Clustering of Synoptic Pattern over the Korean Peninsula from Meteorological Models
DC Field | Value | Language |
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dc.date.accessioned | 2020-07-15T15:52:40Z | - |
dc.date.available | 2020-07-15T15:52:40Z | - |
dc.date.created | 2020-02-11 | - |
dc.date.issued | 2017-04-26 | - |
dc.identifier.uri | https://sciwatch.kiost.ac.kr/handle/2020.kiost/24034 | - |
dc.description.abstract | In our study, we propose unsupervised machine learning method for pattern clustering and applied it to classify a pattern which has occurred abnormal high waves using numerical meteorological model’s reanalysis data from 2000 to 2015 and past historical records of accidents by abnormal high waves. About 25,000 patterns of total spatial distribution of sea surface pressure are clustered into 30 patterns and they are classified into seasonal sea level pressure patterns based on meteorological characteristics of Korean peninsula. Moreover, in order to determine the representative patterns which occurs abnormal high waves, we classified it again using historicalaccidents cases among the winter season pressure patterns.ast historical records of accidents by abnormal high waves. About 25,000 patterns of total spatial distribution of sea surface pressure are clustered into 30 patterns and they are classified into seasonal sea level pressure patterns based on meteorological characteristics of Korean peninsula. Moreover, in order to determine the representative patterns which occurs abnormal high waves, we classified it again using historicalaccidents cases among the winter season pressure patterns. | - |
dc.description.uri | 1 | - |
dc.language | English | - |
dc.publisher | European Geosciences Union | - |
dc.relation.isPartOf | EGU General Assembly 2017 | - |
dc.title | Clustering of Synoptic Pattern over the Korean Peninsula from Meteorological Models | - |
dc.type | Conference | - |
dc.citation.endPage | 1 | - |
dc.citation.startPage | 1 | - |
dc.citation.title | EGU General Assembly 2017 | - |
dc.identifier.bibliographicCitation | EGU General Assembly 2017, pp.1 | - |
dc.description.journalClass | 1 | - |