FN Archimer Export Format
PT J
TI Statistical processing of West Africa wave directional spectra time-series into a climatology of swell events
BT 
AF OLAGNON, Michel
   KPOGO-NUWOKLO, Komlan
   GUEDE, Zakoua
AS 1:1;2:1,2;3:1;
FF 1:PDG-REM-RDT-LCSM;2:PDG-REM-RDT-LCSM;3:PDG-REM-RDT-LCSM;
C1 Ifremer Ctr Brest, F-29280 Plouzane, France.
   Univ Abomey Calavi, Cotonou, Benin.
C2 IFREMER, FRANCE
   UNIV ABOMEY CALAVI, BENIN
SI BREST
SE PDG-REM-RDT-LCSM
IN WOS Ifremer jusqu'en 2018
   copubli-int-hors-europe
   copubli-sud
IF 2.508
TC 4
UR https://archimer.ifremer.fr/doc/00156/26695/24761.pdf
LA English
DT Article
DE ;Joint probabilities;Metocean time-series;Swell spectral models;Ocean wave climate;Event tracking;Offshore fatigue design
AB Accurate estimation of long-term sea conditions is a major issue for the design of coastal and offshore structures, for the preparation of marine operations, and for other applications such as marine energy, and coastal erosion. It requires, on the one hand, proper parametric models of the sea state spectra and the statistics of the parameters, and on the other hand, representations of their time evolutions. In some locations such as West Africa, sea conditions are complex with wave spectra showing many well separated peaks corresponding to several swells and wind sea. The present study focuses on swell at a West Africa location. First, a time-consistent triangular model is assessed for the spectral shapes of the swell components. Then statistical analysis of the time-histories of those components is carried out in connection with the storms at their source. A model that is triangular for Hs and a linear trend for period and direction is found appropriate for the time-histories of those parameters within a storm event. Using the empirical distributions of the characteristics of the individual events parameters, it is shown that arbitrary long durations of the swell climate may be reconstructed preserving the main observed statistical properties.
PY 2014
PD FEB
SO Journal Of Marine Systems
SN 0924-7963
PU Elsevier Science Bv
VL 130
UT 000328873600010
BP 101
EP 108
DI 10.1016/j.jmarsys.2013.07.003
ID 26695
ER

EF