3 research outputs found
Variations in return value estimate of ocean surface waves – a study based on measured buoy data and ERA-Interim reanalysis data
An assessment
of extreme wave characteristics during the design of marine facilities not
only helps to ensure their safety but also assess the economic aspects. In
this study, return levels of significant wave height (Hs) for different
periods are estimated using the generalized extreme value distribution (GEV)
and generalized Pareto distribution (GPD) based on the Waverider buoy data
spanning 8Â years and the ERA-Interim reanalysis data spanning 38Â years. The
analysis is carried out for wind-sea, swell and total Hs separately for
buoy data. Seasonality of the prevailing wave climate is also considered in
the analysis to provide return levels for short-term activities in the
location. The study shows that the initial distribution method (IDM)
underestimates return levels compared to GPD. The maximum return levels
estimated by the GPD corresponding to 100 years are 5.10 m for the monsoon
season (JJAS), 2.66 m for the pre-monsoon season (FMAM) and 4.28 m for the
post-monsoon season (ONDJ). The intercomparison of return levels by block
maxima (annual, seasonal and monthly maxima) and the r-largest method for GEV
theory shows that the maximum return level for 100Â years
is 7.20 m in the r-largest series followed by monthly maxima (6.02 m) and
annual maxima (AM) (5.66 m) series. The analysis is also carried out to
understand the sensitivity of the number of observations for the GEV annual
maxima estimates. It indicates that the variations in the standard deviation
of the series caused by changes in the number of observations are positively
correlated with the return level estimates. The 100-year return level results of Hs
using the GEV method are comparable for short-term (2008 to 2016)
buoy data (4.18 m) and long-term (1979 to 2016) ERA-Interim shallow data
(4.39 m). The 6 h interval data tend to miss high values of Hs, and hence
there is a significant difference in the 100-year return level Hs obtained
using 6 h interval data compared to data at 0.5 h interval. The study shows
that a single storm can cause a large difference in the 100-year Hs value