Korean East Coast wave predictions by means of ensemble Kalman filter data assimilation SCIE SCOPUS

Cited 3 time in WEB OF SCIENCE Cited 3 time in Scopus
Title
Korean East Coast wave predictions by means of ensemble Kalman filter data assimilation
Author(s)
Caires, Sofia; Kim, Jinah; Groeneweg, Jacco
KIOST Author(s)
Kim, Jinah(김진아)
Publication Year
2018-11
Abstract
To respond to the need for preventing offshore and coastal accidents, damage and flooding, a state-of-the-art coastal wave forecast system for the East Coast of Korea waters is being developed. Given that the quality of the input wind has been identified as the main factor influencing the quality of the wave results, the effectiveness of adjusting the wind fields by means of data assimilation using the ensemble Kalman filter technique has been explored. In this article the model setup, the data assimilation parameters and the validation of the predictions during stormy periods is described. The validation shows that the model is able to provide predictions of coastal waves fulfilling available benchmarks; especially, the data assimilation analysis and forecast predictions are judged to be of high quality.
ISSN
1616-7341
URI
https://sciwatch.kiost.ac.kr/handle/2020.kiost/812
DOI
10.1007/s10236-018-1214-0
Bibliographic Citation
OCEAN DYNAMICS, v.68, no.11, pp.1571 - 1592, 2018
Publisher
SPRINGER HEIDELBERG
Subject
WIND; REANALYSIS; SYSTEM
Keywords
EnKF; SWAN; Wave modelling; East Coast of Korea
Type
Article
Language
English
Document Type
Article; Proceedings Paper
Publisher
SPRINGER HEIDELBERG
Related Researcher
Research Interests

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

Files in This Item:
There are no files associated with this item.

qrcode

Items in ScienceWatch@KIOST are protected by copyright, with all rights reserved, unless otherwise indicated.

Browse