Wave Climate Studies Research Articles

A high-resolution coupled circulation-wave model for regional dynamic downscaling of water levels and wind waves in the western North Atlantic ocean

Reanalysis datasets provide temporally/spatially continuous data for sea level and wind wave climate studies but often lack the resolution to resolve the complex coastal physical processes. To address this issue, a high-resolution coupled circulation-wave model (ADCIRC + SWAN) is applied to dynamically downscale storm tides (tide + surge + wave setup) and waves in the western North Atlantic Ocean. The model is forced at its sea surface boundary with hourly surface pressure and wind fields, and its open boundary with water levels and direction-frequency wave spectrum from the ERA5 reanalysis dataset. The sensitivity of the model is evaluated for different combinations of internal tide energy conversion, quadratic friction coefficients, and wave physics packages. For the best model setup, the results show an RMSE range of 0.114 m–0.295 m for storm tide, 0.176 m–0.298 m for non-tidal residual, 0.143 m–0.39 m for significant wave height, and 0.42 s–1.04 s for mean wave period. Incorporating the direction-frequency wave spectrum as an open boundary condition improves the model's accuracy, reducing the RMSE for significant wave heights by up to 7% and for the mean wave period by up to 30% over a one-month simulation period.

Statistical downscaling of coastal directional wave spectra using deep learning

The modelling of coastal Directional Wave Spectra (DWSs) often requires downscaling techniques integrating DWSs from open ocean boundaries. Dynamic downscaling methods reliant on numerical wave models are often computationally expensive. In coastal areas, wave dynamics are strongly influenced by the bathymetry and coastal morphology, implying that once the DWSs at the open ocean boundary are known, the DWSs at various locations along the coast are almost determined. This property can be utilized for statistical downscaling of coastal DWSs. This study presents a deep learning approach to compute coastal DWSs from open ocean DWSs. The performance of the proposed downscaling model was evaluated using both numerical wave model data and buoy data in the Southern California Bight. The results show that the deep learning approach can effectively and efficiently downscale coastal DWSs without relying on any predefined spectral shapes, thereby showing potential for coastal spectral wave climate studies.

Multivariate GRU and LSTM models for wave forecasting and hindcasting in the southern Caspian Sea

Reliable wave forecasts and hindcasts are pivotal elements for a wide range of marine activities, coastal engineering applications, renewable energy and wave climate studies. This study explores capability of recurrent neural networks for wave hindcasting and multi-step wave forecasting in the southern Caspian Sea, a region with a limited observational data. Gated recurrent unit (GRU) and long short-term memory (LSTM) models are fed by buoy records for wave forecasting up to 6 h ahead and ERA5 reanalysis data as the model inputs for wave hindcasting. Special efforts are devoted to improve the accuracy of the models by including some additional features (e.g., wave age and swell) in the input layer. Different error measures are employed to evaluate performance of the models at two stations, Amirabad and Anzali. Using multivariate GRU and LSTM models, suitable wave forecasts and hindcasts are achieved for the locations. Although the model performance deteriorates with extending the forecasting time horizon, the forecasts of significant wave height (Hs) up to 6 h ahead are still satisfactory. The best hindcasting model yields a high linear correlation between hindcasted and measured Hs with R2 = 0.75 for Anzali and R2 = 0.83 for Amirabad. Finally, wave age and significant height of total swell (Hs-swell) can be considered as tentative input variables to improve efficiency of the models.

Advancements on Optimization Algorithms Applied to Wave Energy Assessment: An Overview on Wave Climate and Energy Resource

The wave energy sector has not reached a sufficient level of maturity for commercial competitiveness, thus requiring further efforts towards optimizing existing technologies and making wave energy a viable alternative to bolster energy mixes. Usually, these efforts are supported by physical and numerical modelling of complex physical phenomena, which require extensive resources and time to obtain reliable, yet limited results. To complement these approaches, artificial-intelligence-based techniques (AI) are gaining increasing interest, given their computational speed and capability of searching large solution spaces and/or identifying key study patterns. Under this scope, this paper presents a comprehensive review on the use of computational systems and AI-based techniques to wave climate and energy resource studies. The paper reviews different optimization methods, analyses their application to extreme events and examines their use in wave propagation and forecasting, which are pivotal towards ensuring survivability and assessing the local wave operational conditions, respectively. The use of AI has shown promising results in improving the efficiency, accuracy and reliability of wave predictions and can enable a more thorough and automated sweep of alternative design solutions, within a more reasonable timeframe and at a lower computational cost. However, the particularities of each case study still limit generalizations, although some application patterns have been identified—such as the frequent use of neural networks.

Characterization and classification of wave storm events and wave climate on the Sea of Marmara

This study aims to conduct a wave climate study and provide a detailed analysis of wave storm events on the Sea of Marmara based on a 40-year (1979–2018) hindcast dataset. The accuracy of the used dataset was initially investigated in terms of both climatic and storm conditions using five-year (2013–2017) measurements of significant wave height (Hm0) at the Silivri buoy station. For the wave climate, the measurements’ 5-year mean and maximum Hm0 values were compared with SWAN hindcasts. To detect wave storm events from a time series, a critical value was determined by using the mean and standard deviation of the measurements. After that, the corresponding hindcasted storms with the measurements were detected. This analysis showed that the hindcasts are compatible with the measurements not only for climatic conditions but also for storm conditions. Monthly and annual variations of mean and maximum Hm0 were examined based on a 40-year long-term wave dataset and different return period Hm0 values based on annual maximums were determined at various locations. Wave storm events in the long-term analysis have been characterized and classified based on the storm energy density (Es) in five storm categories according to their energy. Temporal analysis of wave storm events shows that there has been an increase in the number and severity of storm events in recent years. The middle regions of the sea and the northeastern coasts of Marmara Island and Kapıdağ Peninsula are the most affected regions by extreme sea conditions.

Wave distribution and proposed seawall design around Tanjung Emas Port, Semarang

On May 23rd, 2022, an extreme coastal event occurred around the northern coast of Java. This event had caused the collapsed of several seawall segments around Tanjung Emas Port, Semarang. Combined with wave overtopping, it led to severe flooding and economic damages to the port operation. Responding to the event, this research aimed to characterize the hydrodynamic condition around the port and proposed a seawall design. The research was done through field observation, wave climate characterization, and numerical simulation. The field observation confirmed the extent of the damage and wave condition around the harbour. The wave climate study, based on a global wave reanalysis data, confirmed that the offshore wave height could reach 3 m during high wave season, with wave period of 4 – 6 seconds coming from the northwest. The tidal range is up to 1 m with highest astronomical tide (HAT) is at +0.65 MSL. The extreme value analysis gave a 3.7 meter of offshore design wave height for 100-year return period or up to 1.7 meters wave height nearshore. The proposed seawall crest elevation is at least at +3.40 meters above the mean sea level to avoid overtopping, which is 1.1 m higher than the existing seawall.

Wave power trends along the U.S. coastline: in situ measurements and model hindcast estimates

Observational data are successfully assessed to investigate wave power (wave energy flux per unit of wave-crest) trends within four coastal regions around the US, a parameter that is deemed vital to those responsible for coastal protection and community resilience. This study tests for shifting observational inter-annual wave power trends using a newly developed, unique, United States Army Corps of Engineers Quality Controlled Consistent Measurement Archive, and offers a viable methodology to remove documented observational time series data discontinuations. This study is one of the first to show spatially and temporally comparative observational and model wave power results, providing new information on the accuracy of model wave power estimates, while showcasing in situ wave power trends at 29 sites around the U.S. coastline. Overall, the majority of the eastern Pacific Ocean and Hawaii wave power trends are downward, with mixed slope wave power trends apparent within the Atlantic Ocean and the Gulf of Mexico. Observational and model results are similar with respect to timing, but not magnitude, of wave power peaks in long-term inter-annual trends, with the moored buoy data presenting smaller wave power ranges for two (eastern Pacific Ocean and Hawaii) of the four regions. Additionally, the detection of a noticeable variability in the wave power trend direction within each region suggests that site-specific wave power trends should not be generalised to represent a large region. This work demonstrates that observational data are essential in local and regional wave climate studies to accurately estimate wave power for coastal planners and engineers.

Influence of Trade Winds on the Detection of Trans-Hemispheric Swells near the Canary Islands

Trade winds are common in the Canary Islands archipelago and affect not only the weather of the islands but also the local wave climate. On the other hand, the arrival in the Canaries of swells from the Southern Hemisphere is little known, but usual. The records of these swells arriving in the Canary Islands have two clear peaks throughout the year, one in spring and the other in autumn. In this work, how the trade winds influence the detection of this type of swells is studied. It is estimated that only approximately half of this type of wave that reaches the Canary Islands could be adequately recorded in the buoy output data tables by the action of these winds. Therefore, their effects may be underestimated in local wave climate studies.

Distributed fiber-optic sensing transforms an abandoned well into a permanent geophysical monitoring array: A case study from Australian South West

Distributed temperature sensing (DTS) and distributed acoustic sensing (DAS) data recorded by a fiber-optic array installed during the decommissioning operations of the 1550 m Harvey 3 well in Western Australia reveal an abundance of valuable information about the course of the decommissioning process and the quality of the cement job. The DAS monitoring has detected vibrational disturbances during the cement's setting up, while DTS was used to assess setting up of the cement and curing times as well as uniformity of cementation from the distribution of temperature along the borehole. A weeklong trial acquisition of passive seismic data with the same array a year later shows an abundance of seismic events in a wide frequency range from below 1 mHz to above 200 Hz. The downhole DAS array provides traveltimes and amplitudes of these events, which include earthquakes, mine blasts, ocean microseisms, and local human activity. The amplitudes of waves from distant seismic events can be used to estimate and monitor physical properties of the media along the extent of the well. When used in combination with information from active vertical seismic profiling, these events can help obtain independent estimates of velocities and densities. Spectral analysis of low-frequency microseisms shows a strong correlation between passively recorded DAS and local weather observations. This shows that the ability to continuously record oceanic microseisms at low frequencies opens opportunities to employ such arrays for wave climate studies. In addition, the data contain peculiar in-hole reverberations likely caused by crossflow of groundwater behind the intermediate casing, which may indicate imperfections of the cement job. The results demonstrate that a downhole fiber-optic array installed in an abandoned well represents an opportunity to establish a permanent facility for continuous recording of passive and active geophysical data and for exploring various applications.

Validation of global wind wave hindcasts using ERA5, MERRA2, ERA-Interim and CFSRv2 reanalyzes

Four global wind wave hindcasts based on ERA5, MERRA2, ERA-I and CFSR reanalyses and spectral wave model WAVEWATCH III for the period from 1980 to 2017 have been validated against satellite altimetry and NDBC buoys for 1 year. Hindcast based on newly released ECMWF reanalysis ERA5 demonstrated the best agreement with both satellite altimetery (with normalized bias being up to 5%, and RMSE up to 0.5 m) and buoy measurements, including values of upper percentiles. In general, all hindcasts show good correspondence with the observational data and thus can be used in further wind wave climate studies.

Remote Cross-Calibration of Wave Buoys Based on Significant Wave Height Observations of Altimeters in the Northern Hemisphere

Consistency between national wave buoy networks is extremely important for wave climate studies and verification of global operational wave forecasting systems; however, it is insufficiently investigated. The validation of altimeter significant wave heights (SWHs) with the wave buoy networks of China, Europe and the National Data Buoy Center (NDBC) show significant divergence in assessments. This reveals a negative bias and larger root mean square error and scatter index from the Chinese buoy network than from the European and NDBC buoy networks. A remote cross-calibration method is presented using the collocations between altimeters and buoys to match the buoy observations from different networks. The Chinese buoys are found to yield a negative bias of −0.127 m compared to European/NDBC buoy networks. The cross-calibration equation is achieved by regression of the SWHs between the Chinese and European/NDBC buoy networks. The use of this remote cross-calibration significantly reduces the inconsistency between the Chinese and European/NDBC buoys in the validation of SWH from altimeter HY2B.

Intercomparison of Assimilated Coastal Wave Data in the Northwestern Pacific Area

The assimilated coastal wave data are useful for wave climate study, coastal engineering, and design for marine disaster protection. However, the assimilated coastal wave data are few. Here, wave analysis data produced by the JMA (Japan Meteorological Agency) and ERA5 wave data were compared with GPS (Global Positioning System) buoy-measured wave data. In addition, the accuracy of ERA5 wave data for various conditions was investigated. The accuracy of JMA analysis wave height was better than that of ERA5 wave height. The ERA5 wave height was underestimated as the wave height increased. The accuracy of the ERA5 wave height was significantly different in fetch-unlimited and fetch-limited conditions. The difference of the skill metrics between fetch-unlimited and fetch-limited conditions was due to the overestimation of the fetch in the ERA5 grid. This result also applied to the wave period.

Global ocean wave analysis based on the 10-year ENVISAT/ASAR wave mode data

The Synthetic Aperture Radar (SAR) Wave Mode (WM) is an imaging mode dedicated to ocean surface wave observation. Since the first used in the ERS-1/SAR in 1991, this imaging mode has been continuously acquired by ERS-2, ENVISAT/ASAR, Sentinel-1A/1B and the GaoFen-3, and has been providing ocean wave observation data for almost 30 years. As the Envisat/ASAR has the longest archived WM data, we used a parametric model named CWAVE_ENV to retrieve ocean wave parameters of significant wave height (SWH) and mean wave period (MWP) from all the ASAR WM data in its lifetime from December 2002 to April 2012. By comparing with the ECMWF and NDBC buoy data, we found good agreements between the ASAR retrieved results and the buoy measurements. The retrieved ASAR integral wave parameters are further calibrated using the buoy measurements based on the Reduced Majored Axes (RMA) regression method. By calibration, the bias of SWH and MWP are improved from −0.06 m to 0.00 m and from −0.19 s to 0.00 s. Cross-validation of the calibrated ASAR wave parameters with the JASON-1 Radar Altimeter SWH also shows a good consistency, with the correlation coefficient of 0.93, the bias and RMSE of 0.18 m and 0.53 m, respectively. The proposed 10-year global wave product could be a good complementary to the Radar Altimeter product and possible good dataset for wave climate study. A preliminary result of using these data for global wave analysis is shown as well in this paper.

A global ensemble of ocean wave climate projections from CMIP5-driven models

This dataset, produced through the Coordinated Ocean Wave Climate Project (COWCLIP) phase 2, represents the first coordinated multivariate ensemble of 21st Century global wind-wave climate projections available (henceforth COWCLIP2.0). COWCLIP2.0 comprises general and extreme statistics of significant wave height (HS), mean wave period (Tm), and mean wave direction (θm) computed over time-slices 1979–2004 and 2081–2100, at different frequency resolutions (monthly, seasonally and annually). The full ensemble comprising 155 global wave climate simulations is obtained from ten CMIP5-based state-of-the-art wave climate studies and provides data derived from alternative wind-wave downscaling methods, and different climate-model forcing and future emissions scenarios. The data has been produced, and processed, under a specific framework for consistency and quality, and follows CMIP5 Data Reference Syntax, Directory structures, and Metadata requirements. Technical comparison of model skill against 26 years of global satellite measurements of significant wave height has been undertaken at global and regional scales. This new dataset provides support for future broad scale coastal hazard and vulnerability assessments and climate adaptation studies in many offshore and coastal engineering applications.

Extreme value statistics of wind speed and wave height of the Marmara Sea based on combined radar altimeter data

Both reliable and long-term wind and wave data are necessary for the design of coastal and offshore structures. Due to lack of sufficient in-situ measurement data, modeling data have been used in the limited number of wind and wave climate studies of the Marmara Sea. Satellites equipped with instruments capable of observing marine surface wind and ocean waves like Radar Altimeter can be another source for the long term wind and wave climate of the Marmara Sea. In this study, for the first time, the altimeter wind speed and the significant wave height data from different satellite missions are attempted to use in the climate and extreme value analysis of the Marmara Sea. Altimeter wind speeds and significant wave heights are compared with the in-situ measurements and it is found that while the altimeter wind speed agrees with the measurement data, the significant wave height data should be calibrated. After the calibration of the altimeter data and the inter-calibrations of earlier satellite missions, 27 years of altimeter wind speed and wave height data are obtained to use in extreme value analysis. The wind speed and the significant wave height values corresponding to various return periods are determined as a result of extreme value statistics and those values are compared with the results of the measurements and previous studies. Consistent extreme values computed in the current study indicate that the combined radar altimeter data can be used in the wind and the wave climate calculations and the extreme value analysis of the Marmara Sea.

Validation of Sentinel-3A/3B Satellite Altimetry Wave Heights with Buoy and Jason-3 Data.

The validation of significant wave height (SWH) data measured by the Sentinel-3A/3B SAR Altimeter (SRAL) is essential for the application of the data in ocean wave monitoring, forecasting and wave climate studies. Sentinel-3A/3B SWH data are validated by comparisons with U. S. National Data Buoy Center (NDBC) buoys, using a spatial scale of 25 km and a temporal scale of 30 min, and with Jason-3 data at their crossovers, using a time difference of less than 30 min. The comparisons with NDBC buoy data show that the root-mean-square error (RMSE) of Sentinel-3A SWH is 0.30 m, and that of Sentinel-3B is no more than 0.31 m. The pseudo-Low-Resolution Mode (PLRM) SWH is slightly better than that of the Synthetic Aperture Radar (SAR) mode. The statistical analysis of Sentinel-3A/3B SWH in the bin of 0.5 m wave height shows that the accuracy of Sentinel-3A/3B SWH data decreases with increasing wave height. The analysis of the monthly biases and RMSEs of Sentinel-3A SWH shows that Sentinel-3A SWH are stable and have a slight upward trend with time. The comparisons with Jason-3 data show that SWH of Sentinel-3A and Jason-3 are consistent in the global ocean. Finally, the piecewise calibration functions are given for the calibration of Sentinel-3A/3B SWH. The results of the study show that Sentinel-3A/3B SWH data have high accuracy and remain stable.

Maximum wave heights from global model reanalysis

Very large waves populate world oceans and challenge seafarers and offshore structures, but their long-term and global assessment is uneasy because of the scarcity of observations and their narrow time-coverage. Modern model reanalysis datasets with high spatio-temporal extent and resolution represent a valuable tool for this scope. In this paper, we use for the first time reanalysis datasets to provide a long-term and global statistical assessment of the maximum wave parameters, namely crest, crest-to-trough and envelope heights. In particular, we rely on the ERA-Interim directional wave spectra that are used to estimate the parameters of the probability distributions of wave maxima. To represent the customary single-point observations we use time extreme statistical models, while to account for the three-dimensional geometry and short-crestedness of stormy ocean waves, the statistical models are extended to space-time. In order to evaluate the accuracy of the reanalysis-based wave maxima estimates we verify them against buoy and stereo-video wave observations gathered in the North Pacific Ocean. We then provide the global assessment of maximum crest, crest-to-trough and envelope heights during typical and extreme conditions, showing the regions attaining the largest values, which we show are located in the mid-latitude storm belts, in particular in the North Atlantic Ocean. With respect to previous wave climate studies that focused on the significant wave height only, in this study we quantify the maximum wave height extent, also highlighting the role of mean wave steepness and kurtosis (measures of nonlinearity) and spectral bandwidth (measure of irregularity). Beside this, we show that the contribution of the short-crestedness is significant and that taking it into account may be relevant for the safety of navigation, ship routing and marine structural design.

Optimising the Australian Wave Observation Network

The Australian national in situ wave data network currently consists of 35 platforms distributed around the Australian coastline. At present, all except for five are directional waverider buoys. The spatial density of the observation locations is variable – at a glance, density is higher on the east coast compared to the rest of the coastline. This variability has resulted in some areas of the coastline being well observed and well accounted for in models and wave climate studies and other areas not being observed at all. This work aims to identify potential gaps in the existing wave observing network in order to provide guidance for prospective future deployments. In addition, the technique used allows us to easily identify which are the key locations in the existing network. The method is based on considering the spatial coherence of the wave field determined from a multi-decadal hindcast wave data set. For each modelled data point, correlations between monthly statistics (means and 95th percentiles) of modelled variables (significant wave height, mean period and mean direction) at that location and corresponding modelled variables at each observation site are calculated. Areas of low correlation provide an indication of the key network gaps, i.e. areas where climatological variability of the wave fields is poorly captured by existing observations. Removing locations individually from the network and repeating the analysis can also provide an indication of which are the most important locations in the network (and conversely, which are the least important) to capture the regional climatological variability. Several key gaps are identified, suggesting that most value can be gained by placing additional buoys in these areas. However, it is noted that other factors such as accessibility, areas of maritime industry, and population distribution are also important in selecting sites for new buoy deployments.

Wind and wave climate in the vicinity of Bozcaada

One of the most important subjects of coastal engineering is determining the effect of waves on coastal areas. There are different types of marine structures constructed for different purposes such as berths and breakwaters for ports, seawalls for coastal protection, offshore breakwaters and groins.   The basic design tools in planning and designing coastal structures and determining the shoreline changing are wave parameters.Wind and wave climate studies were carried out in Bozcaada coastal region. The wind data used in the wave prediction is obtained from the General Directorate of State Meteorology Affairs. The data includes the hourly wind data for the 30-year period covering the period January 1980-July 2010 for Station number 17111. By evaluating the data, hourly wind distribution percentages, frequency distributions considering direction of wind speeds and direction weighted speed averages were obtained between the years 1980 and 2010. Wave parameters belonging to this region are determined by SMB method which is one of empirical wave estimation methodsWind and wave roses were obtained by using numerical model Mike 21 SW in the thesis comprehensiveness. Wind data required for this model is obtained from ECMWF (European Centre Medium-Range Weather Forecasts) ERA Interim data set. For the calibration of wind – wave models, data is used which was recorded for Bozcaada buoy stations under NATO TU- WAVES project at the coast of Turkey between 21 November 1994- 30 September 1995. When examining the results received from model according to ECMWF data, it has been observed that waves are usually more effective at Bozcaada  in south and  south east direction.

Wave steepness from satellite altimetry for wave dynamics and climate studies

Wave steepness from satellite altimetry for wave dynamics and climate studies