学会誌「自然災害科学」

自然災害科学53 Vol.19,No.1, 2000, p69f

【論文】[Regular Paper]

波浪の長期推算に基づく波候と波高極値の推定システム

A System for Estimating Wave Climate and Wave Extremes Based on Long Term Wave Hindcast

畑田佳男*・山口 正隆*・大福 学*・李 敏杰**・野中 浩一***

Yoshio HATADA*, Masataka YAMAGUCHI*, Manabu OHFUKU*, Min Jie LI** and Hirokazu NONAKA***

Abstract

This paper presents a system capable of estimating both wave climate and wave extremes at the coastal sea areas around Japan based on a long term wave hindcasting. Time series of 1-hourly waves over 20 years from 1979 to 1998 are hindcasted with use of a backward ray tracing shallow water wave model on a nesting grid of high topographical resolution under the input condition of 6-hourly wind distribution over the Northwestern Pacific Ocean recompiled from the ECMWF analysis data sets. From the time series of hindcasted and observed waves, various kinds of wave climate parameters and error statistics are estimated separately for each month, season and year over the whole period, and also calculated for months, seasons and years over the 20-year period. Annual maximum and peak over threshold wave height data including extreme data stratified for several periods of a year are extracted and then extreme wave analysis for each of the data sets is conducted with use of the Goda-type model based on the least square method. The main conclusion is that the system may be highly efficient for the estimation of not only wave climate but also wave extremes at the sea areas around Japan.

キーワード:

波浪の長期推算,波候,波高極値,ECMWF風資料,1点浅海モデル

Key words:

long term wave hindcast, wave climate, wave extremes, ECMWF wind data, backward ray tracing shallow water wave model

*
愛媛大学工学部環境建設工学科
Department of Civil and Environmental Engineering, Faculty of Engineering, Ehime University
**
愛媛大学大学院理工学研究科博士前期課程土木海洋工学専攻
Course of Civil and Ocean Engineering, Graduate School of Science and Engineering, Ehime University
***
愛媛大学大学院理工学研究科博士後期課程生産工学専攻
Course of Engineering for Productions, Graduate School of Science and Engineering, Ehime University