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Im, Jungho
Intelligent Remote sensing and geospatial Information Science Lab.
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dc.citation.number 1 -
dc.citation.startPage 41 -
dc.citation.title NPJ CLIMATE AND ATMOSPHERIC SCIENCE -
dc.citation.volume 6 -
dc.contributor.author Yu, Jinhyeok -
dc.contributor.author Song, Chul H. -
dc.contributor.author Lee, Dogyeong -
dc.contributor.author Lee, Sojin -
dc.contributor.author Kim, Hyun S. -
dc.contributor.author Han, Kyung M. -
dc.contributor.author Park, Seohui -
dc.contributor.author Im, Jungho -
dc.contributor.author Park, Soon-Young -
dc.contributor.author Jeon, Moongu -
dc.contributor.author Peuch, Vincent-Henri -
dc.contributor.author Saide, Pablo E. -
dc.contributor.author Carmichael, Gregory R. -
dc.contributor.author Kim, Jeeho -
dc.contributor.author Kim, Jhoon -
dc.contributor.author Song, Chang-Keun -
dc.contributor.author Woo, Jung-Hun -
dc.contributor.author Ryu, Seong-Hyun -
dc.date.accessioned 2023-12-21T12:39:20Z -
dc.date.available 2023-12-21T12:39:20Z -
dc.date.created 2023-06-12 -
dc.date.issued 2023-05 -
dc.description.abstract Concentrations of ambient particulate matter (such as PM2.5 and PM10) have come to represent a serious environmental problem worldwide, causing many deaths and economic losses. Because of the detrimental effects of PM2.5 on human health, many countries and international organizations have developed and operated regional and global short-term PM2.5 prediction systems. The short-term predictability of PM2.5 (and PM10) is determined by two main factors: the performance of the air quality model and the precision of the initial states. While specifically focusing on the latter factor, this study attempts to demonstrate how information from classical ground observation networks, a state-of-the-art geostationary (GEO) satellite sensor, and an advanced air quality modeling system can be synergistically combined to improve short-term PM2.5 predictability over South Korea. Such a synergistic combination of information can effectively overcome the major obstacle of scarcity of information, which frequently occurs in PM2.5 prediction systems using low Earth orbit (LEO) satellite-borne observations. This study first presents that the scarcity of information is mainly associated with cloud masking, sun-glint effect, and ill-location of satellite-borne data, and it then demonstrates that an advanced air quality modeling system equipped with synergistically-combined information can achieve substantially improved performances, producing enhancements of approximately 10%, 19%, 29%, and 10% in the predictability of PM2.5 over South Korea in terms of index of agreement (IOA), correlation coefficient (R), mean biases (MB), and hit rate (HR), respectively, compared to PM2.5 prediction systems using only LEO satellite-derived observations. -
dc.identifier.bibliographicCitation NPJ CLIMATE AND ATMOSPHERIC SCIENCE, v.6, no.1, pp.41 -
dc.identifier.doi 10.1038/s41612-023-00363-w -
dc.identifier.issn 2397-3722 -
dc.identifier.scopusid 2-s2.0-85160049394 -
dc.identifier.uri https://scholarworks.unist.ac.kr/handle/201301/64499 -
dc.identifier.wosid 000992414100001 -
dc.language 영어 -
dc.publisher Nature Publishing Group -
dc.title Synergistic combination of information from ground observations, geostationary satellite, and air quality modeling towards improved PM2.5 predictability -
dc.type Article -
dc.description.isOpenAccess TRUE -
dc.relation.journalWebOfScienceCategory Meteorology & Atmospheric Sciences -
dc.relation.journalResearchArea Meteorology & Atmospheric Sciences -
dc.type.docType Article -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
dc.subject.keywordPlus FORECASTING SYSTEM -
dc.subject.keywordPlus CHEMISTRY -
dc.subject.keywordPlus PREDICTION -
dc.subject.keywordPlus IMPACT -
dc.subject.keywordPlus OZONE -
dc.subject.keywordPlus V1 -

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