{"id":"https://openalex.org/W3035548318","doi":"https://doi.org/10.1007/s10044-020-00898-1","title":"Deep learning-based effective fine-grained weather forecasting model","display_name":"Deep learning-based effective fine-grained weather forecasting model","publication_year":2020,"publication_date":"2020-06-22","ids":{"openalex":"https://openalex.org/W3035548318","doi":"https://doi.org/10.1007/s10044-020-00898-1","mag":"3035548318"},"language":"en","primary_location":{"id":"doi:10.1007/s10044-020-00898-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10044-020-00898-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10044-020-00898-1.pdf","source":{"id":"https://openalex.org/S45497385","display_name":"Pattern Analysis and Applications","issn_l":"1433-7541","issn":["1433-7541","1433-755X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Pattern Analysis and Applications","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://link.springer.com/content/pdf/10.1007/s10044-020-00898-1.pdf","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5031706224","display_name":"Pradeep Hewage","orcid":"https://orcid.org/0000-0002-6909-3546"},"institutions":[{"id":"https://openalex.org/I165525304","display_name":"Edge Hill University","ror":"https://ror.org/028ndzd53","country_code":"GB","type":"education","lineage":["https://openalex.org/I165525304"]}],"countries":["GB"],"is_corresponding":true,"raw_author_name":"Pradeep Hewage","raw_affiliation_strings":["Department of Computer Science, Edge Hill University, Ormskirk, Lancashire, L39 4QP, UK"],"raw_orcid":"https://orcid.org/0000-0002-6909-3546","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Edge Hill University, Ormskirk, Lancashire, L39 4QP, UK","institution_ids":["https://openalex.org/I165525304"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053739348","display_name":"Marcello Trovati","orcid":"https://orcid.org/0000-0001-6607-422X"},"institutions":[{"id":"https://openalex.org/I165525304","display_name":"Edge Hill University","ror":"https://ror.org/028ndzd53","country_code":"GB","type":"education","lineage":["https://openalex.org/I165525304"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Marcello Trovati","raw_affiliation_strings":["Department of Computer Science, Edge Hill University, Ormskirk, Lancashire, L39 4QP, UK"],"raw_orcid":"https://orcid.org/0000-0001-6607-422X","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Edge Hill University, Ormskirk, Lancashire, L39 4QP, UK","institution_ids":["https://openalex.org/I165525304"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053784165","display_name":"Ella Pereira","orcid":"https://orcid.org/0000-0001-7273-3295"},"institutions":[{"id":"https://openalex.org/I165525304","display_name":"Edge Hill University","ror":"https://ror.org/028ndzd53","country_code":"GB","type":"education","lineage":["https://openalex.org/I165525304"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Ella Pereira","raw_affiliation_strings":["Department of Computer Science, Edge Hill University, Ormskirk, Lancashire, L39 4QP, UK"],"raw_orcid":"https://orcid.org/0000-0001-7273-3295","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Edge Hill University, Ormskirk, Lancashire, L39 4QP, UK","institution_ids":["https://openalex.org/I165525304"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5057980050","display_name":"Ardhendu Behera","orcid":"https://orcid.org/0000-0003-0276-9000"},"institutions":[{"id":"https://openalex.org/I165525304","display_name":"Edge Hill University","ror":"https://ror.org/028ndzd53","country_code":"GB","type":"education","lineage":["https://openalex.org/I165525304"]}],"countries":["GB"],"is_corresponding":false,"raw_author_name":"Ardhendu Behera","raw_affiliation_strings":["Department of Computer Science, Edge Hill University, Ormskirk, Lancashire, L39 4QP, UK"],"raw_orcid":"https://orcid.org/0000-0003-0276-9000","affiliations":[{"raw_affiliation_string":"Department of Computer Science, Edge Hill University, Ormskirk, Lancashire, L39 4QP, UK","institution_ids":["https://openalex.org/I165525304"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5031706224"],"corresponding_institution_ids":["https://openalex.org/I165525304"],"apc_list":{"value":2390,"currency":"EUR","value_usd":2990},"apc_paid":{"value":2390,"currency":"EUR","value_usd":2990},"fwci":12.573,"has_fulltext":true,"cited_by_count":236,"citation_normalized_percentile":{"value":0.99496947,"is_in_top_1_percent":true,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":97,"max":100},"biblio":{"volume":"24","issue":"1","first_page":"343","last_page":"366"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"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/T11490","display_name":"Hydrological Forecasting Using AI","score":0.9991000294685364,"subfield":{"id":"https://openalex.org/subfields/2305","display_name":"Environmental Engineering"},"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/T11052","display_name":"Energy Load and Power Forecasting","score":0.998199999332428,"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"}},{"id":"https://openalex.org/T10466","display_name":"Meteorological Phenomena and Simulations","score":0.9972000122070312,"subfield":{"id":"https://openalex.org/subfields/1902","display_name":"Atmospheric Science"},"field":{"id":"https://openalex.org/fields/19","display_name":"Earth and Planetary Sciences"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/weather-research-and-forecasting-model","display_name":"Weather Research and Forecasting Model","score":0.7754764556884766},{"id":"https://openalex.org/keywords/numerical-weather-prediction","display_name":"Numerical weather prediction","score":0.7159796953201294},{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.7026535868644714},{"id":"https://openalex.org/keywords/autoregressive-integrated-moving-average","display_name":"Autoregressive integrated moving average","score":0.67603600025177},{"id":"https://openalex.org/keywords/weather-forecasting","display_name":"Weather forecasting","score":0.5765992403030396},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5668354630470276},{"id":"https://openalex.org/keywords/support-vector-machine","display_name":"Support vector machine","score":0.5453325510025024},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.5353361368179321},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5223960876464844},{"id":"https://openalex.org/keywords/ensemble-forecasting","display_name":"Ensemble forecasting","score":0.5125079154968262},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5016529560089111},{"id":"https://openalex.org/keywords/probabilistic-forecasting","display_name":"Probabilistic forecasting","score":0.4965594410896301},{"id":"https://openalex.org/keywords/time-series","display_name":"Time series","score":0.4960075318813324},{"id":"https://openalex.org/keywords/north-american-mesoscale-model","display_name":"North American Mesoscale Model","score":0.4935046434402466},{"id":"https://openalex.org/keywords/autoregressive-model","display_name":"Autoregressive model","score":0.4921804964542389},{"id":"https://openalex.org/keywords/random-forest","display_name":"Random forest","score":0.46784886717796326},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.4461306035518646},{"id":"https://openalex.org/keywords/recurrent-neural-network","display_name":"Recurrent neural network","score":0.4446074068546295},{"id":"https://openalex.org/keywords/global-forecast-system","display_name":"Global Forecast System","score":0.3357219099998474},{"id":"https://openalex.org/keywords/meteorology","display_name":"Meteorology","score":0.280942440032959},{"id":"https://openalex.org/keywords/econometrics","display_name":"Econometrics","score":0.1447070837020874},{"id":"https://openalex.org/keywords/mathematics","display_name":"Mathematics","score":0.11412885785102844},{"id":"https://openalex.org/keywords/geography","display_name":"Geography","score":0.07911574840545654}],"concepts":[{"id":"https://openalex.org/C133204551","wikidata":"https://www.wikidata.org/wiki/Q838305","display_name":"Weather Research and Forecasting Model","level":2,"score":0.7754764556884766},{"id":"https://openalex.org/C147947694","wikidata":"https://www.wikidata.org/wiki/Q837552","display_name":"Numerical weather prediction","level":2,"score":0.7159796953201294},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.7026535868644714},{"id":"https://openalex.org/C24338571","wikidata":"https://www.wikidata.org/wiki/Q2566298","display_name":"Autoregressive integrated moving average","level":3,"score":0.67603600025177},{"id":"https://openalex.org/C21001229","wikidata":"https://www.wikidata.org/wiki/Q182868","display_name":"Weather forecasting","level":2,"score":0.5765992403030396},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5668354630470276},{"id":"https://openalex.org/C12267149","wikidata":"https://www.wikidata.org/wiki/Q282453","display_name":"Support vector machine","level":2,"score":0.5453325510025024},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.5353361368179321},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5223960876464844},{"id":"https://openalex.org/C119898033","wikidata":"https://www.wikidata.org/wiki/Q3433888","display_name":"Ensemble forecasting","level":2,"score":0.5125079154968262},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5016529560089111},{"id":"https://openalex.org/C122282355","wikidata":"https://www.wikidata.org/wiki/Q7246855","display_name":"Probabilistic forecasting","level":3,"score":0.4965594410896301},{"id":"https://openalex.org/C151406439","wikidata":"https://www.wikidata.org/wiki/Q186588","display_name":"Time series","level":2,"score":0.4960075318813324},{"id":"https://openalex.org/C197661373","wikidata":"https://www.wikidata.org/wiki/Q7053811","display_name":"North American Mesoscale Model","level":4,"score":0.4935046434402466},{"id":"https://openalex.org/C159877910","wikidata":"https://www.wikidata.org/wiki/Q2202883","display_name":"Autoregressive model","level":2,"score":0.4921804964542389},{"id":"https://openalex.org/C169258074","wikidata":"https://www.wikidata.org/wiki/Q245748","display_name":"Random forest","level":2,"score":0.46784886717796326},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.4461306035518646},{"id":"https://openalex.org/C147168706","wikidata":"https://www.wikidata.org/wiki/Q1457734","display_name":"Recurrent neural network","level":3,"score":0.4446074068546295},{"id":"https://openalex.org/C163109420","wikidata":"https://www.wikidata.org/wiki/Q287011","display_name":"Global Forecast System","level":3,"score":0.3357219099998474},{"id":"https://openalex.org/C153294291","wikidata":"https://www.wikidata.org/wiki/Q25261","display_name":"Meteorology","level":1,"score":0.280942440032959},{"id":"https://openalex.org/C149782125","wikidata":"https://www.wikidata.org/wiki/Q160039","display_name":"Econometrics","level":1,"score":0.1447070837020874},{"id":"https://openalex.org/C33923547","wikidata":"https://www.wikidata.org/wiki/Q395","display_name":"Mathematics","level":0,"score":0.11412885785102844},{"id":"https://openalex.org/C205649164","wikidata":"https://www.wikidata.org/wiki/Q1071","display_name":"Geography","level":0,"score":0.07911574840545654},{"id":"https://openalex.org/C49937458","wikidata":"https://www.wikidata.org/wiki/Q2599292","display_name":"Probabilistic logic","level":2,"score":0.0}],"mesh":[],"locations_count":2,"locations":[{"id":"doi:10.1007/s10044-020-00898-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10044-020-00898-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10044-020-00898-1.pdf","source":{"id":"https://openalex.org/S45497385","display_name":"Pattern Analysis and Applications","issn_l":"1433-7541","issn":["1433-7541","1433-755X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Pattern Analysis and Applications","raw_type":"journal-article"},{"id":"pmh:oai:pure.atira.dk:openaire/14d50840-df70-4420-9cb3-11151ffa5462","is_oa":true,"landing_page_url":"https://research.edgehill.ac.uk/en/publications/14d50840-df70-4420-9cb3-11151ffa5462","pdf_url":"https://research.edgehill.ac.uk/ws/files/29212834/PAA_Accepted_Version_June_2020.pdf","source":{"id":"https://openalex.org/S4306402462","display_name":"Edge Hill University Research Information Repository (Edge Hill University)","issn_l":null,"issn":null,"is_oa":false,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I165525304","host_organization_name":"Edge Hill University","host_organization_lineage":["https://openalex.org/I165525304"],"host_organization_lineage_names":[],"type":"repository"},"license":null,"license_id":null,"version":"submittedVersion","is_accepted":false,"is_published":false,"raw_source_name":"GALBOKKA HEWAGE, PRADEEP RUWAN PADMASIRI, TROVATI, MARCELLO, PEREIRA, ELLA & BEHERA, ARDHENDU 2021, 'Deep Learning Based Effective Fine-grained Weather Forecasting Model', Pattern Analysis and Applications, vol. 24, no. 1, pp. 343-366. https://doi.org/10.1007/s10044-020-00898-1","raw_type":"info:eu-repo/semantics/publishedVersion"}],"best_oa_location":{"id":"doi:10.1007/s10044-020-00898-1","is_oa":true,"landing_page_url":"https://doi.org/10.1007/s10044-020-00898-1","pdf_url":"https://link.springer.com/content/pdf/10.1007/s10044-020-00898-1.pdf","source":{"id":"https://openalex.org/S45497385","display_name":"Pattern Analysis and Applications","issn_l":"1433-7541","issn":["1433-7541","1433-755X"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310319900","host_organization_name":"Springer Science+Business Media","host_organization_lineage":["https://openalex.org/P4310319900","https://openalex.org/P4310319965"],"host_organization_lineage_names":["Springer Science+Business Media","Springer Nature"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Pattern Analysis and Applications","raw_type":"journal-article"},"sustainable_development_goals":[{"score":0.4399999976158142,"id":"https://metadata.un.org/sdg/13","display_name":"Climate action"}],"awards":[],"funders":[{"id":"https://openalex.org/F4320311694","display_name":"Edge Hill University","ror":"https://ror.org/028ndzd53"},{"id":"https://openalex.org/F4320320298","display_name":"University of Leeds","ror":"https://ror.org/024mrxd33"}],"has_content":{"pdf":true,"grobid_xml":true},"content_urls":{"pdf":"https://content.openalex.org/works/W3035548318.pdf","grobid_xml":"https://content.openalex.org/works/W3035548318.grobid-xml"},"referenced_works_count":78,"referenced_works":["https://openalex.org/W143064730","https://openalex.org/W599041235","https://openalex.org/W950853366","https://openalex.org/W1485009520","https://openalex.org/W1519238132","https://openalex.org/W1525302550","https://openalex.org/W1529590533","https://openalex.org/W1578063937","https://openalex.org/W1601795611","https://openalex.org/W1641696774","https://openalex.org/W1663973292","https://openalex.org/W1768149890","https://openalex.org/W1853206290","https://openalex.org/W1924770834","https://openalex.org/W1961772213","https://openalex.org/W1990050111","https://openalex.org/W2026058014","https://openalex.org/W2056132333","https://openalex.org/W2057440314","https://openalex.org/W2062177351","https://openalex.org/W2064675550","https://openalex.org/W2075795701","https://openalex.org/W2083620425","https://openalex.org/W2093643917","https://openalex.org/W2097334502","https://openalex.org/W2100495367","https://openalex.org/W2102722855","https://openalex.org/W2110485445","https://openalex.org/W2125318369","https://openalex.org/W2126822753","https://openalex.org/W2127911554","https://openalex.org/W2141312318","https://openalex.org/W2154333682","https://openalex.org/W2162135219","https://openalex.org/W2163605009","https://openalex.org/W2167265899","https://openalex.org/W2178805635","https://openalex.org/W2179078469","https://openalex.org/W2179403052","https://openalex.org/W2267322323","https://openalex.org/W2290960045","https://openalex.org/W2291445757","https://openalex.org/W2296702628","https://openalex.org/W2367251044","https://openalex.org/W2509746057","https://openalex.org/W2521003103","https://openalex.org/W2523246573","https://openalex.org/W2550143307","https://openalex.org/W2569349941","https://openalex.org/W2569758175","https://openalex.org/W2581376925","https://openalex.org/W2604272474","https://openalex.org/W2610314771","https://openalex.org/W2613570903","https://openalex.org/W2781626870","https://openalex.org/W2792764867","https://openalex.org/W2801361534","https://openalex.org/W2893954686","https://openalex.org/W2898126708","https://openalex.org/W2901942348","https://openalex.org/W2901999103","https://openalex.org/W2903122581","https://openalex.org/W2913181540","https://openalex.org/W2915783575","https://openalex.org/W2945584124","https://openalex.org/W2949436110","https://openalex.org/W2963840672","https://openalex.org/W2974064661","https://openalex.org/W3019433526","https://openalex.org/W3102027041","https://openalex.org/W3120882540","https://openalex.org/W4253020087","https://openalex.org/W4256014633","https://openalex.org/W6601453971","https://openalex.org/W6602254124","https://openalex.org/W6603242443","https://openalex.org/W6604896550","https://openalex.org/W6910787570"],"related_works":["https://openalex.org/W2391836160","https://openalex.org/W4293349030","https://openalex.org/W2021856275","https://openalex.org/W2133144322","https://openalex.org/W2137007565","https://openalex.org/W1969424709","https://openalex.org/W3009664354","https://openalex.org/W3121928891","https://openalex.org/W2901316757","https://openalex.org/W4309030289"],"abstract_inverted_index":{"Abstract":[0],"It":[1],"is":[2,136,148,164],"well-known":[3,234],"that":[4,184,223],"numerical":[5],"weather":[6,26,37,79,172,187,196,246],"prediction":[7],"(NWP)":[8],"models":[9],"require":[10],"considerable":[11],"computer":[12],"power":[13],"to":[14,19,232,249],"solve":[15],"complex":[16,236],"mathematical":[17],"equations":[18],"obtain":[20],"a":[21,33,70,165,180,190],"forecast":[22],"based":[23],"on":[24],"current":[25],"conditions.":[27],"In":[28],"this":[29,144],"article,":[30],"we":[31,155],"propose":[32],"novel":[34],"lightweight":[35,226],"data-driven":[36],"forecasting":[38,67,82,128,247],"model":[39,177,227],"by":[40,150],"exploring":[41],"temporal":[42,51],"modelling":[43],"approaches":[44],"of":[45,132,168,179,182,193],"long":[46],"short-term":[47],"memory":[48],"(LSTM)":[49],"and":[50,55,69,81,95,107,117,153,160,205,217,235,244],"convolutional":[52],"networks":[53,202],"(TCN)":[54],"compare":[56],"its":[57,240],"performance":[58],"with":[59,203],"the":[60,77,102,126,139,157,224,233],"existing":[61],"classical":[62,103],"machine":[63,104],"learning":[64,105,201],"approaches,":[65,68,106],"statistical":[66,127],"dynamic":[71,140],"ensemble":[72,141],"method,":[73],"as":[74,76,101,125,138],"well":[75],"well-established":[78],"research":[80],"(WRF)":[83],"NWP":[84],"model.":[85],"More":[86],"specifically":[87],"Standard":[88],"Regression":[89,93,115],"(SR),":[90],"Support":[91],"Vector":[92,113,118],"(SVR),":[94],"Random":[96],"Forest":[97],"(RF)":[98],"are":[99,123,208],"implemented":[100,124,137],"Autoregressive":[108],"Integrated":[109],"Moving":[110],"Average":[111],"(ARIMA),":[112],"Auto":[114],"(VAR),":[116],"Error":[119],"Correction":[120],"Model":[121],"(VECM)":[122],"approaches.":[129],"Furthermore,":[130],"Arbitrage":[131],"Forecasting":[133],"Expert":[134],"(AFE)":[135],"method":[142],"in":[143,210],"article.":[145],"Weather":[146],"information":[147],"captured":[149],"time-series":[151],"data":[152],"thus,":[154],"explore":[156],"state-of-art":[158],"LSTM":[159,204],"TCN":[161,206],"models,":[162],"which":[163],"specialised":[166],"form":[167],"neural":[169],"network":[170],"for":[171,195,242],"prediction.":[173],"The":[174,198],"proposed":[175,199,225],"deep":[176,200],"consists":[178],"number":[181],"layers":[183,207],"use":[185],"surface":[186],"parameters":[188],"over":[189],"given":[191],"period":[192],"time":[194],"forecasting.":[197],"assessed":[209],"two":[211],"different":[212],"regressions,":[213],"namely":[214],"multi-input":[215,218],"multi-output":[216],"single-output.":[219],"Our":[220],"experiment":[221],"shows":[222],"produces":[228],"better":[229],"results":[230],"compared":[231],"WRF":[237],"model,":[238],"demonstrating":[239],"potential":[241],"efficient":[243],"accurate":[245],"up":[248],"12":[250],"h.":[251]},"counts_by_year":[{"year":2026,"cited_by_count":14},{"year":2025,"cited_by_count":41},{"year":2024,"cited_by_count":52},{"year":2023,"cited_by_count":56},{"year":2022,"cited_by_count":42},{"year":2021,"cited_by_count":27},{"year":2020,"cited_by_count":4}],"updated_date":"2026-06-09T15:46:55.921056","created_date":"2025-10-10T00:00:00"}
