Mean Wave Period Research Articles

Performance Characteristics of Newly Developed Real-Time Wave Measurement Buoy Using the Variometric Approach

Accurate measurement of ocean wave parameters is critical for applications including ocean modeling, coastal engineering, and disaster management. This article introduces a novel global navigation satellite system (GNSS) drifting buoy for surface wave measurements that addresses the challenges of performing real-time, high-precision measurements and realizing cost-effective large-scale deployment. Unlike traditional approaches, this buoy uses the kinematic extension of the variometric approach for displacement analysis stand-alone engine (Kin-VADASE) velocity measurement method, thus eliminating the need for additional high-precision measurement units and an expensive complement of satellite orbital products. Through testing in the South China Sea and Laoshan Bay, the results showed good consistency in significant wave height and main wave direction between the novel buoy and a Datawell DWR-G4, even under mild wind and wave conditions. However, wave mean period disparities were observed partially because of sampling frequency differences. To validate this idea, we used Joint North Sea Wave Project (Jonswap) spectral waves as input signals, the bias characteristics of the mean periods of the spectral calculations were compared under conditions of identical input signals and gradient-distributed wind speeds. Results showed an average difference of 0.28 s between the sampling frequencies of 1.28 Hz and 5 Hz. The consequence that high-frequency signals have considerable effects on the mean wave period calculations indicates the necessity of the buoy’s high-frequency operation mode. This GNSS drifting buoy offers a cost-effective, globally deployable solution for ocean wave measurement. Its potential for large-scale networked ocean wave observation makes it a valuable oceanic research and monitoring instrument.

Dynamics of Sandy Shorelines and Their Response to Wave Climate Change in the East of Hainan Island, China

Beach erosion and shoreline dynamics are strongly affected by alterations in nearshore wave intensity and energy, especially in the context of global climate change. However, existing works do not thoroughly study the evolution of the sandy coasts of eastern Hainan Island, China, nor their responses to wave climate change driven by climate variability. This study focuses on the open sandy coast and assesses shoreline evolutionary dynamics in response to wave climate variability over a 30-year period from 1994 to 2023, using an open-source software toolkit that semi-automatically identify the shorelines (CoastSat v2.4) and reanalysis wave datasets (ERA5). The shorelines of the study area were extracted from CoastSat, and then tidal correction and outlier correction were performed for clearer shorelines. Combining the shoreline changes and wave conditions derived from ERA5, the dynamics of the shorelines and their response to wave climate change were further studied. The findings reveal that the average long-term shoreline change rate along the eastern coast of Hainan Island is 0.03 m/year, with 44.8% of transects experiencing erosion and 55.2% showing long-term accretion. And distinct evolutionary patterns emerge across different sections. Interannual variability is marked by alternating erosion and siltation cycles, while most sections of the coast experiences clear seasonal fluctuations, with accretion typically occurring during summer and erosion occurring in winter. El Niño–Southern Oscillation (ENSO) cycles drive changes in parameters including significant wave height, mean wave period, wave energy flux, and mean wave direction, leading to long-term changes in wave climate. The multi-scale behavior of the sandy shoreline responds distinctly to the ongoing changes in wave climate triggered by ENSO viability, with El Niño events typically resulting in accretion and La Niña periods causing erosion. Notably, mean wave direction is the metric most closely linked to changes in the shoreline among all the others. In conclusion, the interplay of escalating anthropogenic activities, natural processes, and climate change contributes to the long-term evolution of sandy shorelines. We believe this study can offer a scientific reference for erosion prevention and management strategies of sandy beaches, based on the analysis presented above.

Convergence and divergence of storm waves induced by multi-scale currents: Observations and coupled wave-current modeling

Current effects on waves (CEW) are among the most intricate physical processes in wave evolution. In this study, we used a coupled wave-tide-circulation model for the Northwest Atlantic to investigate current effects on storm waves during Hurricane Igor in 2010. Validated with extensive buoy and altimeter data, the inclusion of CEW in the model significantly improves the accuracy in simulating significant wave heights (Hs) by up to 21.3% for a wave buoy. Storm waves experience significant temporal and spatial modulation by multi-scale currents. Storm-driven currents have the most pronounced impact to the right of the storm track, which typically align with wave propagation and reduce Hs by up to 12.1%. The subsequent near-inertial oscillations induce temporal fluctuations of wave convergence and divergence at near-inertial frequencies, which also occurs in regions with strong tidal currents but at tidal frequencies. Furthermore, storm waves are modulated by the Gulf Stream, Labrador Current and associated mesoscale eddies. Overall, these multi-scales yield strong effects on storm waves (Hs > 3.0 m), significantly modulating Hs (−25.2%–+55.4%) and mean wave periods (−14.9%–+15.7%). The mean wave energy power shows more significant modulation by multi-scale currents, reflecting the combined effects of changing wave states and current-induced transport of wave energy. CEW are governed by the interactive dynamic and kinematic effects. The relative wind effect is the primary mechanism for lower storm waves by reducing energy input to waves and influences downstream wave states. Among kinematic effects, current-induced wave refraction typically plays a dominant role in redistributing wave energy. This study systematically quantified the modulation of storm waves by multi-scale currents and revealed the underlying mechanisms, providing a comprehensive understanding of extreme wave states under coupled ocean dynamics.

CanESM5-derived ocean wave projections — Considerations for coarse resolution climate models

This study presents the first set of CanESM5-driven wave projections for two emission scenarios (SSP5-8.5 and SSP2-4.5) and two time periods for mid- and end-century. While coarse resolution climate models, like CanESM5, might be less attractive for development of ocean wave projections, their results are needed to explore the full range of inter-model uncertainty in CMIP6 projections. Considering the coarse resolution limitation, wave simulations were obtained with a proposed computationally efficient 2-step bias-correction approach that consists of (i) calibrating the wind-to-wave energy transfer in the ocean wave model to reduce the underestimation of extremes resulting from coarse resolution, and (ii) bias-correcting the surface winds with a multivariate bias-correction to reduce remaining systematic biases. Results showed overall good performance in comparison with state of the art reanalysis and satellite data. Resulting projections provide increased understanding of future changes in wave conditions, confirming previously reported global-scale changes, such as higher waves in the eastern tropical Pacific and lower waves in the North Atlantic. They also provide more detailed information for areas affected by sea ice conditions in comparison to the latest CMIP5-based wave ensembles, which is critical for the Arctic region, a hotspot for ocean wave changes. Moreover, while the largest changes are typically seen by the end-century under SSP5-8.5, this study reveals that for some variables and areas, such as the mean wave period, larger changes occur for lower warming levels as a result of competing driving factors. Finally, the presented projections can contribute to ongoing efforts to generate a large multi-model ensemble of wave projections based on CMIP6.

Deep learning for retrieving omni-directional ocean wave spectra from spaceborne synthetic aperture radar

Synthetic Aperture Radar (SAR) is a unique remote sensing instrument imaging ocean surface waves in two dimensions with high spatial resolution regardless of sunlight and weather conditions. However, due to the nonlinear imaging process, the ocean wave spectra cannot be retrieved directly from SAR data. The emergence of deep learning (DL) techniques provides a new paradigm for addressing this challenge. In this paper, a deep-learning-based model, called Wave-Spec-CNN, is proposed for retrieving omni-directional ocean wave spectra from SAR data. This model is constructed using approximately 21,000 collocations of Sentinel-1 Interferometric Wide swath mode images matched with global in-situ buoy data. The model adapts the convolution neural network (CNN) to accommodate the multi-valued nature of omni-directional ocean wave spectra, enhances performance by integrating a calibration branch and further incorporates physical characteristics into the training process. The results demonstrate consistency with buoy measurements for significant wave height (SWH) in the range of 0.5 m to 6 m, yielding a root-mean-square error (RMSE) of 0.51 m on the validation dataset, comparable to traditional physical-based methods. In terms of mean wave period (MWP) and peak frequency (PF), the achieved RMSEs are of 1.24 s and 0.03 Hz, respectively. The retrieved omni-directional ocean wave spectra also allow to separate swell and windsea components for respective comparisons with those derived by in-situ buoy data. The RMSEs of respective SWH comparisons are of 0.46 m and 0.42 m. This research represents an initial endeavor into utilizing DL for the long-standing challenge of SAR inversion for ocean wave spectra, as well as providing valuable insights for employing DL in multi-parameter inversion tasks in remote sensing.

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.

Predicting significant wave height in the South China Sea using the SAC-ConvLSTM model

IntroductionThe precise forecasting of Significant wave height(SWH) is vital to ensure the safety and efficiency of aquatic activities such as ocean engineering, shipping, and fishing.MethodsThis paper proposes a deep learning model named SAC-ConvLSTM to perform 24-hour prediction with the SWH in the South China Sea. The long-term prediction capability of the model is enhanced by using the attention mechanism and context vectors. The prediction ability of the model is evaluated by mean absolute error (MAE), root mean square error (RMSE), mean square error (MSE), and Pearson correlation coefficient (PCC).ResultsThe experimental results show that the optimal input sequence length for the model is 12. Starting from 12 hours, the SAC-ConvLSTM model consistently outperforms other models in predictive performance. For the 24-hour prediction, this model achieves RMSE, MAE, and PCC values of 0.2117 m, 0.1083 m, and 0.9630, respectively. In addition, the introduction of wind can improve the accuracy of wave prediction. The SAC-ConvLSTM model also has good prediction performance compared to the ConvLSTM model during extreme weather, especially in coastal areas.DiscussionThis paper presents a 24-hour prediction of SWH in the South China Sea. Through comparative validation, the SAC-ConvLSTM model outperforms other models. The inclusion of wind data enhances the model's predictive capability. This model also performs well under extreme weather conditions. In physical oceanography, variables related to SWH include not only wind but also other factors such as mean wave period and sea surface air pressure. In the future, additional variables can be incorporated to further improve the model's predictive performance.

Projections of the Adriatic wave conditions under climate changes

Assessing the impact of climate change on wave conditions, including average and extreme waves, is vital for numerous marine-related activities, industries, coastal vulnerability, and marine habitats. Previous research, primarily on a large scale, has investigated this topic, but its relevance for marginal basins like the Adriatic Sea is limited due to the low resolution of the wave models used and atmospheric forcing. To contribute to filling in the gap, here we implemented a high-resolution model (about 2 km) for the period 1992–2050. The future wave climate is simulated for the RCP8.5 emission scenario. This model, developed within the AdriaClim project, comprises, among others, a high-resolution atmospheric downscaling, a circulation Limited Area Model and a spectral wave model. A comparison of our simulation's results with Copernicus Marine Service wave reanalysis on the historical baseline, confirms its accuracy in reproducing both average wave parameters and 95th percentile values, as well as the seasonal cycle, showing the AdriaClim model's suitability as a source to predict future wave climates in the Adriatic Sea. The projected changes suggest a slight increase in average significant wave height and mean wave period, and a more significant decrease at the 95th percentile, with a relevant variability by location and season, partially aligning with previous studies. This study highlights the potential effect of local climate change in coastal areas and the importance of developing long-term simulation with a downscaled modeling system for regional areas.

Multi-station collaborative wave height prediction based on multi-feature identification and interpretable analysis

This study proposes a novel deep learning model, the graph convolutional gated recurrent unit (GC-GRU), to address the critical challenge of accurate forecasting of ocean wave heights due to the complex nonlinear spatiotemporal variability of wave dynamics. The proposed model, which integrates the strengths of graph convolutional networks (GCNs) for spatial feature extraction and gated recurrent units (GRUs) for temporal feature extraction, allows for effective capture of complex spatiotemporal patterns in wave height data and is evaluated on a dataset of 666 observation stations in the Gulf of Mexico, forecasting wave heights up to 36 h in advance. Comparative experiments with traditional CNN and GRU models demonstrate the superior predictive performance of the GC-GRU approach. Additionally, we introduce the shapley additive explanation (SHAP) values to provide physical insights into the key physical variables and historical patterns driving the model's predictions. The results show that wind speed and mean wave period are the most influential factors related to wave height variations. It is expected that this work presents a significant advancement in wave height forecasting by introducing the innovative GC-GRU architecture and leveraging SHAP analysis to interpret the model's inner workings. The findings are expected to have important implications for enhancing coastal and maritime operations as well as informing our understanding of complex ocean wave dynamics.

The application of a non-hydrostatic RANS model for simulating irregular wave breaking on a barred and sloping beach

In the current study, a non-hydrostatic two-dimensional vertical (2DV) numerical model with a shock-capturing technique is developed to simulate irregular wave breaking on a barred beach. The complete form of the 2DV Reynolds-averaged Navier-Stokes (RANS) equations is discretized using the finite volume approach. In the spatial discretization phase, a flux limiter function is employed for the velocity advection terms to prevent non-physical oscillations in regions with steep gradients. A novel method has been introduced during the discretization stage, leading to a significant reduction in computational expenses. For temporal discretization, the Leapfrog method, known for its second-order accuracy in time, is employed to address wave-damping issues. Model validation involves a comparison between simulation results and experimental data pertaining to irregular wave breaking, affirming the satisfactory performance of the model. To evaluate the accuracy of the model, the Root Mean Square Error (RMSE) was calculated. The assessment showed that the model is capable of estimating the significant wave height reported in the experiments used with an RMSE between 0.002 and 0.089, and the mean wave periods with an RMSE between 0.061 and 0.252.

DELWAVE 1.0: deep learning surrogate model of surface wave climate in the Adriatic Basin

Abstract. We propose a new point-prediction model, the DEep Learning WAVe Emulating model (DELWAVE), which successfully emulates the behaviour of a numerical surface ocean wave model (Simulating WAves Nearshore, SWAN) at a sparse set of locations, thus enabling numerically cheap large-ensemble prediction over synoptic to climate timescales. DELWAVE was trained on COSMO-CLM (Climate Limited-area Model) and SWAN input data during the period of 1971–1998, tested during 1998–2000, and cross-evaluated over the far-future climate time window of 2071–2100. It is constructed from a convolutional atmospheric encoder block, followed by a temporal collapse block and, finally, a regression block. DELWAVE reproduces SWAN model significant wave heights with a mean absolute error (MAE) of between 5 and 10 cm, mean wave directions with a MAE of 10–25°, and a mean wave period with a MAE of 0.2 s. DELWAVE is able to accurately emulate multi-modal mean wave direction distributions related to dominant wind regimes in the basin. We use wave power analysis from linearised wave theory to explain prediction errors in the long-period limit during southeasterly conditions. We present a storm analysis of DELWAVE, employing threshold-based metrics of precision and recall to show that DELWAVE reaches a very high score (both metrics over 95 %) of storm detection. SWAN and DELWAVE time series are compared to each other in the end-of-century scenario (2071–2100) and compared to the control conditions in the 1971–2000 period. Good agreement between DELWAVE and SWAN is found when considering climatological statistics, with a small (≤ 5 %), though systematic, underestimate of 99th-percentile values. Compared to control climatology over all wind directions, the mismatch between DELWAVE and SWAN is generally small compared to the difference between scenario and control conditions, suggesting that the noise introduced by surrogate modelling is substantially weaker than the climate change signal.

Wave retrieval for Sentinel-1 synthetic aperture radar under complex sea state

ABSTRACT In this paper, sea surface waves during complex sea state (i.e. tropical cyclones (TCs) and Kuroshio current) are inverted from dual-polarized (vertical – vertical (VV) and vertical – horizontal (VH)) synthetic aperture radar (SAR) images using an existing algorithm, namely, the parameterized first-guess spectrum method (PFSM). In particular, the wind term is included in two modulation transfer functions (MTFs), i.e. tilt modulation and velocity bunching. More than 3000 Sentinel (S-1) images acquired in interferometric wide (IW) mode and extra wide (EW) mode are collected, and the operational CyclObs wind product provided by the French Research Institute for Exploitation of the Oceans (IFREMER) is collected corresponding to the TC images. Note that these images are located within 500 km away from TC eyes. The wave spectra during the TCs are then inverted using the PFSM algorithm and the SAR-derived wind product. Simultaneously, the 0.05° gridded wave spectra collocated with these images are simulated using a third-generation numeric wave model, called WAVEWATCH-III (WW3), in which the reconstructed wind based on the European Centre for Medium-Range Weather Forecasts (ECMWF), the Copernicus Marine Environment Monitoring Service (CMEMS) current, and the CMEMS sea level are treated as the forcing field. Comparison of the SAR wave retrievals and simulations reveals that the root mean square errors (RMSEs) of the significant wave height (SWH) and mean wave period (MWP) are 0.55 m and 0.43 s, their correlation coefficients (COR) are 0.94 and 0.92, and their scatter index (SI) values are 0.17 and 0.07. These results demonstrate the better performance of the PFSM algorithm using theoretical MTFs, i.e. an RMSE of 0.68 m and COR of 0.90 for the SWH and an RMSE of 0.60 s and a COR of 0.87 for the MWP. Although the accuracy of the wave retrieval from the SAR image under complex sea state is improved when implementing the tilt and velocity bunching MTFs with the strong wind term, SWH retrievals by algorithm PFSM suffer saturation problem at SWH > 8 m.

Continuous Wave Measurements Collected in Intermediate Depth throughout the North Sea Storm Season during the RealDune/REFLEX Experiments

High-resolution wave measurements at intermediate water depth are required to improve coastal impact modeling. Specifically, such data sets are desired to calibrate and validate models, and broaden the insight on the boundary conditions that force models. Here, we present a wave data set collected in the North Sea at three stations in intermediate water depth (6–14 m) during the 2021/2022 storm season as part of the RealDune/REFLEX experiments. Continuous measurements of synchronized surface elevation, velocity and pressure were recorded at 2–4 Hz by Acoustic Doppler Profilers and an Acoustic Doppler Velocimeter for a 5-month duration. Time series were quality-controlled, directional-frequency energy spectra were calculated and common bulk parameters were derived. Measured wave conditions vary from calm to energetic with 0.1–5.0 m sea-swell wave height, 5–16 s mean wave period and W-NNW direction. Nine storms, i.e., wave height beyond 2.5 m for at least six hours, were recorded including the triple storms Dudley, Eunice and Franklin. This unique data set can be used to investigate wave transformation, wave nonlinearity and wave directionality for higher and lower frequencies (e.g., sea-swell and infragravity waves) to compare with theoretical and empirical descriptions. Furthermore, the data can serve to force, calibrate and validate models during storm conditions.

A study on wave climate variability along the nearshore regions of Bohai Sea based on long term observation data

The Bohai Sea off the coast of northern China plays important economic and ecological roles. No studies have been conducted on wave characteristics in the Bohai Sea nearshore area using ocean station data in the past. Observational data (1998–2022) from eight ocean-stations in the Bohai Sea were used in the present study. Overall, wave height (Hs) was small; winter seasons had higher Hs than summer seasons, and the maximum Hs occurred in the Bohai Strait. Joint distribution analysis indicated that waves with Hs ranging from 0.2 to 0.4 m, and mean wave periods (Tm) ranging from 3 to 3.5 s, were most common. Hs in the Bohai Sea nearshore area showed a weak response to climate variability and was moderately correlated with the Ocean Niño Index (ONI) and the North Pacific Index (NPI) in winter, and with the Arctic Oscillation (AO) in summer. Under extreme conditions, Hs was best described by a Generalized Pareto distribution, Tm was best described by a GEV distribution, and wave directions mainly followed a t location-scale distribution. The maximum wave height (Hmax)/Hs was 1.51, and the ratio varied with location. Machine learning methods were used to calculate Hmax more precisely, and they yielded better results than statistical methods.

Future offshore wind energy evolution in the Bay of Biscay

A relevant point near the Biscay Marine Energy Platform (BiMEP) has been chosen and data from the ERA5 reanalysis and CMIP6 datasets in future SSP5-85 scenario (wind and wave data) have been used to analyse the energy potential and its trends from 2015 to 2100 for wind energy. A 15 MW wind turbine has been chosen in line with current technological state-of-the-art and needs, and its energy production has been computed considering the corresponding power curve, and the wind speed at its hub height based on the variation of sea roughness due to the sea state (wave height and period). The results, although most of them have not been relevant, have shown a slightly downward trend mostly on winter in all parameters analysed: mean wave period (Tm), significant wave height (Hs), corresponding sea roughness (z0), and wind speed at hub height (Ux). The 15MW wind turbine simulation has therefore shown a small decrease in energy production.

QuadWave1D: An optimized quadratic formulation for spectral prediction of coastal waves

Spectral information of coastal waves and the associated statistical parameters (e.g., the significant wave height and mean wave period) over large spatial scales is essential for many applications (e.g., coastal safety assessments, coastal management and developments, etc.). This demand explains the necessity for accurate yet effective models. A well-known efficient modelling approach is the quadratic approach (often referred to as frequency-domain models, weakly nonlinear mild-slope models, amplitude models, etc.). The efficiency of this approach is achieved through modelling reduction of the original governing equations (e.g., Euler equations). Most significantly, wave nonlinearity is described solely by a single quadratic mode-coupling term. Therefore, doubts arise with regard to the predictive capabilities of the quadratic approach to reliably describe the nonlinear development of waves in the coastal environment where nonlinearity is typically significant. This study attempts to push the limit of the prediction capabilities of nonlinear coastal waves based on the quadratic approach. To this end, an optimization process is proposed, striving to extract the quadratic formulation which describes most adequately nonlinear wave developments over water depths and bathymetrical structures which characterize the coastal environment. The outcome is the model QuadWave1D: a fully dispersive quadratic model for coastal wave prediction in one-dimension. Based on a wide set of examples (including monochromatic, bichromatic and irregular wave conditions) and comparing to other representative quadratic formulations, it is found that QuadWave1D presents superior predictive capabilities of both the sea-swell components and the infragravity field.

Improving the WAVEWATCH-III wave model results using data assimilation in the Persian Gulf

In this study, data assimilation with bulk and partitioned spectral parameters were integrated into the WAVEWATCH-III (WW3) wave model. The dependency of future forecasts on the current state in numerical models leads to an error flow in the results. Data assimilation schemes, such as the optimum interpolation, compensate for the problem by using available observations to minimize the error in the current state of the model. Different wind input and wave dissipation packages of WW3 were assessed for the Persian Gulf. The data assimilation scheme was integrated with the most successful formulation, ST3BJA. The enhanced WW3 model was assessed for two time periods in the Persian Gulf in which the wave data from three in-situ ADCPs were available. In addition, the satellite-derived wave height data within the computational domain were also used for model assessment. Assimilating the significant wave height data from in-situ measurements improved the model results in all cases and reduced the underestimation of significant wave height and improved the wave period statistics, significantly. The inclusion of mean wave period or use of partitioning the spectrum in the assimilation process resulted in spurious oscillations in the time series of peak wave period and significant wave height. Since the swells are not energetic in the Persian Gulf, the results from assimilation with partitioning spectrum scheme is not as successful as from assimilation scheme with the bulk parameters in reproducing accurate total wave height.

Machine Learning‐Based Wave Model With High Spatial Resolution in Chesapeake Bay

AbstractA high‐resolution wave model is crucial for accurate modeling of sediment and organic material transports, but its computational costs hinder direct coupling to an ecosystem model. We developed a machine learning model using long short‐term memory to simulate large‐scale, high‐resolution waves. Trained with numerical wave model (NWM) outputs and wind data from nine locations, our model successfully replicates NWM results for daily mean significant wave height and period in Chesapeake Bay with identical spatial resolution. Compared to the NWM, the data‐driven model has root‐mean‐square errors below 6 cm for daily mean significant wave height and 1 s for the wave period in the bay. It demonstrates excellent model skills and can accurately forecast daily mean significant wave height and period at NOAA wave stations comparable to NWMs. Using minimal wind data and having a short runtime, our data‐driven model shows promise as an alternative for wave forecasting and coupling with sediment and ecological models.

Comparison of wave spectrum assimilation and significant wave height assimilation based on Chinese-French oceanography satellite observations

Assimilating observation data into wave models can significantly improve the numerical simulation accuracy of ocean waves. Traditionally, assimilation efforts have predominantly focused on significant wave height (SWH). However, the launch of the China-France Oceanography SATellite (CFOSAT)has facilitated the acquisition to obtain global-scale wave spectrum data, introducing a transformative dimension to assimilation methodologies. This novel wave spectrum data holds substantial potential for refining global wave data assimilation results. In our study, a series of numerical experiments were conducted employing CFOSAT data to systematically contrast the impact of wave spectrum assimilation with that of SWH assimilation on global wave simulations. The assimilating impacts of SWH assimilation and spectrum assimilation were validated against wave data retrieved from the National Data Buoy Center (NDBC) buoys and Jason-3 altimeter. The validation included SWH, mean wave period (MWP), dominant wave period (DWP), the direction from which the waves at the DWP are coming (DWD), and the wave spectrum. When comparing with the buoy measurements, spectrum assimilation demonstrated superior effectiveness in improving mean wave period (MWP) compared to SWH assimilation. The assimilation index for spectrum assimilation reached 12.21%, escalating to 15.78% when MWP exceeded or equaled 8 s. In contrast, for SWH, DWP and DWD, SWH assimilation performed better. Validation against Jason-3 altimeter data revealed that spectrum assimilation surpassed SWH assimilation in terms of overall improvement. Notably, SWH assimilation exhibited a more favorable impact when SWH values were <2.5 m. However, for SWH values greater than or equal to 2.5 m, spectrum assimilation outperformed SWH assimilation, yielding assimilation indices of 8.10% and 6.18% compared to buoys and Jason-3, respectively. Overall, this work illustrates that utilizing CFOSAT data for spectrum assimilation and SWH assimilation can enhance wave simulation accuracy under different conditions, which may point to promising applications in hurricane forecasting and disaster reduction research.

A predictive machine learning model for estimating wave energy based on wave conditions relevant to coastal regions

Growth and expansion in construction has increased recently and especially in coastal areas. In Alexandria, Egypt, mega projects such as El-Max Port Project (Middle Port), Port of ABU QIR (EG AKI), hotels, and restaurants were spread along the coastal lines, thus, it will need a high electrical energy. Although, the great economic benefits of such projects, it will have some negative impacts, such as overloading on the present grid. According to recommendations of COP 27, Egypt is one of the countries targeting to increase the dependency on green energy to minimize the production of greenhouse gases. This study is interested in wave energy as a renewable source of energy. Using a machine learning model that predicts wave height and wave period through the year 2030 in three separate places (Alamein, Alexandria, and Mersa-Matruh), this study will try to estimate the future amount of wave energy along Egypt's coast. Hourly measurements of the significant height and the mean wave period for the period 1979–2023 have been utilized for this. An extractor for wave energy can also be built on the Overtopping Breakwater for Energy Conversion (OBREC) in order to use this energy to fill the hole in the electric grid. The machine learning model was developed using hourly wave height and period data from three buoys, and as a result, the results have a root mean square error (RMSE) of 0.52. The amount of energy taken, wave power, and system efficiency at each place were then fully determined using a mathematical model for each of the three locations. The area along the coast of Alamein had the highest energy extraction rates, followed by Alexandria and Mersa-Matruh in that order. The results of the mathematical model indicate that the yearly power generation for Alamein, Alexandria, and Mersa-Matruh is 25287 MWhr, 14713 MWhr, and 4865 MWhr, respectively.