Search Results (63)

This study focuses on the significance of wind input source terms and their impact on wave generation in the wave model, WAVEWATCH III. Storm wave modeling capabilities were assessed with three different wind source term schemes ST4, ST6, and a new implementation ST6-IWD in a wave model to study Hurricane Ida (2021). A nested modeling approach was employed with high-resolution atmospheric wind forcing products obtained from the NOAA and the ECMWF. The model results were compared to field observations from NDBC buoys. One key finding indicates that the ST4 physical scheme is not necessarily suitable for modeling waves under extreme wind conditions. The ST6 and ST6-IWD schemes performed well for the hurricane scenario and the wave parameters obtained from these two sets of simulations were in good agreement with the observed values. The wind source term derived in the ST6 scheme holds good for wind speeds up to 50 m/s, whereas the drag method in ST6-IWD could remain stable up to ~113 m/s wind speeds. Therefore, this study recommends the ST6-IWD scheme, as it is suitable for more extreme hurricane wind conditions. It was also identified that the ST6-IWD method better estimates the peak wave periods and peak directions for Ida’s conditions. Full article

The Hong Kong Observatory has been using a parametric storm surge model to forecast the rise of sea level due to the passage of tropical cyclones. This model includes an offset parameter to account for the rise in sea level due to other meteorological factors. By adding the sea level rise forecast to the astronomical tide prediction using the harmonic analysis method, coastal sea level prediction can be produced for the sites with tidal observations, which supports the high water level forecast operation and alert service for risk assessment of sea flooding in Hong Kong. The Coupled Ocean-Atmosphere-Wave-Sediment Transport (COAWST) Modelling System, which comprises the Weather Research and Forecasting (WRF) Model and Regional Ocean Modelling System (ROMS), which in itself is coupled with wave model WaveWatch III and nearshore wave model SWAN, was tested with tropical cyclone cases where there was significant water level rise in Hong Kong. This case study includes two super typhoons, namely Hato in 2017 and Mangkhut in 2018, three cases of the combined effect of tropical cyclone and northeast monsoon, including Typhoon Kompasu in 2021, Typhoon Nesat and Severe Tropical Storm Nalgae in 2022, as well as two cases of monsoon-induced sea level anomalies in February 2022 and February 2023. This study aims to evaluate the ability of the WRF-ROMS-SWAN model to downscale the meteorological fields and the performance of the coupled models in capturing the maximum sea levels under the influence of significant weather events. The results suggested that both configurations could reproduce the sea level variations with a high coefficient of determination (R²) of around 0.9. However, the WRF-ROMS-SWAN model gave better results with a reduced RMSE in the surface wind and sea level anomaly predictions. Except for some cases where the atmospheric model has introduced errors during the downscaling of the ERA5 dataset, bias in the peak sea levels could be reduced by the WRF-ROMS-SWAN coupled model. The study result serves as one of the bases for the implementation of the three-way coupled atmosphere–ocean–wave modelling system for producing an integrated forecast of storm surge or sea level anomalies due to meteorological factors, as well as meteorological and oceanographic parameters as an upgrade to the two-way coupled Operational Marine Forecasting System in the Hong Kong Observatory. Full article

Based on the WAVEWATCH III wave model, China’s National Marine Environmental Forecasting Center has developed an operational global ocean wave forecasting system that covers the Arctic region. In this study, in situ buoy observations and satellite remote sensing data were used to perform a detailed evaluation of the system’s forecasting results for 2022, with a focus on China’s offshore and global ocean waters, so as to comprehensively understand the model’s forecasting performance. The study results showed the following: In China’s coastal waters, the model had a high forecasting accuracy for significant wave heights. The model tended to underestimate the significant wave heights in autumn and winter and overestimate them in spring and summer. In addition, the model slightly underestimated low (below 1 m) wave heights, while overestimating them in other ranges. In terms of spatial distribution, negative deviations and high scatter indexes were observed in the forecasting of significant wave heights in semi-enclosed sea areas such as the Bohai Sea, Yellow Sea, and Beibu Gulf, with the largest negative deviation occurring near Liaodong Bay of the Bohai Sea (−0.18 m). There was a slight positive deviation (0.01 m) in the East China Sea, while the South China Sea exhibited a more significant positive deviation (0.17 m). The model showed a trend of underestimation for the forecasting of the mean wave period in China’s coastal waters. In the global oceanic waters, the forecasting results of the model were found to have obvious positive deviations for most regions, with negative deviations mainly occurring on the east coast and in relatively closed basins. There were latitude differences in the forecasting deviations of the model: specifically, the most significant positive deviations occurred in the Southern Ocean, with smaller positive deviations toward the north, while a slight negative deviation was observed in the Arctic waters. Overall, the global wave model has high reliability and can meet the current operational forecasting needs. In the future, the accuracy and performance of ocean wave forecasting can be further improved by adjusting the parameterization scheme, replacing the wind fields with more accurate ones, adopting spherical multiple-cell grids, and data assimilation. Full article

In this study, a novel algorithm to retrieve the current speed along the range direction under extreme sea states is developed from C-band synthetic aperture radar imagery. To this aim, a Sentinel-1 (S-1) dual-polarized synthetic aperture radar (SAR) dataset consisting of 2300 images is collected during 200 tropical cyclones (TCs). The dataset is complemented with collocated wave simulations from the Wavewatch-III (WW3) model and reanalysis currents from the HYbrid Coordinate Ocean Model (HYCOM). The corresponding TC winds are officially released by IFRMER, while the Stokes drift following the wave propagation direction is estimated from the waves simulated by WW3. In this study, first the dependence of wind, Stokes drift, and range current on the Doppler centroid anomaly is investigated, and then the extreme gradient boosting (XGBoost) machine learning model is trained on 87% of the S-1 dataset for range current retrieval purposes. The rest of the dataset is used for testing the retrieval algorithm, showing a root mean square error (RMSE) and a correlation coefficient (r) of 0.11 m/s and 0.97, respectively, with the HYCOM outputs. A validation against measurements collected from two high-frequency (HF) phased-array radars is also performed, resulting in an RMSE and r of 0.12 m/s and 0.75, respectively. Those validation results are better than the 0.22 m/s RMSE and 0.28 r achieved by the empirical CDOP model. Hence, the experimental results confirm the soundness of the XGBoost, exhibiting a certain improvement over the empirical model. Full article

This study introduces a new machine learning-based algorithm for the retrieving significant wave height (SWH) using synthetic aperture radar (SAR) images. This algorithm is based on the azimuthal cut-off wavelength and was developed in quad-polarized stripmap (QPS) mode in coastal waters. The collected images are collocated with a wave simulation from the numeric model, called WAVEWATCH-III (WW3), and the current speed from the HYbrid Coordinate Ocean Model (HYCOM). The sea surface wind is retrieved from the image at the vertical–vertical polarization channel, using the geophysical model function (GMF) CSARMOD-GF. The results of the algorithm were validated against the measurements obtained from the Haiyang-2B (HY-2B) scatterometer, yielding a root mean squared error (RMSE) of 1.99 m/s with a 0.82 correlation (COR) and 0.27 scatter index of wind speed. It was found that the SWH depends on the wind speed and azimuthal cut-off wavelength. However, the current speed has less of an influence on azimuthal cut-off wavelength. Following this rationale, four widely known machine learning methods were employed that take the SAR-derived azimuthal cut-off wavelength, wind speed, and radar incidence angle as inputs and then output the SWH. The validation result shows that the SAR-derived SWH by eXtreme Gradient Boosting (XGBoost) against the HY-2B altimeter products has a 0.34 m RMSE with a 0.97 COR and a 0.07 bias, which is better than the results obtained using an existing algorithm (i.e., a 1.10 m RMSE with a 0.77 COR and a 0.44 bias) and the other three machine learning methods (i.e., a >0.58 m RMSE with a <0.95 COR), i.e., convolutional neural networks (CNNs), Support Vector Regression (SVR) and the ridge regression model (RR). As a result, XGBoost is a highly efficient approach for GF-3 wave retrieval at the regular sea state. Full article

This study estimates wave energy for the Moroccan Atlantic coast using SWAN, a third-generation wave model, covering a period of 30 years, from 1991 to 2020. The model is forced by the wind from the ERA-5 reanalysis dataset and uses boundary conditions generated by the WAVEWATCH III model. The significant wave height and period are used to obtain wave energy, which is analyzed at a regional scale. The mean wave energy density within the domain is assessed to be about 20 kW/m. Five specific locations are evaluated along the coast in order to determine the most energetic ones. The most energetic area of the Moroccan Atlantic coast is located at the center, between the cities of Agadir and Essaouira. Finally, the performance of six different wave energy converters is assessed through their power matrix for each of the five locations. Full article

Oceanic general circulation models (OGCMs) are important tools used to investigate mechanisms for ocean climate variability and predict the ocean change in the future. However, in most current ocean models, the impact of sea surface waves as one of the most significant dynamic processes in the upper ocean is absent. In this study, the Stokes drift and the vertical mixing induced by nonbreaking surface waves derived from the wave model (WAVEWATCH III) are incorporated into a Climate System Ocean Model, and their effects on an ocean climate simulation are analyzed. Numerical experiments show that both physical processes can improve the simulation of sea surface temperature (SST) and mixed layer depth (MLD) in the Southern Hemisphere. The introduction of Stokes drift effectively reduces the subsurface warm bias in the equatorial tropics, which is caused by the weakening of vertical mixing in the equatorial region. The nonbreaking surface wave mainly reduces the temperature bias in the Southern Ocean by enhancing mixing in the upper ocean. For the MLD, the Stokes drift mainly improves the simulation of the winter MLD, and the nonbreaking surface wave improves the summer MLD. For MLD south of 40° S in summer, the introduction of nonbreaking surface waves resulted in a reduction of 11.86 m in MLD bias and 7.8 m in root mean square errors (RMSEs), respectively. For winter subtropical MLD in the Southern Hemisphere, considering the Stokes drift, the MLD bias and RMSEs were reduced by 2.49 and 5.39 m, respectively. Adding these two physical processes simultaneously provides the best simulation performance for the structure of the upper layer. The introduction of sea surface waves effectively modulates the vertical mixing of the upper ocean and then improves the simulation of the MLD. Thus, sea surface waves are very important for ocean simulation, so we will further couple a sea waves model in the Chinese Academy of Sciences Earth System Model (CAS-ESM) as part of their default model component. Full article

In recent decades, acceleration of coastal erosion has been observed at many key sites of the Arctic region. Coastal dynamics of both erosional and accretional stretches at Kharasavey, Kara Sea, was studied using multi-temporal remote sensing data covering the period from 1964 to 2022. Cross-proxy analyses of the interplay between coastal dynamics and regional (wave and thermal action) and local (geomorphic and lithological features; technogenic impact) drivers were supported by cluster analysis and wind–wave modelling via the Popov–Sovershaev method and WaveWatch III. Ice-rich permafrost bluffs and accretional sandy beaches exhibited a tendency towards persistent erosion (−1.03 m/yr and −0.42 m/yr, respectively). Shoreline progradation occurred locally near Cape Burunniy (6% of the accretional stretch) and may be due to sediment flux reversals responding to sea-ice decline. Although the mean rates of erosion were decreasing at a decadal scale, cluster analysis captured a slight increase in the retreat for 71% of the erosional stretch, which is apparently related to the forcing of wind–wave and thermal energy. Erosional hotspots (up to −7.9 m/yr) occurred mainly in the alignment of Cape Kharasavey and were predominantly caused by direct human impact. The presented study highlights the non-linear interaction of the Arctic coastal change and environmental drivers that require further upscaling of the applied models and remote sensing data. Full article

The purpose of our work is to investigate the performance of fusion wind from multiple remote-sensed data in forcing numeric wave models, and the experiment is described herein. In this study, 0.125° gridded wind fields at 12 h intervals were fused by using swath products from an advanced scatterometer (ASCAT) (a Haiyang-2B (HY-2B) scatterometer) and a spaceborne polarimetric microwave radiometer (WindSAT) during the period November 2019 to October 2020. The daily average wind speeds were compared with observations from National Data Buoy Center (NDBC) buoys from the National Oceanic and Atmospheric Administration (NOAA), yielding a 1.66 m/s root mean squared error (RMSE) with a 0.81 correlation (COR). This suggests that fusion wind was reliable for our work. The fusion winds were used for hindcasting sea surface waves by using two third-generation numeric wave models, denoted as WAVEWATCH-III (WW3) and Simulation Wave Nearshore (SWAN). The WW3-simulated waves in the North Pacific Ocean and the SWAN-simulated waves in the Gulf of Mexico were validated against the measurements from the NDBC buoys and the European Centre for Medium-Range Weather Forecasts (ECMWF) reanalysis (ERA-5) for the period June−September 2020. The analysis of significant wave heights (SWHs) up to 9 m yielded a < 0.5 m RMSE with a > 0.8 COR for the WW3 and SWAN models. Therefore, it was believed that the accuracy of the simulation using the two numeric models was comparable with that forced by a numeric atmospheric model. An error analysis was systematically conducted by comparing the modeled WW3-simulated SWHs with the monthly average products from the HY-2B and a Jason-3 altimeter over global seas. The seasonal analysis showed that the differences in the SWHs (i.e., altimeter minus the WW3) were within ±1.5 m in March and June; however, the difference was quite significant in December. It was concluded that remote-sensed fusion wind can serve as a driving force for hindcasting waves using numeric wave models. Full article

The HISEA-1 synthetic aperture radar (SAR) minisatellite has been orbiting for over two years since its launch in 2020, acquiring numerous high-resolution images independent of weather and daylight. A typical and important application is the observation of ocean waves, essential ocean dynamical phenomena. Here, we proposed a new semi-automatic empirical method to retrieve ocean wave parameters from HISEA-1 images. We first applied some automated processing methods to remove non-wave information and artifacts, which largely improves the efficiency and robustness. Then, we developed an empirical model to retrieve significant wave height (SWH) by considering the dependence of SWH on azimuth cut-off, wind speed, and information extracted from the cross-spectrum. Comparisons with the Wavewatch III (WW3) data show that the performance of the proposed model significantly improved compared to the previous semi-empirical model; the root mean square error, correlation, and scattering index are 0.45 m (0.63 m), 0.87 (0.75), and 18% (26%), respectively. Our results are also consistent well with those from the altimeter measurements. Further case studies show that this new ocean wave model is reliable even under typhoon conditions. This work first provides accurate ocean-wave products from HISEA-1 SAR data and demonstrates its ability to perform high-resolution observation of coasts and oceans. Full article

Wind and wave conditions are limiting factors for economic activity, and it is very important to study the long-term variability of storm activity. The main motivation of this research is to assess the impact of wind variability on the storm activity in the Caspian Sea over the past 42 years. The paper presents the analysis of a number of storms based on the results of wave model WAVEWATCH III and the Peak Over Threshold method. The mean, maximum, and 95th percentile significant wave heights were analyzed by season. The highest waves were in the Middle Caspian Sea in winter. Detailed interannual and seasonal analyses of the number and duration of storm waves were performed for the whole Caspian Sea and its separate regions. Positive significant trends were found in the whole sea. Significant positive trends in the number and duration of storms were found for the North and Middle Caspian. In the South Caspian, the trends were negative and not significant. High correlations were found between the number of storms and events with wind speed > 10–14 m/s and 95th percentile wind speed. Positive trends in the number of storms in the Middle Caspian were caused by positive trends in extreme wind situations. Full article

Wave conditions around islands in the South China Sea (SCS) are of significant interest due to their importance for marine operations and coastal engineering. Understanding and accurately predicting wave characteristics in this region are crucial. In this study, the third-generation wave model WAVEWATCH III is employed to examine wave conditions around islands in the SCS. According to the water depth and significant wave height, the sea state around the island was classified into two categories: typhoon sea state and moderate sea state. Several popular wind input–dissipation source terms (ST2, ST4 and ST6) are used to assess the typhoon sea state and the moderate sea state separately. The results are validated by field wave data. ST4 and ST6 show good performance in significant wave height for moderate sea states, while ST2 is good at the mean wave period. For the typhoon sea state, ST2 gives the best results in significant wave height with larger correlation coefficients and a smaller RMSE. The above results provide valuable insights into the effects of different source terms on the accuracy of wave simulations for different sea states. The spatial distribution of the significant wave heights is also demonstrated with ST2, which may be useful for assessing the wave conditions of marine structures from the large scale of the SCS to the island scale of the Yongle Atoll. Full article

The main parameters of wind waves in the World Ocean are connected with global climate change. Renewable energy technologies, intensive shipping, fishery, marine infrastructure, and many different human marine activities in the coastal zone and open sea need knowledge about the wind-wave climate. The main motivation of this research is to share various wind wave parameters with high spatial resolution in the coastal zone via a modern cartographic web atlas. The developed atlas contains information on 13 Russian Seas, including the Azov, Black, Baltic, Caspian, White, Barents, Kara, Laptev, East Siberian, Chukchi, Bering Seas, the Sea of Okhotsk, and the Sea of Japan/East Sea. The analysis of wave climate was based on the results of wave modeling by WAVEWATCH III with input NCEP/CFSR wind and ice data. The web atlas was organized using the classic three-tier architecture, which includes a data storage subsystem (database server), a data analysis and publishing subsystem (GIS server), and a web application subsystem that provides a user interface for interacting with data and map services (webserver). The web atlas provides access to the following parameters: mean and maximum significant wave height, wave length and period, wave energy flux, wind speed, and wind power. The developed atlas allows changing the map scale (zoom) for detailed analysis of wave parameters in the coastal zones where the wave model spatial resolution is 300–1000 m. Full article

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. Full article

A regional wave forecasting system in East Asia, including the Korean Peninsula, was built based on WAVEWATCH III using offshore wind forecast data from the Global Data Assimilation Prediction System. The numerical simulations were performed on the sensitivity of the interaction between input wind and wave development. The forecasts for each condition were compared and verified with the observational data of marine meteorological buoys from 1 August to 30 September 2020. The sensitivity conditions were configured to have a specific range of variables related to the directional distribution of input winds (SINA0) and variables indicating the development of input wind–wave (CDFAC) in the ST6. The results were presented by calculating the mean error and root mean square error for all observation points. Overall, as the CDFAC increased, the mean error tended to decrease according to the forecast time and the root mean square error increased. Although the effect of SINA0 at the same CDFAC was insignificant, when SINA0 increased in sections where the significant wave height decreased rapidly, the significant wave height tended to decrease. In addition, the main variables that affect the physical process of wind–wave interaction should be considered to improve wave model forecasting performance and accuracy. Full article