{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,2]],"date-time":"2026-06-02T10:49:17Z","timestamp":1780397357947,"version":"3.54.1"},"reference-count":23,"publisher":"Institute of Electrical and Electronics Engineers (IEEE)","license":[{"start":{"date-parts":[[2023,1,1]],"date-time":"2023-01-01T00:00:00Z","timestamp":1672531200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by-nc-nd\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100003629","name":"Korea Meteorological Administration through the Project \u201cDevelopment of Application Technology for Climate and Climate Change Monitoring and Prediction Information\u201d","doi-asserted-by":"publisher","award":["KMI-2021-01310"],"award-info":[{"award-number":["KMI-2021-01310"]}],"id":[{"id":"10.13039\/501100003629","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IEEE Access"],"published-print":{"date-parts":[[2023]]},"DOI":"10.1109\/access.2023.3297200","type":"journal-article","created":{"date-parts":[[2023,7,20]],"date-time":"2023-07-20T17:56:11Z","timestamp":1689875771000},"page":"86038-86055","source":"Crossref","is-referenced-by-count":11,"title":["Optimizing Numerical Weather Prediction Model Performance Using Machine Learning Techniques"],"prefix":"10.1109","volume":"11","author":[{"ORCID":"https:\/\/orcid.org\/0009-0000-1086-058X","authenticated-orcid":false,"given":"Soohyuck","family":"Choi","sequence":"first","affiliation":[{"name":"Department of Software and Communications Engineering, Hongik University, Sejong, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1288-7521","authenticated-orcid":false,"given":"Eun-Sung","family":"Jung","sequence":"additional","affiliation":[{"name":"Department of Software and Communications Engineering, Hongik University, Sejong, Republic of Korea"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"263","reference":[{"key":"ref13","year":"2023","journal-title":"Darshan Introduction"},{"key":"ref12","doi-asserted-by":"publisher","DOI":"10.1109\/CLUSTR.2009.5289150"},{"key":"ref23","author":"kabacoff","year":"2022","journal-title":"R in Action Data Analysis and Graphics with R and Tidyverse"},{"key":"ref15","doi-asserted-by":"publisher","DOI":"10.1145\/2907294.2907316"},{"key":"ref14","doi-asserted-by":"publisher","DOI":"10.1145\/582034.582045"},{"key":"ref20","year":"2023","journal-title":"Package GBM"},{"key":"ref11","year":"2023","journal-title":"Free Icons Clipart Illustrations Photos and Music"},{"key":"ref22","author":"rea","year":"2012","journal-title":"Designing and conducting survey research A comprehensive guide"},{"key":"ref10","year":"2022","journal-title":"Unidata |NetCDF"},{"key":"ref21","year":"2022","journal-title":"SSD vs HDD—Comparing Speed Lifespan Reliability"},{"key":"ref2","volume":"2020","author":"davis","year":"2020","journal-title":"A large ensemble seasonal forecasting system GloSea6"},{"key":"ref1","year":"2023","journal-title":"Concept of a Numerical Forecast Model"},{"key":"ref17","article-title":"Random forests: Some methodological insights","author":"genuer","year":"2008","journal-title":"arXiv 0811 3619"},{"key":"ref16","year":"2023","journal-title":"R Package"},{"key":"ref19","year":"2023","journal-title":"Package 'RandomForest"},{"key":"ref18","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-84858-7"},{"key":"ref8","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-642-23360-9_4"},{"key":"ref7","first-page":"1","article-title":"Improving the I\/O performance of applications with predictive modeling based auto-tuning","author":"ba?baba","year":"2021","journal-title":"Proc Int Conf Eng Emerg Technol (ICEET)"},{"key":"ref9","year":"2023","journal-title":"Analysing UM Outputs"},{"key":"ref4","doi-asserted-by":"publisher","DOI":"10.1145\/2503210.2503278"},{"key":"ref3","article-title":"Tuning HDF5 for Lustre file systems","author":"howison","year":"2010"},{"key":"ref6","doi-asserted-by":"publisher","DOI":"10.1109\/HPCS48598.2019.9188173"},{"key":"ref5","doi-asserted-by":"publisher","DOI":"10.1145\/3309205"}],"container-title":["IEEE Access"],"original-title":[],"link":[{"URL":"http:\/\/xplorestaging.ieee.org\/ielx7\/6287639\/10005208\/10188815.pdf?arnumber=10188815","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2023,9,4]],"date-time":"2023-09-04T18:24:40Z","timestamp":1693851880000},"score":1,"resource":{"primary":{"URL":"https:\/\/ieeexplore.ieee.org\/document\/10188815\/"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2023]]},"references-count":23,"URL":"https:\/\/doi.org\/10.1109\/access.2023.3297200","relation":{},"ISSN":["2169-3536"],"issn-type":[{"value":"2169-3536","type":"electronic"}],"subject":[],"published":{"date-parts":[[2023]]}}}