Simulated Significant Wave Height Research Articles

Optimized WAVEWATCH Ⅲ for significant wave height computation using machine learning

This paper addresses the improvement of the skill of a wave generation model in calculating the significant wave height of sea states by proposing a wave correction model based on machine learning techniques. The model corrects the simulated significant wave heights(Hs) by fitting the discrepancies between numerical simulation results and buoy measurement data. We consider the effects of boundaries and the simplified computation of source terms in the wave model, employing a wave correction model to optimize wave results. The research results demonstrate that the integration of the correction model with the physical calculation model can significantly enhance both accuracy and efficiency in wave computations. The MAPE for Hs decreased from 48.38% to 7.14% and from 20.68% to 5.09%. Simultaneously, computational efficiency improved by factors of 1 and 133.2, respectively. Furthermore, the model demonstrated good generalization capabilities, enabling high-precision wave simulations for other locations within the sea area.

Influence of sea surface waves on numerical modeling of an oil spill: Revisit of symphony wheel accident

The greatest purpose of this study is to analyze the importance of surface waves on the hindcasting of the oil spill through the Symphony wheel accident in the Qingdao coastal waters. During the accident period, a total of four synthetic aperture radar (SAR) images by Gaofen-3 (GF-3) were acquired from 2 to 19 May 2021. The hindcasting of two sea surface dynamics, namely currents and waves, is carried out using a coupled marine numeric model. This model, known as the finite-volume community ocean model-simulating waves nearshore (FVCOM-SWAVE), employs a triangular grid. Simulated significant wave height (SWH) is validated against remotely sensed product by the Haiyang-2B (HY-2B) altimeter on April 2021 yields a root mean square error (RMSE) of 0.38, a correlation coefficient (COR) of 0.78, and a scatter index (SI) of 0.34. Subsequently, Stokes drift estimated by waves are included to hindcasting oil spills using the oil particle-tracing method. The bias of the spatial coverage (SAR minus simulations) of an algorithm called the constant false alarm rate (CFAR) is −73.92 km2 with Stokes drift, which is significantly less than the 55.45 km2 coverage without Stokes drift. Moreover, compared with model-simulated oil spills, the bias of the geographic location at the center point with Stokes drift is 8.18 km, which is less than the 12.95 km bias without Stokes drift. These results demonstrate that Stokes drift needs to be included in the prediction of oil spills.

Current Effect on Wave Condition around Island in the South China Sea

Abstract Currents have a significant impact on wave parameters around islands. In this study, high-resolution unsteady current simulations based on island geography and wind fields from Weather Research and Forecasting (WRF) Model are used as input sources. The wave action balance model uses an unstructured grid to assess the wave conditions in the Atoll during Typhoon Noul. The characteristic wave parameters, with and without the effect of currents, are compared with the field observation data, including significant wave height, wave period, and the spatial distribution of significant wave height. The results show that simulated significant wave heights and wave periods are close to observed data, considering the effect of currents. The energy and shape of the spectrum are also verified during Typhoon Noul, and the observed agreement is improved when considering the currents. The effects of current within the Atoll are relatively weaker compared to the surroundings, while stronger current effects are observed in the deeper water outside the Atoll. Refraction caused by current expands the area of moderate sea state behind the island. Significance Statement Several innovations of this article are as follows: 1) the influence of currents on wave conditions at the Atoll; 2) exploring the impact of currents using key parameters, such as significant wave height, wave period, and wave spectrum, especially during the passage of Typhoon Noul; 3) swell emerges as the dominant factor influencing wave conditions as the center of Typhoon Noul gradually moves away; and 4) refraction caused by current expands the area of moderate sea state behind the island.

Wave and Meso-Scale Eddy Climate in the Arctic Ocean

Under global climate change, the characteristics of oceanic dynamics are gradually beginning to change due to melting sea ice. This study focused on inter-annual variation in waves and mesoscale eddies (radius > 40 km) in the Arctic Ocean from 1993 to 2021. The waves were simulated by a numerical wave model, WAVEWATCH-III (WW3), which included a parameterization of ice–wave interaction. The long-term wind data were from the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-5), and current and sea level data from the HYbrid Coordinate Ocean Model (HYCOM)were used as the forcing fields. The simulated significant wave heights (SWHs) were validated against the 2012 measurements from the Jason-2 altimeter, yielding a 0.55 m root mean square error (RMSE) with a 0.95 correlation (COR). The seasonal variation in WW3-simulated SWH from 2021 to 2022 showed that the SWH was the lowest in summer (July and August 2021) and highest in winter (November 2021 to April 2022). This result indicates that parts of the Arctic could become navigable in summer. The mesoscale eddies were identified using a daily-averaged sea level anomalies (SLA) product with a spatial resolution of a 0.25° grid for 1993−2021. We found that the activity intensity (EKE) and radius of mesoscale eddies in the spatial distribution behaved in opposing ways. The analysis of seasonal variation showed that the increase in eddy activity could lead to wave growth. The amplitude of SWH peaks was reduced when the Arctic Oscillation Index (AOI) was <−1.0 and increased when the AOI was >0.5, especially in the case of swells. The amplitude of SWH oscillation was low, and the EKE and radius of eddies were relatively small. Although the radius and EKE of eddies were almost similar to the AOI, the waves also influenced the eddies.

Spatial and temporal wave climate variability along the south coast of Sweden during 1959–2021

This study presents 62 years of hindcast wave climate data for the south coast of Sweden from 1959–2021. The 100-km-long coast consists mainly of sandy beaches and eroding bluffs interrupted by headlands and harbours alongshore, making it sensitive to variations in incoming wave direction. A SWAN wave model of the Baltic Sea, extending from the North Sea to the Åland Sea, was calibrated and validated against wave observations from 16 locations distributed within the model domain. Wave data collected from open databases were complemented with new wave buoy observations from two nearshore locations within the study area at 14 and 15 m depth. The simulated significant wave height showed good agreement with the local observations, with an average R2 of 0.83. The multi-decadal hindcast data was used to analyse spatial and temporal wave climate variability. The results show that the directional distribution of incoming waves varies along the coast, with a gradually increasing wave energy exposure from the west towards the east. The wave climate is most energetic from October to March, with the highest wave heights in November, December, and January. In general, waves from westerly directions dominate the annual wave energy, but within the hindcast time series, a few years had larger wave energy from easterly directions. The interannual variability of total wave energy and wave direction is correlated to the North Atlantic Oscillation (NAO) index. In the offshore, the total annual wave energy had a statistically significant positive correlation with the NAO DJFM station-based index, with a Spearman rank correlation coefficient of 0.51. In the nearshore, the correlation was even stronger. Future studies should investigate the possibility of using the NAO index as a proxy for the wave energy direction and its effect on coastal evolution.

Comparative analysis of significant wave height between satellite altimetry data and SWAN model simulations in the Black Sea basin

Significant wave height (Hs) data are essential in different scientific studies regarding coastal and marine environments, coastal management, various economic activities nearshore and offshore, as well as in the use of marine renewable energy, oil and gas offshore platform operation and security, safety of personnel and equipment. The current paper presents an analysis of the significant wave height parameter for the particular case of the Black Sea basin. In order to achieve this analysis, satellite altimetry measurements from five different satellites taken from the IMOS (Integrated Marine Observing System) database were used and compared with the results of SWAN model simulations in order to assess the accuracy of simulated Hs values. The analysis was performed on data available for a period of 3 years, from 2018 to 2020. The comparison of the simulated significant wave height with data measured by altimetry satellites showed a good correlation of 0.8. The obtained results bring a contribution with regards to a better insight and knowledge into the characteristics of wind waves in the Black Sea.

The Feasibility of the ERA5 Forced Numerical Wave Model in Fetch-Limited Basins

Numerical wave models are critical in hindcasting reliable long-term time series of significant wave heights, which play a crucial role in coastal and ocean engineering activities. Although wind fields are an important input to numerical wave models, few studies have investigated the feasibility of the widely used ERA5 wind reanalysis dataset in fetch-limited basins. In this work, we investigated the feasibility of the ERA5 forced numerical wave model (SWAN) in fetch-limited basins. ERA5 wind velocities were first compared to ground-based meteorological stations, showing poorer accuracy compared to finer gridded ALADIN wind data. Subsequently, the white-capping coefficient Cds in the Janssen white-capping formulation was calibrated separately using a surrogate model when establishing the ERA5 and ALADIN forced wave models. The calibrated ERA5 forced model showed a similar agreement to wave buoy data as the calibrated ALADIN forced wave model during the calibration period and even superior accuracy in the validation period. Overall, these results show that the wave model calibration procedure mitigates the effect of the poorer accuracy of the ERA5 wind data on the significant wave height results. Nevertheless, both ERA5 and ALADIN forced wave models showed an alarming overprediction for high simulated significant wave heights.

Dynamical Projections of the Mean and Extreme Wave Climate in the Bohai Sea, Yellow Sea and East China Sea

Few studies have focused on the projected future changes in wave climate in the Chinese marginal seas. For the first time, we investigate the projected changes of the mean and extreme wave climate over the Bohai Sea, Yellow Sea, and East China Sea (BYE) during two future periods (2021–2050 and 2071–2100) under the RCP2.6 and RCP8.5 scenarios from the WAM wave model simulations with a resolution of 0.1°. This is currently the highest-resolution wave projection dataset available for the study domain. The wind forcings for WAM are from high-resolution (0.22°) regional climate model (RCM) CCLM-MPIESM simulations. The multivariate bias-adjustment method based on the N-dimensional probability density function transform is used to correct the raw simulated significant wave height (SWH), mean wave period (MWP), and mean wave direction (MWD). The annual and seasonal mean SWH are generally projected to decrease (-0.15 to -0.01 m) for 2021–2050 and 2071–2100 under the RCP2.6 and RCP8.5 scenarios, with statistical significance at a 0.1 level for most BYE in spring and for most of the Bohai Sea and Yellow Sea in annual and winter/autumn mean. There is a significant decrease in the spring MWP for two future periods under both the RCP2.6 and RCP8.5 scenarios. In contrast, the annual and summer/winter 99th percentile SWH are generally projected to increase for large parts of the study domain. Results imply that the projected changes in the mean and 99th percentile extreme waves are very likely related to projected changes in local mean and extreme surface wind speeds, respectively.

Cyclonic Wave Simulations Based on WAVEWATCH-III Using a Sea Surface Drag Coefficient Derived from CFOSAT SWIM Data

It is well known that numerical models are powerful methods for wave simulation of typhoons, where the sea surface drag coefficient is sensitive to strong winds. With the development of remote sensing techniques, typhoon data (i.e., wind and waves) have been captured by optical and microwave satellites such as the Chinese-French Oceanography SATellite (CFOSAT). In particular, wind and wave spectra data can be simultaneously measured by the Surface Wave Investigation and Monitoring (SWIM) onboard CFOSAT. In this study, existing parameterizations for the drag coefficient are implemented for typhoon wave simulations using the WAVEWATCH-III (WW3) model. In particular, a parameterization of the drag coefficient derived from sea surface roughness is adopted by considering the terms for wave steepness and wave age from the measurements from SWIM products of CFOSAT from 20 typhoons during 2019–2020 at winds up to 30 m/s. The simulated significant wave height (Hs) from the WW3 model was validated against the observations from several moored buoys active during three typhoons, i.e., Typhoon Fung-wong (2014), Chan-hom (2015), and Lekima (2019). The analysis results indicated that the proposed parameterization of the drag coefficient significantly improved the accuracy of typhoon wave estimation (a 0.49 m root mean square error (RMSE) of Hs and a 0.35 scatter index (SI)), greater than the 0.55 RMSE of Hs and >0.4 SI using other existing parameterizations. In this sense, the adopted parameterization for the drag coefficient is recommended for typhoon wave simulations using the WW3 model, especially for sea states with Hs < 7 m. Moreover, the accuracy of simulated waves was not reduced with growing winds and sea states using the proposed parameterization. However, the applicability of the proposed parameterization in hurricanes necessitates further investigation at high winds (>30 m/s).

Wave‐Current Interactions During Hurricanes Earl and Igor in the Northwest Atlantic

AbstractInteractions between surface waves and ocean currents over the northwest Atlantic during Hurricanes Earl and Igor (2010) are investigated using a coupled wave‐circulation model and observational data. Our results show that the coupled model improves the model performance in simulating the surface waves, sea level, water temperature, and salinity. By including the wave‐current interactions (WCIs), the maximum significant wave heights (SWHs) are reduced by more than 10% along the storm tracks and more than 20% over eddies. In the Gulf of Maine (GoM), the simulated SWHs and peak wave periods are also modulated by strong tidal currents. Surface waves, in turn, reduce the semidiurnal tidal amplitudes (up to ∼9%) and currents (up to ∼40%) in the GoM due mainly to the wave‐enhanced bottom stress. Surface waves also increase the peak storm surge (up to ∼19%) during these hurricanes, which is mostly attributed to the wave‐induced forces through Stokes drift and wave dissipation but slightly compensated by the wave‐enhanced bottom stress. The hurricane‐driven surface currents are modulated by waves toward the clockwise direction with a decrease in current speeds of more than ∼60%. This is attributed to the reduced current shear and density gradient resulting from mixing enhanced by wave breaking, but compensated by wave‐induced forces. Salinity and temperature are also modulated by surface waves, mostly through the wave‐induced forces and enhanced mixing by wave breaking. Overall, the coupled model produces higher surface salinity and stronger surface cooling due to the inclusion of the WCIs.

Comparative analysis of parametric cyclone models and relations for radius of maximum winds for storm surge simulations: case of Typhoon Meranti 2016

ABSTRACT Parametric cyclone models consist of relations for a pressure field and wind field based on specified typhoon parameters such as coordinates, central pressure, maximum wind, and radius of maximum wind. Such models generate the forcing conditions for simulations of storm surges. This study investigated the cyclone model parameters considering the Typhoon Meranti event, which made landfall in Batanes Islands of the Philippines in 2016. Available empirical equations for radius of maximum wind were compared and it showed that the equation developed by Vickery and Wadhera in 2008 best estimated the radius of maximum winds for the dataset considered. Comparisons of modeled and observed winds and pressures using various parametric cyclone models showed that the 1952 Fujitamodel gave more accurate pressure fields, while the 2010 Holland model gave more accurate wind fields. Based on storm surge simulations of Typhoon Meranti, the 1952 Fujita cyclone model gave the more accurate simulation of the storm tides. The 2010 Holland cyclone model generally has lower wind fields which reduced the wind surge component and the simulated significant wave heights which also reduces the contribution of wave setup to the storm tide levels.

A comparative study of viscoelastic models for ocean wave dissipation in ice-covered regions of the eastern Canadian shelf

A nested-grid ocean surface wave model based on WAVEWATCH III (WW3) is used to investigate wave propagations in the ice-covered regions of the eastern Canadian shelf (ECS) during a winter storm in March 2014. The applicability of two viscoelastic models (known as IC3 and IC5) for wave dissipation in ice is investigated. Two essential ice rheological parameters (kinetic viscosity v and elasticity G) in these two viscoelastic models are determined by comparing the simulated significant wave heights (SWHs) with observations from altimeters and buoys. Both viscoelastic models reproduce reasonably well the observed wave propagations in ice with the average scatter index less than 12.5% for the SWHs near the ice edge. In the inner ice pack, however, IC3 is superior to IC5, since IC5 produces unrealistic large waves. The two viscoelastic models perform differently in the inner ice pack, due largely to the nonlinear effect of different wave dissipation rates on the wind input for surface waves. In comparison with IC5, IC3 has a stronger dependence of the wave dissipation rate on the wave frequency and thus generates more rapidly wave energy decay in ice. Wave scattering, which is typically not considered in many previous studies, significantly modifies wave propagations in ice over the ECS during the study period. Wave scattering leads to large decreases of SWHs and increases of mean wave periods in the inner ice pack due to the nonlinear effect on the wind input associated with broadening of wave directional spreads.

On the Sensitivity of Typhoon Wave Simulations to Tidal Elevation and Current

The sensitivity of storm wave simulations to storm tides and tidal currents was investigated using a high-resolution, unstructured-grid, coupled circulation-wave model (Semi-implicit Cross-scale Hydroscience Integrated System Model Wind Wave Model version III (SCHISM-WWM-III)) driven by two typhoon events (Typhoons Soudelor and Megi) impacting the northeastern coast of Taiwan. Hourly wind fields were acquired from a fifth-generation global atmospheric reanalysis (ERA5) and were used as meteorological conditions for the circulation-wave model after direct modification (MERA5). The large typhoon-induced waves derived from SCHISM-WWM-III were significantly improved with the MERA5 winds, and the peak wave height was increased by 1.0–2.0 m. A series of numerical experiments were conducted with SCHISM-WWM-II and MERA5 to explore the responses of typhoon wave simulations to tidal elevation and current. The results demonstrate that the simulated significant wave height, mean wave period and wave direction for a wave buoy in the outer region of the typhoon are more sensitive to the tidal current but less sensitive to the tidal elevation than those for a wave buoy moored in the inner region of the typhoon. This study suggests that the inclusion of the tidal current and elevation could be more important for typhoon wave modeling in sea areas with larger tidal ranges and higher tidal currents. Additionally, the suitable modification of the typhoon winds from a global atmospheric reanalysis is necessary for the accurate simulation of storm waves over the entire region of a typhoon.

Analysis of Wave Fields under Tropical Cyclones in the South China Sea

Sun, W.; Shao, Z.; Liang, B., and Gao, H., 2020. Analysis of wave fields under tropical cyclones in the South China Sea. In: Zheng, C.W.; Wang, Q.; Zhan, C., and Yang, S.B. (eds.), Air-Sea Interaction and Coastal Environments of the Maritime and Polar Silk Roads. Journal of Coastal Research, Special Issue No. 99, pp. 126–130. Coconut Creek (Florida), ISSN 0749-0208.The study of wave fields under extreme weather conditions, such as tropical cyclones, is becoming increasingly important for ocean engineering. In this work, wave simulation under a tropical cyclone is carried out in the South China Sea (SCS). The SWAN model is driven by the blended wind field. In this model, the wind input term and whitecapping dissipation term are specifically studied to obtain a reliable model result. During tropical cyclones Doksuri and Kai-tak in 2012, the waves are simulated in the SCS, and the simulated significant wave heights are validated by the measured data at buoys B1, B2 and B3. The validation results show that the simulated values match the measured values well. By the simulated significant wave heights, the wave field is analyzed during the tropical cyclone. In the mean and maximal conditions, the wave fields are studied with the tropical cyclone track. The maximal significant wave heights at the buoy locations are associated with the corresponding spatial distribution of significant wave heights in the SCS, which can reflect the effect of the tropical cyclone on the wave at a specified point.

Spectral Modeling of Ice-Induced Wave Decay

AbstractThree dissipative (two viscoelastic and one viscous) ice models are implemented in the spectral wave model WAVEWATCH III to estimate the ice-induced wave attenuation rate. These models are then explored and intercompared through hindcasts of two field cases: one in the autumn Beaufort Sea in 2015 and the other in the Antarctic marginal ice zone (MIZ) in 2012. The capability of these dissipative models, along with their limitations and applicability to operational forecasts, are analyzed and discussed. The sensitivity of the simulated wave height to different source terms—the ice-induced wave decay Sice and other physical processes Sother (e.g., wind input, nonlinear four-wave interactions)—is also investigated. For the Antarctic MIZ experiment, Sother is found to be remarkably less than Sice and thus contributes little to the simulated significant wave height Hs. The saturation of dHs/dx at large wave heights in this case, as reported by a previous study, is well reproduced by the three dissipative ice models with or without the utilization of Sother in the ice-infested seas. A clear downward trend in the peak frequency fp is found as Hs increases. As fp decreases, the dominant wave components of a wave spectrum will experience reduced damping by sea ice, and finally result in the flattening of dHs/dx for Hs > 3 m in this specific case. Nonetheless, Sother should not be disregarded within a more general modeling perspective, as our simulations suggest Sother could be comparable to Sice in the Beaufort Sea case where wave and ice conditions are remarkably different.

Long-term variation of storm surge-associated waves in the Bohai Sea

When investigating the long-term variation of wave characteristics as associated with storm surges in the Bohai Sea, the Simulating Waves Nearshore (SWAN) model and ADvanced CIRCulation (ADCIRC) model were coupled to simulate 32 storm surges between 1985 and 2014. This simulation was validated by reproducing three actual wave processes, showing that the simulated significant wave height (SWH) and mean wave period agreed well with the actual measurements. In addition, the long-term variations in SWH, patterns in SWH extremes along the Bohai Sea coast, the 100-year return period SWH extreme distribution, and waves conditional probability distribution were calculated and analyzed. We find that the trend of SWH extremes in most of the coastal stations was negative, among which the largest trend was −0.03 m/a in the western part of Liaodong Bay. From the 100-year return period of the SWH distribution calculated in the Gumbel method, we find that the SWH extremes associated with storm surges decreased gradually from the center of the Bohai Sea to the coast. In addition, the joint probability of wave and surge for the entire Bohai Sea in 100-year return period was determined by the Gumbel logistic method. We therefore, assuming a minimum surge of one meter across the entire Bohai Sea, obtained the spatial SWH distribution. The conclusions of this study are significant for offshore and coastal engineering design.

Impact of Ice Data Quality and Treatment on Wave Hindcast Statistics in Seasonally Ice-Covered Seas

The seasonal ice cover has significant effect on the wave climate of the Baltic Sea. We used the third-generation wave model WAM to simulate the Baltic Sea wave field during four ice seasons (2009†-2012). We used data from two different sources: daily ice charts compiled by FMI’s Ice Service and modeled daily mean ice concentration from SMHI’s NEMO†Nordic model. We utilized two different methods: a fixed threshold of 30 % ice concentration, after which wave energy is set to zero, and a grid obstruction method up to 70 % ice concentration, after which wave energy is set to zero. The simulations run using ice chart data had slightly better accuracy than the simulation using NEMO-Nordic ice data, when compared to altimeter measurements. The analysis of the monthly mean statistics of significant wave height (SWH) showed that the differences between the simulations were relatively small and mainly seen in the Bothnian Bay, the Quark, and the eastern Gulf of Finland. There were larger differences, up to 3.2 m, in the monthly maximum values of SWH. These resulted from individual high wind situations during which the ice edge in the ice chart and NEMO-Nordic was located differently. The two different methods to handle ice concentration resulted only in small differences in the SWH statistics, typically near the ice edge. However, in some individual cases the two methods resulted in quite large differences in the simulated SWH and the handling of ice concentrations as additional grid obstructions could be important, for example, in operational wave forecasting.

Evaluation of Typhoon Waves Simulated by WaveWatch-III Model in Shallow Waters Around Zhoushan Islands

In this study, we simulated typhoon waves in the shallow waters around the Zhoushan Islands using the WaveWatch-III (WW3) model version 5.16, the latest version released by the National Oceanic and Atmospheric Administration. Specifically, we used in-situ measurements to evaluate the performance of seven packages of input/dissipation source terms in the WW3 model. We forced the WW3 model by wind fields derived from a combination of the parametric Holland model and high-resolution European Center for Medium-Range Weather Forecasts (ECMWF) wind data in a 0.125 degrees grid, herein called H-E winds. We trained the H-E winds by fitting a shape parameter B to buoy-measured observations, which resulted in a smallest root mean square error (RMSE) of 3 m s(-1) for B, when treated as a constant 0.4. Then, we applied the seven input/dissipation terms of WW3, labelled ST1, ST2, ST2+STAB2, ST3, ST3+STAB3, ST4, and ST6, to simulate the significant wave height (SWH) up to 5 m during typhoons Fungwong and Chan-hom around the Zhoushan Islands. We then compared the SWHs of the simulated waves with those measured by the in-situ buoys. The results indicate that the simulation using ST2 performs best with an RMSE of 0.79 m for typhoon Fung-wong and an RMSE of 1.12 m for typhoon Chan-hom. Interestingly, we found the simulated SWH results to be relatively higher than those of the observations in the area between Hangzhou Bay and the Zhoushan Islands. This behavior is worthy of further investigation in the future.

Extreme wave climate variability in South China Sea

Extreme wave climate variability in the South China Sea (SCS) was investigated using significant wave height (SWH) data simulated by the third generation wave model WAVEWATCH-III (WW-III) for the period 1976–2014. The wind forcing data was using the objective reanalysis wind dataset from the weather research and forecasting (WRF) model. The simulated SWH was well validated by observation data from satellite altimeter and in-situ buoys. A generalized extreme value (GEV) model was applied to analyze the extreme wave climate variability. Monthly significant wave height maxima is used to characterize the seasonal variability. The spatial distributions of positional and scale parameters are achieved. The positional parameter values reach the maximum in winter, while the scale parameter values are greater in summer and autumn due to the impact of typhoon. The regression analysis of harmonic functions was applied to give the annual cycle. Interannual climatology of extreme wave climate from Empirical Orthogonal Function (EOF) decomposition method and spectrum analysis method revealed a dominant 12 months period and a 22 months period, respectively. Results show that El Nino may significantly affect the extreme wave climate variability in the SCS.

Validation of Operational WAVEWATCH III Wave Model Against Satellite Altimetry Data Over South West Indian Ocean Off-Coast of Tanzania

The WAVEWATCH III model is a third generation wave model and is commonly used for wave forecasting over different oceans. In this study, the performance of WAVEWATCH III to simulate Ocean wave characteristics (wavelengths, and wave heights (amplitudes)) over the western Indian Ocean in the Coast of East African countries was validated against satellite observation data. Simulated significant wave heights (SWH) and wavelengths over the South West Indian Ocean domain during the month of June 2014 was compared with satellite observation. Statistical measures of model performance that includes bias, Mean Error (ME), Root Mean Square Error (RMSE), Standard Deviation of error (SDE) and Correlation Coefficient (r) are used. It is found that in June 2014, when the WAVEWATCH III model was forced by wind data from the Global Forecasting System (GFS), simulated the wave heights over the Coast of East African countries with biases, Mean Error (ME), Root Mean Square Error (RMSE), Correlation Coefficient (r) and Standard Deviation of error (SDE) in the range of -0.25 to -0.39 m, 0.71 to 3.38 m, 0.84 to 1.84 m, 0.55 to 0.76 and 0.38 to 0.44 respectively. While, when the model was forced by wind data from the European Centre for Medium Range Weather Foresting (ECMWF) simulated wave height with biases, Mean Error (ME), Root Mean Square Error (RMSE), Correlation Coefficient (r) and Standard Deviation of error (SDE) in the range of -0.034 to 0.008 m, 0.0006 to 0.049 m, 0.026 to 0.22 m, 0.76 to 0.89 and 0.31 to 0.41 respectively. This implies that the WAVEWATCH III model performs better in simulating wave characteristics over the South West of Indian Ocean when forced by the boundary condition from ECMWF than from GFS.