measurement Techniques","score":0.9807000160217285,"subfield":{"id":"https://openalex.org/subfields/2202","display_name":"Aerospace Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/curse-of-dimensionality","display_name":"Curse of dimensionality","score":0.6506209373474121},{"id":"https://openalex.org/keywords/radiance","display_name":"Radiance","score":0.6273677349090576},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.595464289188385},{"id":"https://openalex.org/keywords/uncertainty-quantification","display_name":"Uncertainty quantification","score":0.5016458034515381},{"id":"https://openalex.org/keywords/algorithm","display_name":"Algorithm","score":0.5011324882507324},{"id":"https://openalex.org/keywords/dimensionality-reduction","display_name":"Dimensionality reduction","score":0.4893578886985779},{"id":"https://openalex.org/keywords/gaussian-process","display_name":"Gaussian process","score":0.4857500195503235},{"id":"https://openalex.org/keywords/subspace-topology","display_name":"Subspace topology","score":0.4641009569168091},{"id":"https://openalex.org/keywords/computation","display_name":"Computation","score":0.44845905900001526},{"id":"https://openalex.org/keywords/emulation","display_name":"Emulation","score":0.441346138715744},{"id":"https://openalex.org/keywords/bayesian-inference","display_name":"Bayesian inference","score":0.42434489727020264},{"id":"https://openalex.org/keywords/scale","display_name":"Scale (ratio)","score":0.419005423784256},{"id":"https://openalex.org/keywords/gaussian","display_name":"Gaussian","score":0.3882621228694916},{"id":"https://openalex.org/keywords/bayesian-probability","display_name":"Bayesian probability","score":0.3156206011772156},{"id":"https://openalex.org/keywords/remote-sensing","display_name":"Remote sensing","score":0.2938317060470581},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.17763859033584595},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.1699991524219513},{"id":"https://openalex.org/keywords/physics","display_name":"Physics","score":0.14375784993171692}],"concepts":[{"id":"https://openalex.org/C111030470","wikidata":"https://www.wikidata.org/wiki/Q1430460","display_name":"Curse of dimensionality","level":2,"score":0.6506209373474121},{"id":"https://openalex.org/C23690007","wikidata":"https://www.wikidata.org/wiki/Q1411145","display_name":"Radiance","level":2,"score":0.6273677349090576},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.595464289188385},{"id":"https://openalex.org/C32230216","wikidata":"https://www.wikidata.org/wiki/Q7882499","display_name":"Uncertainty quantification","level":2,"score":0.5016458034515381},{"id":"https://openalex.org/C11413529","wikidata":"https://www.wikidata.org/wiki/Q8366","display_name":"Algorithm","level":1,"score":0.5011324882507324},{"id":"https://openalex.org/C70518039","wikidata":"https://www.wikidata.org/wiki/Q16000077","display_name":"Dimensionality reduction","level":2,"score":0.4893578886985779},{"id":"https://openalex.org/C61326573","wikidata":"https://www.wikidata.org/wiki/Q1496376","display_name":"Gaussian process","level":3,"score":0.4857500195503235},{"id":"https://openalex.org/C32834561","wikidata":"https://www.wikidata.org/wiki/Q660730","display_name":"Subspace topology","level":2,"score":0.4641009569168091},{"id":"https://openalex.org/C45374587","wikidata":"https://www.wikidata.org/wiki/Q12525525","display_name":"Computation","level":2,"score":0.44845905900001526},{"id":"https://openalex.org/C149810388","wikidata":"https://www.wikidata.org/wiki/Q5374873","display_name":"Emulation","level":2,"score":0.441346138715744},{"id":"https://openalex.org/C160234255","wikidata":"https://www.wikidata.org/wiki/Q812535","display_name":"Bayesian inference","level":3,"score":0.42434489727020264},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.419005423784256},{"id":"https://openalex.org/C163716315","wikidata":"https://www.wikidata.org/wiki/Q901177","display_name":"Gaussian","level":2,"score":0.3882621228694916},{"id":"https://openalex.org/C107673813","wikidata":"https://www.wikidata.org/wiki/Q812534","display_name":"Bayesian probability","level":2,"score":0.3156206011772156},{"id":"https://openalex.org/C62649853","wikidata":"https://www.wikidata.org/wiki/Q199687","display_name":"Remote sensing","level":1,"score":0.2938317060470581},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.17763859033584595},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.1699991524219513},{"id":"https://openalex.org/C121332964","wikidata":"https://www.wikidata.org/wiki/Q413","display_name":"Physics","level":0,"score":0.14375784993171692},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0},{"id":"https://openalex.org/C127313418","wikidata":"https://www.wikidata.org/wiki/Q1069","display_name":"Geology","level":0,"score":0.0},{"id":"https://openalex.org/C62520636","wikidata":"https://www.wikidata.org/wiki/Q944","display_name":"Quantum mechanics","level":1,"score":0.0},{"id":"https://openalex.org/C50522688","wikidata":"https://www.wikidata.org/wiki/Q189833","display_name":"Economic growth","level":1,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1080/00401706.2021.1895890","is_oa":false,"landing_page_url":"https://doi.org/10.1080/00401706.2021.1895890","pdf_url":null,"source":{"id":"https://openalex.org/S985303","display_name":"Technometrics","issn_l":"0040-1706","issn":["0040-1706","1537-2723"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320547","host_organization_name":"Taylor & Francis","host_organization_lineage":["https://openalex.org/P4310320547"],"host_organization_lineage_names":["Taylor & Francis"],"type":"journal"},"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Technometrics","raw_type":"journal-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":34,"referenced_works":["https://openalex.org/W1898904249","https://openalex.org/W1964490787","https://openalex.org/W1973333099","https://openalex.org/W2003884505","https://openalex.org/W2014059759","https://openalex.org/W2018044188","https://openalex.org/W2020005999","https://openalex.org/W2025720061","https://openalex.org/W2040932457","https://openalex.org/W2053469438","https://openalex.org/W2078454401","https://openalex.org/W2091794125","https://openalex.org/W2095104918","https://openalex.org/W2110465139","https://openalex.org/W2125275885","https://openalex.org/W2163490846","https://openalex.org/W2437949503","https://openalex.org/W2514303448","https://openalex.org/W2523742139","https://openalex.org/W2618462867","https://openalex.org/W2745428954","https://openalex.org/W2750902980","https://openalex.org/W2795443545","https://openalex.org/W2804406442","https://openalex.org/W2901761063","https://openalex.org/W2964215139","https://openalex.org/W2991499393","https://openalex.org/W3099125944","https://openalex.org/W3103869760","https://openalex.org/W3114272883","https://openalex.org/W3192431161","https://openalex.org/W4240733927","https://openalex.org/W4241282836","https://openalex.org/W4292156489"],"related_works":["https://openalex.org/W2896728493","https://openalex.org/W2043512367","https://openalex.org/W2392142157","https://openalex.org/W2083200807","https://openalex.org/W4321518006","https://openalex.org/W2154523322","https://openalex.org/W2331836163","https://openalex.org/W2364195017","https://openalex.org/W2049983405","https://openalex.org/W2355430452"],"abstract_inverted_index":{"Observing":[0],"system":[1],"uncertainty":[2],"experiments":[3,166],"(OSUEs)":[4],"have":[5],"been":[6],"recently":[7],"proposed":[8,142],"as":[9,48,113],"a":[10,35,89,114,160,177],"cost-effective":[11],"way":[12],"to":[13,92],"perform":[14],"probabilistic":[15],"assessment":[16],"of":[17,71,117],"retrievals":[18],"for":[19,80],"NASA\u2019s":[20],"Orbiting":[21],"Carbon":[22],"Observatory-2":[23],"(OCO-2)":[24],"mission.":[25,99],"One":[26],"important":[27],"component":[28],"in":[29,96,146],"the":[30,41,55,97,105,183],"OCO-2":[31,98],"retrieval":[32],"algorithm":[33],"is":[34,63,157],"full-physics":[36,184],"forward":[37,61,185],"model":[38,62,73,180],"that":[39,154,168,181],"describes":[40],"mathematical":[42],"relationship":[43],"between":[44],"atmospheric":[45],"variables":[46],"such":[47],"carbon":[49],"dioxide":[50],"and":[51,120,150,176],"radiances":[52],"measured":[53],"by":[54],"remote":[56],"sensing":[57],"instrument.":[58],"This":[59],"complex":[60],"computationally":[64],"expensive":[65],"but":[66],"large-scale":[67,94],"OSUEs":[68,95],"require":[69],"evaluation":[70],"this":[72,85,169],"numerous":[74],"times,":[75],"which":[76],"makes":[77],"it":[78],"infeasible":[79],"comprehensive":[81],"experiments.":[82],"To":[83],"tackle":[84],"issue,":[86],"we":[87],"develop":[88],"statistical":[90,174],"emulator":[91,106,143,170],"facilitate":[93],"Within":[100],"each":[101],"distinct":[102],"spectral":[103],"band,":[104],"represents":[107],"radiance":[108],"output":[109,151],"at":[110],"irregular":[111],"wavelengths":[112],"linear":[115],"combination":[116],"basis":[118],"functions":[119],"random":[121,124],"coefficients.":[122],"These":[123],"coefficients":[125],"are":[126],"then":[127],"modeled":[128],"with":[129,133],"nearest-neighbor":[130],"Gaussian":[131],"processes":[132],"built-in":[134],"input":[135,148],"dimension":[136],"reduction":[137],"via":[138],"active":[139],"subspace.":[140],"The":[141],"reduces":[144],"dimensionality":[145],"both":[147],"space":[149],"space,":[152],"so":[153],"fast":[155],"computation":[156],"achieved":[158],"within":[159],"fully":[161],"Bayesian":[162],"inference":[163],"framework.":[164],"Validation":[165],"demonstrate":[167],"outperforms":[171],"other":[172],"competing":[173],"methods":[175],"reduced":[178],"order":[179],"approximates":[182],"model.":[186]},"counts_by_year":[{"year":2026,"cited_by_count":2},{"year":2025,"cited_by_count":5},{"year":2024,"cited_by_count":7},{"year":2023,"cited_by_count":1},{"year":2022,"cited_by_count":2}],"updated_date":"2026-05-27T09:02:27.158192","created_date":"2025-10-10T00:00:00"}