Assessment and improvement of global gridded sea surface temperature datasets in the yellow sea using in situ ocean buoy and research vessel observations SCIE SCOPUS

DC Field Value Language
dc.contributor.author Kwon K. -
dc.contributor.author Choi B.-J. -
dc.contributor.author Kim S.-D. -
dc.contributor.author Lee S.-H. -
dc.contributor.author Park K.-A. -
dc.date.accessioned 2020-12-10T07:51:25Z -
dc.date.available 2020-12-10T07:51:25Z -
dc.date.created 2020-05-08 -
dc.date.issued 2020-05 -
dc.identifier.issn 2072-4292 -
dc.identifier.uri https://sciwatch.kiost.ac.kr/handle/2020.kiost/38678 -
dc.description.abstract The sea surface temperature (SST) is essential data for the ocean and atmospheric prediction systems and climate change studies. Five global gridded sea surface temperature products were evaluated with independent in situ SST data of the Yellow Sea (YS) from 2010 to 2013 and the sources of SST error were identified. On average, SST from the gridded optimally interpolated level 4 (L4) datasets had a root mean square difference (RMSD) of less than 1 °C compared to the in situ observation data of the YS. However, the RMSD was relatively high (2.3 °C) in the shallow coastal region in June and July and this RMSD was mostly attributed to the large warm bias (>2 °C). The level 3 (L3) SST data were frequently missing in early summer because of frequent sea fog formation and a strong (>1.2 °C/12 km) spatial temperature gradient across the tidal mixing front in the eastern YS. The missing data were optimally interpolated from the SST observation in offhore warm water and warm biased SST climatology in the region. To fundamentally improve the accuracy of the L4 gridded SST data, it is necessary to increase the number of SST observation data in the tidally well mixed region. As an interim solution to the warm bias in the gridded SST datasets in the eastern YS, the SST climatology for the optimal interpolation can be improved based on long-term in situ observation data. To reduce the warm bias in the gridded SST products, two bias correction methods were suggested and compared. Bias correction methods using a simple analytical function and using climatological observation data reduced the RMSD by 19-29% and 37-49%, respectively, in June. © 2020 by the authors. -
dc.description.uri 1 -
dc.language English -
dc.publisher MDPI AG -
dc.title Assessment and improvement of global gridded sea surface temperature datasets in the yellow sea using in situ ocean buoy and research vessel observations -
dc.type Article -
dc.citation.title Remote Sensing -
dc.citation.volume 12 -
dc.citation.number 5 -
dc.identifier.bibliographicCitation Remote Sensing, v.12, no.5 -
dc.identifier.doi 10.3390/rs12050759 -
dc.identifier.scopusid 2-s2.0-85081913569 -
dc.identifier.wosid 000531559300014 -
dc.type.docType Article -
dc.description.journalClass 1 -
dc.description.isOpenAccess N -
dc.subject.keywordPlus EAST CHINA SEA -
dc.subject.keywordPlus DATA ASSIMILATION -
dc.subject.keywordPlus FOG EVENT -
dc.subject.keywordPlus SATELLITE -
dc.subject.keywordPlus SYSTEM -
dc.subject.keywordPlus VALIDATION -
dc.subject.keywordPlus MODEL -
dc.subject.keywordPlus SUMMER -
dc.subject.keywordAuthor sea surface temperature -
dc.subject.keywordAuthor global gridded dataset -
dc.subject.keywordAuthor validation -
dc.subject.keywordAuthor evaluation -
dc.subject.keywordAuthor Yellow Sea -
dc.subject.keywordAuthor bias correction -
dc.relation.journalWebOfScienceCategory Remote Sensing -
dc.description.journalRegisteredClass scie -
dc.description.journalRegisteredClass scopus -
Appears in Collections:
Ocean Research, Operations & Support Department > Marine Bigdata Center > 1. Journal Articles
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