Clustering of Synoptic Pattern over the Korean Peninsula from Meteorological Models

Title
Clustering of Synoptic Pattern over the Korean Peninsula from Meteorological Models
Author(s)
김진아; 허기영; 최정운; 정상훈
KIOST Author(s)
Kim, Jinah(김진아)Heo, Ki-Young(허기영)Choi, Jung Woon(최정운)Jeong, Sang Hun(정상훈)
Publication Year
2017-04-26
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.
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/24033
Bibliographic Citation
EGU General Assembly 2017, pp.1, 2017
Publisher
European Geosciences Union
Type
Conference
Language
English
Publisher
European Geosciences Union
Related Researcher
Research Interests

AI/Machine Learning,Climate Change,Marine Disaster,인공지능/기계학습,기후변화,해양기상재해

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