Error quantification of abnormal extreme high waves in Operational Oceanographic System in Korea

Title
Error quantification of abnormal extreme high waves in Operational Oceanographic System in Korea
Alternative Title
Error quantification of abnormal extreme high waves in Operational Oceanographic System in Korea
Author(s)
정상훈; 김진아; 허기영; 박광순
KIOST Author(s)
Jeong, Sang Hun(정상훈)Kim, Jinah(김진아)Heo, Ki-Young(허기영)
Publication Year
2017-04-27
Abstract
In winter season, large-height swell-like waves have occurred on the East coast of Korea, causing property damages and loss of human life. It is known that those waves are generated by a local strong wind made by temperate cyclone moving to eastward in the East Sea of Korean peninsula. Because the waves are often occurred in the clear weather, in particular, the damages are to be maximized. Therefore, it is necessary to predict and forecast large-height swell-like waves to prevent and correspond to the coastal damages.In this study, we proposed an appropriate method of error quantification especially on abnormal high waves which are occurred by local weather condition. Furthermore, we introduced that how the quantification errors are contributed to improve wind-wave modeling by applying data assimilation and utilizing reanalysis data. eastward in the East Sea of Korean peninsula. Because the waves are often occurred in the clear weather, in particular, the damages are to be maximized. Therefore, it is necessary to predict and forecast large-height swell-like waves to prevent and correspond to the coastal damages.In this study, we proposed an appropriate method of error quantification especially on abnormal high waves which are occurred by local weather condition. Furthermore, we introduced that how the quantification errors are contributed to improve wind-wave modeling by applying data assimilation and utilizing reanalysis data.
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/24014
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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