{"id":"https://openalex.org/W4414908983","doi":"https://doi.org/10.1109/escience65000.2025.00017","title":"Accelerating Weather Forecasting: A Neural Network-Based Emulation of ISORROPIA","display_name":"Accelerating Weather Forecasting: A Neural Network-Based Emulation of ISORROPIA","publication_year":2025,"publication_date":"2025-09-15","ids":{"openalex":"https://openalex.org/W4414908983","doi":"https://doi.org/10.1109/escience65000.2025.00017"},"language":"en","primary_location":{"id":"doi:10.1109/escience65000.2025.00017","is_oa":false,"landing_page_url":"https://doi.org/10.1109/escience65000.2025.00017","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on eScience (eScience)","raw_type":"proceedings-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/A5043020206","display_name":"Georgios Evangelopoulos","orcid":"https://orcid.org/0000-0003-2240-1801"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Georgios Evangelopoulos","raw_affiliation_strings":["Karlsruhe Institute of Technology,Scientific Computing Center,Karlsruhe,Germany"],"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology,Scientific Computing Center,Karlsruhe,Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011386784","display_name":"Gholam Ali Hoshyaripour","orcid":"https://orcid.org/0000-0001-7770-7838"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Gholamali Hoshyaripour","raw_affiliation_strings":["Karlsruhe Institute of Technology,Institute of Meteorology &amp; Climate Research,Karlsruhe,Germany"],"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology,Institute of Meteorology &amp; Climate Research,Karlsruhe,Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5087225478","display_name":"J. Meyer","orcid":"https://orcid.org/0000-0003-0861-8481"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"J\u00f6rg Meyer","raw_affiliation_strings":["Karlsruhe Institute of Technology,Scientific Computing Center,Karlsruhe,Germany"],"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology,Scientific Computing Center,Karlsruhe,Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5011643487","display_name":"Pankaj Kumar","orcid":"https://orcid.org/0000-0003-0301-1357"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Pankaj Kumar","raw_affiliation_strings":["Karlsruhe Institute of Technology,Institute of Meteorology &amp; Climate Research,Karlsruhe,Germany"],"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology,Institute of Meteorology &amp; Climate Research,Karlsruhe,Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5037501781","display_name":"Julia Bruckert","orcid":"https://orcid.org/0000-0003-2302-8383"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Julia Bruckert","raw_affiliation_strings":["Karlsruhe Institute of Technology,Institute of Meteorology &amp; Climate Research,Karlsruhe,Germany"],"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology,Institute of Meteorology &amp; Climate Research,Karlsruhe,Germany","institution_ids":["https://openalex.org/I102335020"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5030034618","display_name":"Achim Streit","orcid":"https://orcid.org/0000-0002-5065-469X"},"institutions":[{"id":"https://openalex.org/I102335020","display_name":"Karlsruhe Institute of Technology","ror":"https://ror.org/04t3en479","country_code":"DE","type":"education","lineage":["https://openalex.org/I102335020","https://openalex.org/I1305996414"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Achim Streit","raw_affiliation_strings":["Karlsruhe Institute of Technology,Scientific Computing Center,Karlsruhe,Germany"],"affiliations":[{"raw_affiliation_string":"Karlsruhe Institute of Technology,Scientific Computing Center,Karlsruhe,Germany","institution_ids":["https://openalex.org/I102335020"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":6,"corresponding_author_ids":["https://openalex.org/A5043020206"],"corresponding_institution_ids":["https://openalex.org/I102335020"],"apc_list":null,"apc_paid":null,"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.29455211,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":"67","last_page":"75"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.6625000238418579,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic Engineering"},"field":{"id":"https://openalex.org/fields/22","display_name":"Engineering"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},"topics":[{"id":"https://openalex.org/T11052","display_name":"Energy Load and Power Forecasting","score":0.6625000238418579,"subfield":{"id":"https://openalex.org/subfields/2208","display_name":"Electrical and Electronic 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/emulation","display_name":"Emulation","score":0.7470999956130981},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.6830999851226807},{"id":"https://openalex.org/keywords/trace","display_name":"TRACE (psycholinguistics)","score":0.5019999742507935},{"id":"https://openalex.org/keywords/feed-forward","display_name":"Feed forward","score":0.48989999294281006},{"id":"https://openalex.org/keywords/range","display_name":"Range (aeronautics)","score":0.4325000047683716},{"id":"https://openalex.org/keywords/feedforward-neural-network","display_name":"Feedforward neural network","score":0.4002000093460083},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.39959999918937683},{"id":"https://openalex.org/keywords/atmospheric-model","display_name":"Atmospheric model","score":0.3808000087738037},{"id":"https://openalex.org/keywords/task","display_name":"Task (project management)","score":0.3783999979496002}],"concepts":[{"id":"https://openalex.org/C149810388","wikidata":"https://www.wikidata.org/wiki/Q5374873","display_name":"Emulation","level":2,"score":0.7470999956130981},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.6830999851226807},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.669700026512146},{"id":"https://openalex.org/C75291252","wikidata":"https://www.wikidata.org/wiki/Q1315756","display_name":"TRACE (psycholinguistics)","level":2,"score":0.5019999742507935},{"id":"https://openalex.org/C38858127","wikidata":"https://www.wikidata.org/wiki/Q5441228","display_name":"Feed forward","level":2,"score":0.48989999294281006},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.4595000147819519},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.45329999923706055},{"id":"https://openalex.org/C204323151","wikidata":"https://www.wikidata.org/wiki/Q905424","display_name":"Range (aeronautics)","level":2,"score":0.4325000047683716},{"id":"https://openalex.org/C47702885","wikidata":"https://www.wikidata.org/wiki/Q5441227","display_name":"Feedforward neural network","level":3,"score":0.4002000093460083},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.39959999918937683},{"id":"https://openalex.org/C118365302","wikidata":"https://www.wikidata.org/wiki/Q4817115","display_name":"Atmospheric model","level":2,"score":0.3808000087738037},{"id":"https://openalex.org/C2780451532","wikidata":"https://www.wikidata.org/wiki/Q759676","display_name":"Task (project management)","level":2,"score":0.3783999979496002},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.3707999885082245},{"id":"https://openalex.org/C168754636","wikidata":"https://www.wikidata.org/wiki/Q620920","display_name":"Climate model","level":3,"score":0.335999995470047},{"id":"https://openalex.org/C2780586882","wikidata":"https://www.wikidata.org/wiki/Q7520643","display_name":"Simple (philosophy)","level":2,"score":0.3174000084400177},{"id":"https://openalex.org/C80368990","wikidata":"https://www.wikidata.org/wiki/Q3046459","display_name":"Earth system science","level":2,"score":0.31690001487731934},{"id":"https://openalex.org/C117896860","wikidata":"https://www.wikidata.org/wiki/Q11376","display_name":"Acceleration","level":2,"score":0.31360000371932983},{"id":"https://openalex.org/C2778755073","wikidata":"https://www.wikidata.org/wiki/Q10858537","display_name":"Scale (ratio)","level":2,"score":0.2962999939918518},{"id":"https://openalex.org/C76969082","wikidata":"https://www.wikidata.org/wiki/Q486902","display_name":"Mathematical model","level":2,"score":0.27889999747276306},{"id":"https://openalex.org/C131675550","wikidata":"https://www.wikidata.org/wiki/Q7646884","display_name":"Surrogate model","level":2,"score":0.27630001306533813},{"id":"https://openalex.org/C66024118","wikidata":"https://www.wikidata.org/wiki/Q1122506","display_name":"Computational model","level":2,"score":0.2624000012874603},{"id":"https://openalex.org/C67186912","wikidata":"https://www.wikidata.org/wiki/Q367664","display_name":"Data modeling","level":2,"score":0.2603999972343445},{"id":"https://openalex.org/C113843644","wikidata":"https://www.wikidata.org/wiki/Q901882","display_name":"Interface (matter)","level":4,"score":0.25850000977516174},{"id":"https://openalex.org/C21001229","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather forecasting","level":2,"score":0.2563000023365021},{"id":"https://openalex.org/C44154836","wikidata":"https://www.wikidata.org/wiki/Q45045","display_name":"Simulation","level":1,"score":0.2526000142097473},{"id":"https://openalex.org/C2779345167","wikidata":"https://www.wikidata.org/wiki/Q104541","display_name":"Aerosol","level":2,"score":0.251800000667572},{"id":"https://openalex.org/C193415008","wikidata":"https://www.wikidata.org/wiki/Q639681","display_name":"Network architecture","level":2,"score":0.2502000033855438}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/escience65000.2025.00017","is_oa":false,"landing_page_url":"https://doi.org/10.1109/escience65000.2025.00017","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2025 IEEE International Conference on eScience (eScience)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Atmospheric":[0],"composition":[1,16],"is":[2,17,58,136],"an":[3,137],"essential":[4],"part":[5],"of":[6,29,61,102,118,154,192,224],"weather,":[7],"climate":[8],"and":[9,21,37,50,129,195,207,218,275],"Earth":[10],"system":[11],"modeling.":[12],"However,":[13],"modeling":[14],"atmospheric":[15],"a":[18,26,103,110,151,169,189,226],"computationally":[19],"expensive":[20],"time-consuming":[22],"task":[23],"that":[24,145,258],"requires":[25,146],"significant":[27,152],"amount":[28],"energy.":[30],"As":[31],"models":[32],"scale":[33],"to":[34,70,87,107,233,249],"finer":[35],"spatial":[36],"temporal":[38],"resolutions,":[39],"maintaining":[40,225,268],"real-time":[41],"performance":[42],"becomes":[43],"increasingly":[44],"challenging.":[45],"To":[46],"address":[47],"this,":[48],"optimization":[49],"acceleration":[51],"techniques":[52],"are":[53,83],"essential.":[54],"One":[55],"promising":[56],"approach":[57],"the":[59,68,92,100,116,122,155,173,179,183,198,204,221,235,242,259,265,273],"use":[60],"deep":[62],"neural":[63,81,184,199,244,260],"networks,":[64],"which":[65,176],"have":[66],"demonstrated":[67,257],"capability":[69],"efficiently":[71],"approximate":[72],"complex":[73],"systems":[74],"with":[75,127,220],"high":[76],"accuracy.":[77],"Predictions":[78],"using":[79,172],"these":[80],"networks":[82],"notably":[84],"faster":[85],"compared":[86,248],"traditional":[88,111,174],"methods,":[89],"significantly":[90],"reducing":[91],"computational":[93,148],"burden.":[94],"In":[95],"this":[96],"study,":[97],"we":[98],"present":[99],"development":[101],"surrogate":[104],"model":[105,112,126,141,246,262],"designed":[106],"emulate":[108],"ISORROPIA,":[109],"used":[113,142,217],"for":[114,163,182,209],"calculating":[115],"concentrations":[117,194],"chemical":[119,193],"compounds":[120],"in":[121],"ICON-ART":[123,252],"(ICOsahedral":[124],"Nonhydrostatic":[125],"Aerosol":[128],"Reactive":[130],"Trace":[131],"gases)":[132],"model.":[133],"Specifically,":[134],"ISORROPIA":[135,250],"aerosol":[138],"thermodynamic":[139],"equilibrium":[140],"by":[143],"ART":[144],"substantial":[147],"resources,":[149],"occupying":[150],"portion":[153],"overall":[156],"calculation":[157],"time,":[158],"making":[159],"it":[160],"particularly":[161],"well-suited":[162],"emulation.":[164],"The":[165,255],"methodology":[166],"involved":[167],"generating":[168],"comprehensive":[170],"dataset":[171,187],"model,":[175],"served":[177],"as":[178],"training":[180],"data":[181],"network.":[185],"This":[186],"encompassed":[188],"wide":[190],"range":[191],"conditions,":[196],"ensuring":[197],"network":[200,245,261],"could":[201],"effectively":[202],"learn":[203],"underlying":[205],"patterns":[206],"relationships":[208],"real-life":[210],"scenarios.":[211],"A":[212],"simple":[213],"feedforward":[214],"architecture":[215],"was":[216,247],"fine-tuned":[219],"primary":[222],"goal":[223],"low":[227,269],"approximation":[228,270],"error":[229,271],"while":[230],"also":[231],"striving":[232],"achieve":[234],"lowest":[236],"possible":[237],"inference":[238,277],"timing.":[239,278],"After":[240],"training,":[241],"new":[243],"on":[251],"simulation":[253],"data.":[254],"results":[256],"successfully":[263],"achieved":[264],"desired":[266],"outcomes,":[267],"across":[272],"globe":[274],"efficient":[276]},"counts_by_year":[],"updated_date":"2026-03-07T16:01:11.037858","created_date":"2025-10-10T00:00:00"}
