Bulk wave parameters, such as wave height and wave period, are required for engineering and environmental applications. In this study, measured wave data from six shallow-water locations in the data-sparse north Indian Ocean are used to assess the Interim European Centre for Medium-Range Weather Forecasts (ECMWF) Re-Analysis [ERA-Interim (ERA-I)] wave height and period data in the nearshore waters around India. The difference between the ERA-I significant wave height (SWH) and the buoy SWH varies from −1 to 1 m with an average value of −0.1 m for the three west coast locations. For the three east coast locations, the variation ranges from −2.2 to 1.7 m with an average value of −0.2 m. The ERA-I SWH data show positive biases, indicating an overall overestimation for all locations except for the northern location in the west coast of India, where underestimation is observed. During the tropical cyclone period, a large (~33%) underestimation of SWH in the ERA-I data is observed. Hence, the ERA-I SWH data cannot be used for design applications without site-specific validation. The difference between the wave period from ERA-I and the energy wave period from the buoy varies from −6 to 4 s with an average value of −0.3 s. The intercomparisons suggest that the ERA-I dataset shows encouraging agreement with the annual mean SWH and the energy wave period.

In this study, the wind and the surface waves in the Indian Ocean (IO) during 1979–2017 are studied based on the ECMWF ERA5 Reanalysis data. Long‐term statistical analysis of extreme waves is carried out based on Generalized Extreme Value distribution using annual maxima and the spatial distribution of return levels for 50 and 100 years are studied. In general, the ERA5 significant wave height (Hs) and maximum wave height (Hmax) show a good agreement with measured buoy data in the coastal (bias ~0.29 m) and deep waters (bias ~0.18 m). During the tropical cyclone, underestimation of Hs and Hmax in the ERA5 data compared to buoy data is 2.7 and 1.4%, but in general the bias is large (~0.69 m). Swell domination is observed in larger regions of the IO, whereas wind‐seas are comparable to swells in the Southern Ocean. The meridional wind speed largely influences the spatial pattern of Hs in the North IO. The stronger winds over the Southern Ocean play a major role in generating higher waves at higher latitudes. Maximum value of the 100‐year return level for Hs in IO is 17.8 m, whereas highest value of the Hs is 16.7 m and Hmax is 32.0 m. Severe wave events are common at 50°–60°S and only during 25% of the time in a year, Hmax is less than 5 m in this region. Ratio of the Hmax to Hs varies from 1.46 to 2.3 with a mean value of 1.87. The 100‐year return value of Hs changed by −4 to 5 m, when the length of the dataset is decreased from 39 years (1979–2017) to recent 20 years. On an average, from 1979 to 2017, the annual average Hmax increased by 0.73 cm/year. In major areas of the IO, a clear decrease of the Hs is observed during 1991–2017, whereas during 1979–2017, an increase in Hs is found.

Abstract. 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 (H s ) for different periods are estimated using the generalized extreme value distribution (GEV) and generalized Pareto distribution (GPD) based on the Waverider buoy data spanning 8 years and the ERA-Interim reanalysis data spanning 38 years. The analysis is carried out for wind-sea, swell and total H s 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 premonsoon 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 100 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 H s 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 H s , and hence there is a significant difference in the 100-year return level H s 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 H s value.