Simulating Waves Nearshore Model Research Articles

An analysis of surface waves in the Caribbean Sea based on a high-resolution numerical wave model

Surface waves are extremely important in a large variety of oceanographic applications and thus, the study of their spatiotemporal characteristics remains crucial. This study analyzes waves in the Caribbean Sea (CS) and western Atlantic Ocean (AO) using a high-resolution (HR) Simulating WAves Nearshore model validated with buoy observations and paired with a HR bathymetric dataset from 2010 – 2019. Island sheltering effects are examined but special attention is given to these effects under Hurricane Dorian in The Bahamas using observations from the China-France Oceanographic Satellite. Results illustrate that wave heights within the CS fluctuated with Caribbean Low-Level Jet activity, but a different wave regime exists within the AO. While wind waves overwhelmingly dominate the wave field and this is true even in the AO, surprisingly, the contribution of swell in the central CS was equal to one site in the AO. Possibly, due to interaction with the shallow Nicaraguan Rise, wave heights were strongly (depth-induced) refracted nearly 45°, a feature unseen in previous research using coarse bathymetric datasets. Island sheltering effects were pervasive and were naturally most pronounced under hurricane conditions. Crucially, New Providence in The Bahamas is vulnerable to hurricane-forced waves funneled through the Grand Bahama and Northeastern Providence Channels.

An analysis of the expected wave conditions in the Mediterranean Sea in the context of global warming

The main objective of the work proposed herewith is to assess the climate change impact on sea state conditions in the Mediterranean Sea under Representative Concentration Pathways and Shared Socio-economic Pathways scenarios and its expected dynamics in the near future. To perform this, the analysis is structured in two-time intervals, each one covering 30 years. The first period represents the recent past, while the second comprises the near future (2041–2070) under the RCP8.5 and SSP5-8.5 scenarios. A wave modelling system based on the SWAN (Simulating WAves Nearshore) model is implemented and validated in the target area using ERA5 wind data to drive the wave model for the recent past. Then, for the same period, simulations with the wave modelling system forced by the Regional Climate Model wind fields are carried out. For the near future, wind fields under the above-mentioned scenarios are considered to drive the wave model. Various analyses and comparisons are performed to identify the climate change impacts on the wave climate and wave power, to evaluate the differences between the results obtained for each scenario, and also the evolution of the characteristics of the extreme values in the Mediterranean Sea basin. No high differences between the average values of significant wave height and wave power obtained under the two scenarios along the entire basin of the Mediterranean Sea were found. In the marginal seas sometimes different trends appear, especially in the Aegean and Tyrrhenian Seas. Regarding the 99th percentile of Hs, under the SSP5-8.5 scenario, they are slightly higher than those under the RCP8.5 scenario.

A general method to determine the optimal whitecapping dissipation coefficient in the SWAN model

Whitecapping dissipation is a critical term in affecting the accuracy of wave height modeling. However, the whitecapping dissipation coefficient (Cds), as a primary factor influencing whitecapping, is commonly determined through trial and error in various studies. In this study, we present a general method for calibrating the Simulating Waves Nearshore (SWAN) wave model using the whitecapping dissipation term, demonstrated through a detailed study in the South China Sea (SCS). Theoretical analysis reveals that the optimal Cds value shows a one-to-one correspondence with the applied wind field. Expectedly, under high-quality wind field conditions, the optimal Cds values tend to fall within a narrow range, regardless of the model domain or time span. Numerical experiments executed in the SCS further consolidated this inference, encompassing two common wind input schemes (ST6 and YAN) and three distinct whitecapping dissipation schemes (KOMEN, JANSSEN, and WST). Based on the experimental results, we have identified an optimal Cds range for each whitecapping dissipation scheme. Cds values within the optimal range consistently outperformed the default Cds in the SWAN model. Subsequent experiments verified the method’s applicability to the Gulf of Mexico and the Mediterranean Sea. The findings suggest that this research holds substantial promise for practical applications on a global scale.

Effects of Terrain near Taiwan Island on Typhoons with Different Tracks and Typhoon Waves

The terrain, such as Taiwan Island, have been shown to have complex effects on typhoons and the associated typhoon waves. Terrain effects change with typhoon tracks. In this study, three types of typhoon tracks (northern, middle and southern) were defined according to the relationship between the typhoon tracks and Taiwan Island. Typhoons on these three tracks and typhoon waves were simulated using the Weather Research and Forecasting–Simulating Waves Nearshore model. In each type of typhoon, a control case without the Taiwan topography was simulated to compare with real cases. The results showed that typhoons on different tracks were affected by the terrain of Taiwan Island in different ways. Taiwan Island had weakening, decelerating and deflective effects on typhoons. The ranking for the weakening effect was middle track (81%) > southern track (69%) > northern track (3%). The decelerating effect was 7% in the northern track and 25% in the southern track. The deflective effect of the terrain makes typhoons on the northern (southern) track deflect toward the south (north). When a typhoon on a middle track passed over Taiwan Island, a new center of low pressure would replace the former center and make the track discontinuous. In addition, the influence of typhoons on regions near Taiwan Island changed with the typhoon’s tracks. The influence ranking of typhoons in Taiwan Island was the middle track > southern track > northern track, which was consistent with that of typhoons in the Taiwan Strait and opposite to that of typhoons in Fujian Province. The influence ranking of typhoons on the Taiwan Strait was the opposite of the typhoon intensity ranking, which suggests that the intensities of wind and waves in the strait were more related to typhoon tracks than typhoon intensity. The variations in the significant wave height were similar to those of the wind speed, but there was a time lag (2 h) between them due to the wave growth process and swells. In addition, the significant wave height distribution sometimes differed from the wind speed distribution under the influence of swells and terrain.

An evaluation of the synergy between the wave and wind energy along the west Iberian nearshore

There is a growing interest in extracting marine renewable energy and contributing in this way to the green road towards a low-carbon future. From this perspective, the Iberian nearshore represents a coastal environment with very high potential. In this context, the objective of the work proposed herewith is to perform a joint evaluation of the wave and wind power along the west Iberian coastal environment. The analysis is structured in two time intervals. The first is the 20-year period (2001–2020), for which the ERA5 wind data have been considered. The second 20-year period (2026–2045) corresponds to the near future and in this case, wind data provided by the climate model RCA4 (Rossby Centre regional atmospheric model, version 4) have been processed and analysed, corresponding to the climate scenario RCP4.5. (Representative Concentration Pathway). The same two periods have been considered for the evaluation of wave power. In this case simulations with spectral phase averaged model SWAN (Simulating WAves Nearshore) have been performed. This wave modelling system was implemented for the entire North Atlantic basin and focused on the west Iberian coastal environment. Finally, the results also indicate that there is a good synergy between wind and wave power in the west Iberian nearshore. As a conclusion, the development of joint wind–wave projects along the west Iberian coastal environment represents a viable direction for the near future. Especially, the Northwestern part of the Iberian nearshore has a very good potential both in terms of wave and wind energy.

NUMERICAL SIMULATION OF GRAVEL NOURISHMENT TO THE SEISHO COASTLINE IN JAPAN

It is feared that as a consequence of the potential future intensification of tropical cyclones due to ongoing climate change the patterns of coastal erosion around the world could change. In this study, the mesoscale meteorological model WRF (Weather Research and Forecasting), the third-generation stand-alone wave model SWAN (Simulating WAves Nearshore), and the coastal deformation model XBeach (eXtreme Beach behavior) are combined to calculate the short-term coastal deformation caused by high waves during the approach of Typhoon No.21 (Paolo) in 2017 to the Seisho Coast and Niigata Coast in Japan (Nishida and Shibayama, 2020). The model is then applied to predict future patterns of coastal erosion. Potential countermeasures to prevent future coastal erosion are then examined. The construction of detached breakwaters, which are currently used as a countermeasure against coastal erosion in many parts of the world, is often opposed by local residents due to the changes they can produce on the fishing environment and landscape. Therefore, it is often desirable to stabilize the beach without constructing structures offshore. Thus, gravel nourishment is proposed as a countermeasure against the short-term erosion that can be caused high waves, and its benefits and impacts were verified through numerical simulations.

A Numerical Simulation Study on the Probable Maximum Typhoon Wave in the South China Sea

The South China Sea spans the tropics and subtropics. Tropical cyclones in the area are extremely active, due to the features of its marine environment, such as wide water-depth profile, complex topography and hydrology. The maximum wave heights along the coast of China are normally generated by typhoons. Especially in the context of global warming, extreme weather events have significantly increased in the recent years, leading to more frequent strong and super typhoons. With the development of resources and energy in China expanding into the deep sea, extreme waves have caused serious damage to sea projects, endangering people’s lives and properties. Selecting the accurate typhoon gradient model to calculate various extreme waves, including the probable maximum tropical cyclone (PMTC) wave, is of significance for the safety of marine engineering construction and disaster prevention and mitigation. In this paper, we first proposed a wind field fusion model suitable for the South China Sea by superimposing an empirical typhoon model with the background wind field, and further verified it using the measured typhoon data. Secondly, the fused wind field was used as the input wind field of the SWAN (Simulating Waves Nearshore) model, and the wave fields of typhoons “Usagi” and “Mangosteen” were used to verify the model. The relevant parameters of PMTC were calculated using the Pearson Type III frequency fitting method, while the verified SWAN model was used to calculate the probable maximum typhoon wave, and P-III frequency analysis was carried out by direction of the extrapolated result of typhoon waves to obtain the design wave elements of each return period. Finally, a model for calculating the probable maximum typhoon wave suitable for this sea area was proposed to derive the characteristic parameters and time-histories of the probable maximum typhoon wave and the wave heights and their corresponding frequencies of various extreme waves.

Ocean Surface Gravity Wave Evolution during Three Along-Shelf Propagating Tropical Cyclones: Model’s Performance of Wind-Sea and Swell

Despite recent advancements in ocean–wave observations, how a tropical cyclone’s (TC’s) track, intensity, and translation speed affect the directional wave spectra evolution is poorly understood. Given the scarcity of available wave spectral observations during TCs, there are few studies about the performance of spectral wave models, such as Simulating Waves Nearshore (SWAN), under various TC scenarios. We combined the National Data Buoy Center observations and numerical model hindcasts to determine the linkages between wave spectrum evolution and TC characteristics during hurricanes Matthew 2016, Dorian 2019, and Isaias 2020. Five phases were identified in the wave spectrogram based on the normalized distance to the TC, the sea–swell separation frequency, and the peak wave frequency, indicating how the wave evolution relates to TC characteristics. The wave spectral structure and SWAN model’s performance for wave energy distribution within different phases were identified. The TC intensity and its normalized distance to a buoy were the dominant factors in the energy levels and peak wave frequencies. The TC heading direction and translation speed were more likely to impact the durations of the phases. TC translation speeds also influenced the model’s performance on swell energy. The knowledge gained in this work paves the way for improving model’s performance during severe weather events.

Genetic algorithm and deep learning models compared for swell wave height prediction

A comparative study has been conducted between genetic algorithm (GA) and deep learning models to predict swell wave heights in the Bay of Bengal (BOB) region. To simulate the required parameter SWAN (Simulating Waves Nearshore) model is integrated with daily 25 km wind from 2009 to 2018 for July and December separately representing the southwest and northeast monsoons respectively. For the BOB region empirical orthogonal function (EOF) analysis is applied on the swell parameter to study the spatial and temporal patterns. GA is applied on the principal component of swell wave heights to generate a forecast explicit equation and thus a basin scale EOF-GA model is established. Next a grid (200 N, 900E) is chosen in the head bay region and the outcomes of the standalone GA model and the deep learning models are compared to predict the time series data of swell wave heights (SWS). It is observed that the performances of the deep learning model is better during the calm conditions in December than the rough seas in July. Another grid (150 N, 820E) is chosen along the east coast through which the severe cyclonic storm PHETHAI (13–18 December 2018) passed and the model accuracies are tested. The EOF-GA model serves as an effective computationally cheap basin scale forecast model. Thus, both the genetic algorithm and deep learning models can be developed and utilized for normal and extreme wave prediction having wide application in the ocean engineering domains.

Multi-decade high-resolution regional hindcasts for wave energy resource characterization in U.S. coastal waters

Long-term, high-resolution, regional wave hindcast datasets were generated using unstructured-grid Simulating WAves Nearshore (SWAN) models for the U.S. coastal waters to support nearshore wave energy development in the U.S. including those bordering U.S. territorial islands. The model domains resolved the entire U.S. exclusive economic zones, with a spatial resolution of approximately 200 m nearshore. The regional SWAN models were driven by the global WAVEWATCH III® model outputs and run for a 42-year period from 1979 to 2020. Extensive model validations were performed using buoy observations and altimeter data. Regional resource characterization was performed based on hindcast data points at 2 km from shore and along the 100 m isobath. Aggregations of wave resource parameters were produced, and spatial and seasonal variations were analyzed for all the regions. Wave resource metrics recommended by international standards, including a 3-h time series of six resource parameters, hourly frequency- and directionally resolved wave spectra at selected “virtual buoy” locations, and average-annual values of omni-directional wave power, significant wave height, and energy period are publicly disseminated through an Amazon Web Service and a Marine Energy Atlas web application tool to facilitate wave energy research and a wide range of coastal ocean applications.

A Comparison of Wave Spectra during Pre-Monsoon and Post-Monsoon Tropical Cyclones under an Intense Positive IOD Year 2019

The impact of Indian Ocean Dipole (IOD) events on the generation and intensity of tropical cyclones under the influence of monsoons is explored. The standardized sea surface temperature (SST) anomalies are computed for the pre-monsoon and post-monsoon months for the Bay of Bengal (BOB) and Arabian Sea (AS) from 1971 to 2020 and relationships are analyzed with the frequency of tropical cyclones for the neutral, positive and negative IOD years. Ocean states are sensitive to cyclonic conditions exhibiting a complex spectral distribution of the wave energy. Due to a tropical cyclone, the surface waves remain under high wind forcing conditions for prolonged periods generating a huge amount of energy. The spectral wave model SWAN (Simulating WAves Nearshore) is used to generate the energy density spectra during FANI (26 April–5 May 2019), which was a pre-monsoon extreme severe cyclonic storm, and BULBUL (5–12 November 2019), which was a post-monsoon very severe cyclonic storm in the BOB region. This study aims to estimate the intensity of wave energy during tropical cyclones in the pre- and post-monsoon months for 2019 (an extremely positive IOD year).

Whitecapping term analysis of extreme wind wave modelling considering spectral characteristics and water depth

The Simulating WAves Nearshore (SWAN) model can be used to simulate extreme wind waves such as tropical cyclone waves, which are of great significance for the study of coastal processes and the protection of coastal engineering. In this study, the basic setting and whitecapping term of the SWAN model are analysed to model tropical cyclone waves, with spectral characteristics and water depth taken into account. By investigating the temporal variation of bulk wave parameters and wave spectral characteristics of key nodes, the optimal values of 4 basic setting parameters are determined. By analysing the sensitivity of tuning effects of key coefficients in different whitecapping formulations, calibration criteria of these parameters are provided, and the coefficients Stpm and Powk in the Komen formulation are found to be most sensitive to the outputted significant wave height and mean wave period, respectively. To further study the sensitivity of these coefficients, especially their relationship with water depth, spatial analyses of sensitivity variations are performed in the coastal region, which show that the sensitivity of these coefficients increases first and then tends to be stable with the increase of water depth, indicating a critical water depth between 20 m and 50 m. This study provides insights for more effective and directional modelling of tropical cyclone waves in coastal and shelf seas.

Analysis of Komen scheme in the SWAN model for the whitecapping dissipation during the tropical cyclone

The extreme wave during the tropical cyclone poses a great threat to the coastal and ocean engineering. In this study, this wave is simulated by the Simulating WAves Nearshore (SWAN) model in the Yellow Sea, the East China Sea and the northern South China Sea where tropical cyclones frequently occur. The simulation focuses on the whitecapping dissipation with the Komen scheme, which controls the dissipation through several key coefficients, such as two wave-steepness-dependent coefficients (Stpm and Powst) and two wave-number-dependent coefficients (Delta and Powk). By calculating statistical parameters and introducing a quantitative evaluation method (Technique for Order Preference by Similarity to Ideal Solution, TOPSIS), optimal selections of key coefficients in the Komen scheme are determined after identifying reasonable ranges of these coefficients. The simulated wave parameters of the calibrated model match the measured data well, demonstrating the reliability of the key coefficient combination in the Komen scheme. The underestimation of large significant wave heights is improved, with little variation of small significant wave heights, and the underestimation of wave periods is improved during the whole process of the tropical cyclone.

A regional wind wave prediction surrogate model based on CNN deep learning network

Accurate prediction of wave characteristics of large-scale waves is of great significance for ocean disaster prevention and shipping safety. The traditional method is mainly based on ocean numerical models using wind as input to predict regional waves. In recent years, machine learning methods have gradually been concerned and applied in wave prediction, providing an effective approach for the real-time forecasting of regional wind waves. This paper proposes a regional wind wave prediction surrogate model based on a convolutional neural network (CNN), which takes historical wind and wave data as input to realize the prediction of current waves. Herein, the wind datasets from the European Center for Medium-Term Weather Forecast (ECMWF) and validated wave datasets simulated by the Simulating Waves Nearshore (SWAN) model are employed in model verification and optimization. We investigate the influence of historical winds and waves on the prediction accuracy of current waves and verify the adaptability of the surrogate model to different computational regions. The results show that the prediction accuracy is significantly promoted by considering historical wind and waves data in model input. The proposed model has good adaptability to different computational regions, and the results indicate that the surrogate model can effectively realize regional wind wave prediction, which greatly improves the computational efficiency as compared to the SWAN model. In addition, its relative error with the verified SWAN simulation results is less than 5%.

Comparison of the predicted and the observed wave spectral parameters during the storms at Filyos coasts, The Southwestern Black Sea

In-situ wave measurement data are mainly used to validate the bulk wave parameters predicted by numerical models. Although the frequently used third-generation wave models are spectral models, determination of various spectral parameters and validation with the observed data are not common. This study covers the spectral analysis of selected storm records of a nearshore wave measurement campaign carried out at Filyos coasts with the complex bottom topography in Turkey, Southwestern Black Sea. The bulk wave and the spectral parameters are also calculated by a third-generation nearshore wave model, SWAN (Simulating Waves Nearshore), forced by the ERA5 offshore wave data, which is the newest re-analysis of the European Centre for Medium-Range Weather Forecasts (ECMWF) for the selected storms. Before using ERA5 offshore wave data, they are calibrated by the wave data of the satellite radar altimeter. In-situ measured bathymetry data are used in the SWAN model. Observed and predicted bulk wave and spectral parameters are compared, and the statistical error measures are calculated not only for the significant wave height, the peak period, and the peak wave direction but also for the three different spectral periods, three different frequency width parameters, a directional width and, a spectral peakedness parameter for the first time. Low values of statistical error measures show that the current wave predictions have a good agreement with the observed ones in terms of the significant wave height, Hs, and the peak period, Tp. However, the SWAN model predicts a slightly narrower frequency and directional spectrum with higher peaks, although the error measures are low. Moreover, SWAN can not predict the wide range of spectral shape occurrences that the observed spectra have. The development of the various spectral parameters during the storms is also investigated for the first time. It is found that the frequency and directional spreading of the observed spectra become wider and unsharpened in the late stages of the storm compared to the early stages. However, the same tendency is not observed clearly in the predicted directional spreading.

The near future expected wave power in the coastal environment of the Iberian Peninsula

The main target of the proposed work is to assess the expected wave energy dynamics in the coastal environment of the Iberian Peninsula. This study is motivated by the fact that the Iberian nearshore is considered among the most promising European coastal environments for wave energy extraction with experimental wave projects already operating there since many years. The near future period considered for this analysis is the 20-year time interval 2026–2045 under the Representative Concentration Pathway scenario 4.5 (RCP4.5). In parallel, in order to have a coherent degree of comparison, the results are compared with those corresponding to the recent past 20-year time interval 2001–2020. A wave modelling system based on the third-generation spectral phase averaged model SWAN (acronym from Simulating WAves Nearshore) was implemented for the entire North Atlantic basin and focused on the Iberian nearshore. Furthermore, calibrations and extensive validations of this wave prediction system have been performed. Thus, system reliability was evaluated both against buoy and satellite data and the results were found as having good accuracy. The wave energy data corresponding to the near future (2026–2045) are compared with those from the other 20-year period from the recent past (2001–2020). In this way, a comprehensive picture of the expected wave power dynamics in the coastal environment of the Iberian Peninsula is provided. The results show that there are no significant differences between the two periods considered, especially in the transition seasons (spring and autumn), both in terms of maximum values and also as regards the general patterns of the wave power fields. However, in the near future, a decrease of the resources is expected near to the coast in the summer season, compared to the values simulated for the recent past (from 12 kW/m to 9 kW/m). A contrary tendency corresponds to the winter season when for the near future we find a maximum value higher by about 14 kW/m than in the recent past.

Numerical study of a CNN-based model for regional wave prediction

The accurate prediction of large-scale waves is of great significance for marine resource assessment and ocean disaster prevention. In recent years, a new generation of wave models represented by the Simulating Waves Nearshore (SWAN) model has been widely applied in the field of large-scale wave prevention. However, high accuracy of long-term wave simulation using the SWAN model is costly in computation resources. Considering the high prediction efficiency of machine learning methods, this paper proposes a CNN-based regional wave prediction (CNN-RWP) model. We apply a convolutional neural network (CNN) to construct the mapping relationship between wind data and wave data, which takes an hourglass configuration. In this paper, wind datasets from the European Center for Medium-Term Weather Forecast (ECMWF) and the validated wave datasets simulated by SWAN were employed in model-training optimization, verification, and generalization analysis. A comparison between the CNN-RWP model and the SWAN model is addressed using a dataset from the Gulf of Mexico. The mean absolute error between the prediction result of the CNN-RWP model and the SWAN output result is less than 10%, while the calculation efficiency is improved by about 1000 times. The proposed CNN-RWP model provides an effective approach for achieving high-precision wave prediction.

The effect of changing sea ice on wave climate trends along Alaska's central Beaufort Sea coast

Abstract. Diminishing sea ice is impacting the wave field across the Arctic region. Recent observation- and model-based studies highlight the spatiotemporal influence of sea ice on offshore wave climatologies, but effects within the nearshore region are still poorly described. This study characterizes the wave climate in the central Beaufort Sea coast from 1979 to 2019 by utilizing a wave hindcast model that uses ERA5 winds, waves, and ice concentrations as input. The spectral wave model SWAN (Simulating Waves Nearshore) is calibrated and validated based on more than 10 000 in situ time point measurements collected over a 13-year time period across the region, with friction variations and empirical coefficients for newly implemented empirical ice formulations for the open-water and shoulder seasons. Model results and trends are analyzed over the 41-year time period using the non-parametric Mann–Kendall test, including an estimate of Sen's slope. The model results show that the reduction in sea ice concentration correlates strongly with increases in average and extreme wave conditions. In particular, the open-water season extended by ∼96 d over the 41-year time period (∼2.4 d yr−1), resulting in a 5-fold increase in the yearly cumulative wave power. Moreover, the open-water season extends later into the year, resulting in relatively more open-water conditions during fall storms with high wind speeds. The later freeze-up results in an increase in the annual offshore median wave heights of 1 % yr−1 and an increase in the average number of rough wave days (defined as days when maximum wave heights exceed 2.5 m) from 1.5 in 1979 to 13.1 d in 2019. Trends in the nearshore areas deviate from the patterns offshore. Model results indicate a saturation limit for high wave heights in the shallow areas of Foggy Island Bay. Similar patterns are found for yearly cumulative wave power.

Using nested grids to simulate sediment transport in the Caspian Sea with account taken of global and local scales

Introduction. The paper provides information on hydrodynamic and lithodynamic characteristics of processes in the Caspian Sea. A description of applied hydrodynamic models is presented. The numerical simulation of circulation, wave and lithodynamic conditions in the waters of the Northern Caspian Sea, middle and southern parts of the Caspian Sea was first conducted using composite computational grids. Simulated sediment transport results are presented. Results of numerical calculations of sediment accumulations in the second sweep of the Volga-Caspian canal are discussed.
 Materials and methods. Numerical modeling of coastal wave and hydrodynamic processes is performed using the Delft3d morphodynamic computer model. The Delft3D wave module includes a SWAN (Simulating Waves Nearshore) model for calculating the propagation and generation of waves. The hydrodynamic flow is simulated using the FLOW module, that solves non-stationary hydrodynamic equations and has a sediment transfer unit.
 Results. Computational grids are generated; they have a large step equal to 5,000 m. One grid is built for the middle and southern parts of the Caspian Sea and the other is made for the Northern Caspian; the grid step is equal to 1,000 m. The height and direction of significant waves in the Northern Caspian are provided. Sediment flows, averaged over 210 days in the SC, are provided. The authors also provide graphs of cumulative accumulation/erosion in the second sweep of the Volga-Caspian canal.
 Conclusions. The authors show that the average yearly shore-parallel sediment flow in the area of the marine edge of the Volga river delta has a southwesterly direction, and an estimate of its magnitude is provided. An assessment of sediment accumulation in the Volga-Caspian canal is made, and its results are compared with the data obtained in the course of dredging works.

Assimilating Near-Surface Wind Retrievals from High-Frequency Radars

Abstract High-frequency radars (HFR) are traditionally used in coastal environments to observe ocean current and wave characteristics. With an HFR forward model, HFR adjoint model, and the Simulating Waves Nearshore model, HFR Doppler spectra observations were used to estimate near-surface winds in the Southern California Bight in October 2017. The HFR 10-m wind retrievals were assimilated into the Coupled Ocean–Atmosphere Mesoscale Prediction System (COAMPS) with the COAMPS four-dimensional variational (4DVar) assimilation system to integrate the HFR wind retrievals into the initial conditions. Impact of the HFR-derived winds on the forecast are evaluated in terms of adjoint-derived forecast sensitivity observation impact (FSOI), and by an observing system experiment that compared forecasts from simulations that assimilated the HFR wind retrievals to a control simulation that excluded HFR winds. The addition of the HFR-estimated wind observations reduced the error in the forecasted dry energy norm in the lowest model level and also contributed to small improvements in the 10-m wind field over a 25-day experiment. The potential benefit of this new method to estimate near-surface ocean winds near the coast for data assimilation and improved numerical weather prediction is an exciting advancement in remote sensing of coastal winds and expands the benefit of existing HFR networks beyond their intended use. More importantly, wind fields retrieved from HFR have the potential to fill an observation gap near the shoreline where ship and buoy observations are sparse and scatterometer observations are unavailable due to land contamination.