Global Wave Model Research Articles

Improvements in Wave Age-Based Sea and Swell Separation for Offshore Industry Applications

Abstract For offshore wind turbines, an accurate description of the wave conditions is crucial for their design, construction, installation, maintenance, and operation. Typical sea states in the open ocean are often a combination of wind-sea and swell systems which cannot be fully described by a single set of parameters. In such cases the wave conditions are better defined by separating and quantifying the individual wave systems. In this paper, the wave age method has been revisited, and a modification has been proposed to add more functionality to better describe the wind/wave alignment of the wind-sea. A qualitative analysis was performed using offshore spectral energy data from a global wave model. The analysis was carried out for four different geographical locations: the Philippine Sea, the North Sea, the Atlantic Ocean, and the Pacific Ocean. The analysis compares the wind-sea and swell partitions separated by the standard and modified wave age methods. It shows an improved description of the wind-sea compared to the standard equation, especially in the case of high wind speed events and where the wind-sea partition is expected to dominate most of the spectral energy.

MS-GWaM: A Three-Dimensional Transient Gravity Wave Parametrization for Atmospheric Models

Abstract Parameterizations for internal gravity waves in atmospheric models are traditionally subject to a number of simplifications. Most notably, they rely on both neglecting wave propagation and advection in the horizontal direction (single-column assumption) and an instantaneous balance in the vertical direction (steady-state assumption). While these simplifications are well justified to cover some essential dynamic effects and keep the computational effort small, it has been shown that both mechanisms are potentially significant. In particular, the recently introduced Multiscale Gravity Wave Model (MS-GWaM) successfully applied ray-tracing methods in a novel type of transient but columnar internal gravity wave parameterization (MS-GWaM-1D). We extend this concept to a three-dimensional version of the parameterization (MS-GWaM-3D) to simulate subgrid-scale nonorographic internal gravity waves. The resulting global wave model—implemented into the weather forecast and climate code Icosahedral Nonhydrostatic (ICON)—contains three-dimensional transient propagation with accurate flux calculations, a latitude-dependent background source, and convectively generated waves. MS-GWaM-3D helps reproduce expected temperature and wind patterns in the mesopause region in the climatological zonal mean state and thus proves a viable internal gravity wave (IGW) parameterization. Analyzing the global wave action budget, we find that horizontal wave propagation is as important as vertical wave propagation. The corresponding wave refraction includes previously missing but well-known effects such as wave refraction into the polar jet streams. On a global scale, three-dimensional wave refraction leads to a horizontal flow-dependent redistribution of waves such that the structures of the zonal mean wave drag and consequently the zonal mean winds are modified.

Ocean waves in the southwestern Atlantic Ocean: An evaluation of global wave models

AbstractIn situ wave observations are essential for implementing coastal management strategies, designing coastal and offshore structures and understanding many processes related to sedimentology and marine biology. Despite their importance, these observations are rather scarce in the coastal areas of southeastern South America. Moreover, the existing observations are discontinuous, with several gaps and no simultaneity. Today, global wave models have improved their performance by providing reliable wave parameters with high resolution and coverage even at near‐shore areas. In this work, wave parameters from five global models were compared with significant wave heights (HS), peak periods (T) and propagation direction at nine sites located at the Southwestern South Atlantic Ocean. The global products used were WAVERYS (Global Reanalysis Wav 001 032), IOWAGA (WAVEWATCH III Hindcast – WW3 – Global grid – CFSR), ERA5 (ECMWF reanalysis), NOAA WW3 – 30‐year Hindcast Phase 2 and NCEP/EMC Operational global wave model GFS – Wave. Mean arithmetic error (Bias), root mean square deviation, scatter index and the Pearson linear correlation coefficient (r) were assessed at each location where wave observations were available. The best agreements for HS were obtained at the deepest sites located farther from the coast. Lower statistical metrics were achieved at coastal sites where the bathymetry is roughly represented by the global models. The calculated correlation coefficients were found to be lower for T than for HS. Overall, T was slightly underestimated by global models. Wave propagation directions were excellently represented by global models at offshore sites. The WAVERYS model is the global product that best represents the wave parameters in the study area, probably due to its higher spatial resolution (0.2°) and more realistic atmospheric forcing (ERA5). In addition, it satisfactorily represents the temporal variability of HS in open water as well as swell characteristics at the outer the Río de la Plata.

Cross-Shore Modeling Features: Calibration and Impacts of Wave Climate Uncertainties

Numerical models can be powerful tools for evaluating the best scenarios for the construction of artificial nourishments to mitigate coastal erosion. Until recent decades, when looking at medium- to long-term simulations, cross-shore and alongshore processes have been studied separately. Accounting for both processes in a shoreline evolution numerical model would improve the understanding and predictive capacity of future changes in coastline evolution. The AX-COAST project aims to develop new capacities in modeling cross-shore sediment transport processes by adding the CS-Model, a cross-shore numerical model, into the existing LTC (Long-Term Configuration) model. The LTC model is a shoreline evolution numerical model which is a module of the cost–benefit assessment tool COAST. This work presents the first steps of the CS-Model implementation, which involve evaluating its performance by calibrating the model with extensive measured datasets of wave climate, beach profiles, tide levels, etc., from coastal areas in IJmuiden and Sand Motor in the Netherlands. The results show good agreement between modeled and observed values. Additionally, wave climate datasets derived from global and regional wave models were considered to evaluate modeling performance at IJmuiden. Using derived timeseries from the wave models did not significantly lead to different results compared to using measured data. The obtained mean absolute and relative errors for each profile were low for both types of datasets. Calibration processes with consistent data are important in modeling simulations to accurately represent the study area and ensure the credibility of future simulations.

An investigation of sea state conditions at the time of ship accidents in Indonesia

ABSTRACT In this study, we investigate the sea state conditions during the two cases of ship accidents that have recently occurred in the Indonesian ocean. The first accident case occurred in the middle part of Indonesia at the end of May 2022 resulted in fatalities. Similarly, another incident was in the eastern part of Indonesia in the middle of July 2022, killing 12 people. For both cases, we observed the maximum wave height, significant wave height, and wind speed during the respective accident periods. The observations were carried out using the global ocean wave model, WAVEWATCH III, in conjunction with Altimeters and ASCAT scatterometers. Specifically, the study employed data from Cryosat-2, Jason-3, Saral, Sentinel-3B, Sentinel-6A, Metop-B and Metop-C. The resultant satellite observations are consistent with the model, meaning that the wave heights at the time of the accident were very high. The first case clearly shows that strong wind and high waves most likely triggered the accident. As a result, all stakeholders who run their ocean transport business must consider the warning from the related agency, which is, in this case meteorological, climatological, and geophysical agency. Such proactive measures have the potential to mitigate marine accidents stemming from adverse weather conditions.

Coastal Data Information Program: advances in measuring and modeling wave activity, climate, and extremes

ABSTRACT The Coastal Data Information Program (CDIP) provides wave data to the public in near real-time, maintains an operational wave model for the California coast, and is engaged in ocean wave research on a global scale. CDIP’s array of moored wave buoy stations are instrumented with Datawell Waveriders. CDIP’s recent work to improve monitoring capabilities for directional surface wave spectra, surface current, and temperature is described. This includes data quality assurance and control, real-time alerts, telemetry and dissemination information technology infrastructure and methodology, data visualization tools, and mooring and instrumentation innovation. CDIP’s goal is to maximize data reliability, availability, accuracy, and precision. Validation of commonly used wave models – operational, hindcast, and forecast – with CDIP data is automated to provide assessments of relative skills at a variety of coastal locations and wave conditions. Wave heights measured by the buoys typically exceed modeled heights during the most energetic events, such as hurricanes, nor’easters, and bomb cyclones. In a practical validation example, the global wave model used to drive CDIP’s California coastal wave forecasts was recently changed, based on comparisons against buoy data. Forecast performance has improved for swell events, which are a challenge to model accurately in the Southern California Bight.

Evaluation of Spectral Wave Models Physics as Applied to a 100‐Year Southern Hemisphere Extra‐Tropical Cyclone Sea State

AbstractExtreme significant wave height estimates, and their probability of exceedance, are fundamental offshore and coastal engineering design parameters. These estimates are characterized by uncertainty due to an incomplete understanding of the atmosphere‐ocean energy and momentum exchanges during intense storms. This particularly affects extreme wave statistics of ocean regions exposed to large and frequent synoptic disturbances such as Extra‐Tropical Cyclones (ETCs). In this work, we assessed the performance of global phase‐averaged spectral wave models in representing the 1 in 100‐year sea state generated by a Southern Ocean ETC in April 2021. We collected in situ and remote sensing observations, from the storm generation region to its decaying phase and the impact on South‐East Australian coastlines. We compared the observations with a suite of reanalysis and hindcast global wave model data sets. While comparing well for wind speed up to 20 m/s, the models presented differences in solving the air‐sea momentum exchange between the atmosphere and the ocean for wind speed velocities between 20 and 35 m/s, which are a distinctive characteristic of ETCs. Despite marked differences in the storm generation region, the models converged to a similar representation of the swell systems impacting the South‐East Australian coastlines, as demonstrated by a comparison with deep‐water buoy observations close to the coastlines. Furthermore, we found that the energy of the ERA5 reanalysis, which assimilates satellite wave height measurements is quickly dispersed and, as such, of little advantage in representing the 9.9 m significant wave heights that impacted the South‐East Australian coastlines on 10 April 2021.

Impact of sea level changes on future wave conditions along the coasts of western Europe

Abstract. Wind waves and swells are major drivers of coastal environment changes and coastal hazards such as coastal flooding and erosion. Wave characteristics are sensitive to changes in water depth in shallow and intermediate waters. However, wave models used for historical simulations and projections typically do not account for sea level changes whether from tides, storm surges, or long-term sea level rise. In this study, the sensitivity of projected changes in wave characteristics to the sea level changes is investigated along the Atlantic European coastline. For this purpose, a global wave model is dynamically downscaled over the northeastern Atlantic for the 1970–2100 period under the SSP5–8.5 climate change scenario. Twin experiments are performed with or without the inclusion of hourly sea level variations from regional 3D ocean simulations in the regional wave model. The largest impact of sea level changes on waves is located on the wide continental shelf where shallow-water dynamics prevail, especially in macro-tidal areas. For instance, in the Bay of Mont-Saint-Michel in France, due to an average tidal range of 10 m, extreme historical wave heights were found to be up to 1 m higher (+30 %) when sea level variations are included. At the end of the 21st century, extreme significant wave heights are larger by up to +40 % (+60 cm), mainly due to the effect of tides and mean sea level rise. The estimates provided in this study only partially represent the processes responsible for the sea-level–wave non-linear interactions due to model limitations in terms of resolution and the processes included.

Verification and intercomparison of global ocean Eulerian near-surface currents

The OceanPredict task team for Intercomparison and Validation (IV-TT) has established the CLASS4 data standard for routine forecast verification against selected reference observing platforms. The set of CLASS4 reference data has been recently extended to include near-surface currents derived from the trajectories of drifting buoys drogued at 15 m. We have applied these data to the Ocean Model, Analysis and Prediction System (OceanMAPS) at the Australian Bureau of Meteorology (ABoM) for verification and inter-comparison with Mercator Océan International ocean forecast system (MOi), the operational models of the Met Office, UK: Forecast Ocean Assimilation Model (FOAM) and Coupled Atmosphere-Land-Ocean-Ice Data Assimilation (CPLDA) systems, and the Global Ice Ocean Prediction System (GDPS-GIOPS) at the Canadian Centre for Meteorological and Environmental Prediction (CCMEP). The aims for this verification analysis are to extend the routine monitoring of the operational system; to assess the OceanMAPS skill against other models; and to inform our stakeholders of the OceanMAPS performance.We have evaluated the impacts of adding Stokes drift and tidal currents from separate global wave and global tide models to the model currents on the verification of currents. Including Stokes drift leads to significant improvements in representation and the metrics for model skill while inclusion of tides has no significant impact on the global statistics. Zonal currents have a stronger signal than the meridional currents as indicated by their high correlation with observations, and lower errors. This indicates zonal currents on average are more predictable and persistent than the meridional currents, resulting in improved representation in the models.Overall, MOi, and the new version of OceanMAPS (4.0i), and FOAM show marginally better performance against the observations relative to other models. Although there are significant differences in the configurations of the eight models under evaluation, all the models are shown to be remarkably statistically equivalent with consistent spatial and temporal patterns indicating the main differences are attributable to unrepresented processes. We therefore conclude that there is significant scope to further improve the representation of the modelled currents with the observations.

The Impacts of the Application of the Ensemble Optimal Interpolation Method in Global Ocean Wave Data Assimilation

The ensemble optimal interpolation method was used in this study to conduct an examination of the assimilations of significant wave height (SWH) data from HY-2A satellite altimeter based on the WAVEWATCH III global ocean wave model. The results suggested that the ensemble optimal interpolation method using HY-2A SWH data played a positive role in enhancing the accuracy of the global ocean wave simulations and could effectively improve the deviations of SWH in the simulation processes. The root mean square errors of the NDBC buoy inspections were improved by 7 to 44% after the assimilation, and those of China’s offshore buoy inspections were improved by 3 to 11% after the assimilation. It was observed that the farther the buoys were from the shore, the better the effects of the assimilation improvements. The root mean square errors of the Jason-2 satellite data validations were improved by 17% after the assimilation, with monthly improvements of 8–25%. The improvements occurred in most of the global oceans, particularly in the Southern Ocean, the Eastern Pacific Ocean and the Indian Ocean. The results obtained in this research can be used as a reference for the operational applications of China’s ocean satellite data in ocean wave data assimilation and prediction.

Ensemble-based data assimilation of significant wave height from Sofar Spotters and satellite altimeters with a global operational wave model

An ensemble-based method for wave data assimilation is implemented using significant wave height observations from the globally distributed network of Sofar Spotter buoys and satellite altimeters. The Local Ensemble Transform Kalman Filter (LETKF) method generates skillful analysis fields resulting in reduced forecast errors out to 2.5 days when used as initial conditions in a cycled wave data assimilation system. The LETKF method provides more physically realistic model state updates that better reflect the underlying sea state dynamics and uncertainty compared to methods such as optimal interpolation. Skill assessment far from any included observations and inspection of specific storm events highlight the advantages of LETKF over an optimal interpolation method for data assimilation. This advancement has immediate value in improving predictions of the sea state and, more broadly, enabling future coupled data assimilation and utilization of global surface observations across domains (atmosphere-wave-ocean).

Multiparametric sea state fields from synthetic aperture radar for maritime situational awareness

This paper introduces a method for estimating a series of sea state parameters from satellite-borne synthetic aperture radar (SAR). The method was realized in a near real time (NRT) application which allows for the processing of data from different satellites and modes. The algorithm estimates the total significant wave height Hs, dominant and secondary swell and windsea wave heights, first, and second moment wave periods, the mean wave period and the period of wind sea. The algorithm was applied to the Sentinel-1 (S1) C-band Interferometric Wide Swath Mode (IW), Extra Wide (EW) and Wave Mode (WV) Level-1 (L1) products and also extended to X-band TerraSAR-X (TS-X) StripMap (SM) products. The scenes are processed in raster and result in continuous sea state fields, with the exception of S1 WV, where averaged values for sea state parameters for along-orbit imagettes of 20 km × 20 km are presented.The developed empirical algorithm consists of two parts: a first CWAVE_EX (extended CWAVE) part, based on a linear regression approach, and a subsequent machine learning part using the support vector machine (SVM) technique. A series of new data preparation steps (i.e. filtering, denoising) and new features estimated from SAR images are also introduced. The algorithm was tuned and validated using two independent global wave model hindcasts, WaveWatch-3 and MFWAM as well as National Data Buoy Center (NDBC) measurements. The achieved root mean squared errors (RMSE) for CWAVE_EX for the total Hs are 0.60 m for low-resolution modes S1 IW (10 m pixel) and EW (40 m pixel) and 0.35 m for S1 WV and TS-X SM (pixel spacing ca.1–4 m) in comparison to model predictions. The RMSEs of the retrieved wave periods are in the range of 0.45–0.90 s for all of the satellites and models considered. Similarly, the dominant and secondary swell, and wind sea wave height RMSEs are in the range of 0.35–0.80 m. The SVM postprocessing improves the accuracy of the initial results of CWAVE_EX for Hs and reaches an RMSE of 0.25 m for S1 WV. Comparisons to 64 NDBC buoys, collocated at distances shorter than 50 km to S1 WV imagettes worldwide, result in an RMSE of 0.41 m. All results and the methods presented are novel in terms of the accuracy achieved, combining the classical approach with machine learning techniques, and performing an automatic NRT processing of multiparametric sea state fields from L1 data with automatic switching for different satellites and modes.The complete archive of S1 WV L1 Single Look Complex products from December 2014 until February 2021 was processed to create a sea state parameter database and validated using model hindcast and buoy measurements. The derived parameters are available to the public within the scope of the European Space Agency's Climate Change Initiative.

Quantifying microseismic noise generation from coastal reflection of gravity waves recorded by seafloor DAS

SUMMARY Secondary microseisms are the most energetic noise in continuous seismometer recordings. They are generated by interactions between ocean waves, including between gravity waves incident on and reflected from the coast. Coastal reflections of ocean waves leading to coastal microseismic sources are hard to estimate in various global numerical wave models, and independent quantification of these coastal sources through direct measurements can greatly improve these models. Here, we exploit a 41-km-long submarine optical fibre (OF) cable located offshore Toulon, France, using distributed acoustic sensing (DAS). We record both the amplitude and frequency of seafloor strains induced by ocean surface gravity waves, as well as secondary microseisms caused by the interaction of gravity waves incident and reflected from the coast. By leveraging the spatially distributed nature of DAS measurements, additional fundamental information is recovered such as the velocity and azimuth of the waves. We find that on average 30 per cent of the gravity waves are reflected at the coast generating local sources of secondary microseisms that manifest as Scholte waves. These local sources represent the most energetic contribution to the seismic noise recorded along the OF and by an onshore broad-band station located near the DAS interrogator. Furthermore, we estimate a coastal reflection coefficient of ocean surface gravity waves R2 of about 0.07, which provides improved constraints for seismic noise generation models. In addition, we show that new local sources of microseisms can be generated when gravity waves characteristics (azimuth and frequency content) change and lead to some delays between the OF cable and buoy recordings. These analyses pave the way for a wide use of DAS data to monitor ocean–solid earth interactions as they provide a wealth of information on the reflection of gravity waves, coastal microseismic sources, and new constraints for numerical models of microseismic noise.

Variability in High Wave Energy Events Around the Southern African Coast

AbstractThe wave energy flux along the southern African coastline often reaches extreme levels, seriously impacting coastal communities, infrastructure, as well as near‐coast and offshore marine operations. Understanding the drivers behind past high wave energy events and their frequency is key to forecasting future events. Using in‐situ wave buoy data recorded by the Council for Scientific and Industrial Research (CSIR), the ERA5 and CACWR global wave model products are evaluated. The better‐performing ERA5 product is used to assess long‐term variability and trends around the coastline between 1979 and 2020. There are significant increasing trends in offshore flux for all seasons, with spring having the strongest coastal trends. Substantial interannual variability exists in wave energy flux and direction. Possible drivers, the Southern Annular Mode (SAM), El Niño Southern Oscillation (ENSO), and the semi‐annual oscillation (SAO), were assessed to find that SAM appears to show the strongest relationship overall. Negative (positive) SAM is associated with above (below) average and westerly (easterly) anomalies in both flux and direction. ENSO directly impacts the summer wave climate while the SAO indirectly impacts the wave energy flux over spring and winter. During many seasons, more than one climate mode is active. It is found the strongest significant near‐coast positive westerly anomalies occurred under negative SAM and negative SAO, with more intense offshore anomalies under El Niño, whereas the strongest significant negative east‐northeasterly anomalies occurred under a combination of La Niña with positive SAM and SAO phases.

Effective coastal adaptation needs accurate hazard assessment: a case study in Port Resolution, Tanna Island Vanuatu

Developing countries face risks from coastal hazards that are being amplified by climate change. The selection of effective adaptation interventions to manage these risks requires a sufficiently accurate assessment of the coastal hazard at a given location. Yet challenges remain in terms of understanding local coastal risks given the coarseness of global wave models and the paucity of locally scaled data in most developing countries, including Small Island Developing States (SIDS) like Vanuatu. This paper aims to examine the differences in hazard assessment and adaptation option selections arising from analyses using globally versus locally scaled data on coastal processes. As a case study, we focused on an eroding cliff face in Port Resolution on Tanna Island, Vanuatu, which is of concern to the local community and government authorities. The coastal process modeling revealed that the global wave data generated unrealistically high predictions of wave height within Port Resolution Bay. Expensive engineering adaptations designed to provide coastal protection were therefore likely to fail in preventing ongoing cliff erosion. In this case, the best adaptation solution involves changing land use to revegetate and help stabilize the cliff top. Our case study highlights the importance of accurate hazard assessment, especially in data-poor regions where the extrapolation of global datasets and models in the absence of local data can result in poor adaptation decision-making. Furthermore, the multidisciplinary approach applied here can be applied in other data-poor regions to strengthen analyses exploring the benefits of local adaptation interventions.

Coastal Flooding in the Maldives Induced by Mean Sea-Level Rise and Wind-Waves: From Global to Local Coastal Modelling

The Maldives, with one of the lowest average land elevations above present-day mean sea level, is among the world regions that will be the most impacted by mean sea-level rise and marine extreme events induced by climate change. Yet, the lack of regional and local information on marine drivers is a major drawback that coastal decision-makers face to anticipate the impacts of climate change along the Maldivian coastlines. In this study we focus on wind-waves, the main driver of extremes causing coastal flooding in the region. We dynamically downscale large-scale fields from global wave models, providing a valuable source of climate information along the coastlines with spatial resolution down to 500 m. This dataset serves to characterise the wave climate around the Maldives, with applications in regional development and land reclamation, and is also an essential input for local flood hazard modelling. We illustrate this with a case study of HA Hoarafushi, an atoll island where local topo-bathymetry is available. This island is exposed to the highest incoming waves in the archipelago and recently saw development of an airport island on its reef via land reclamation. Regional waves are propagated toward the shoreline using a phase-resolving model and coastal inundation is simulated under different mean sea-level rise conditions of up to 1 m above present-day mean sea level. The results are represented as risk maps with different hazard levels gathering inundation depth and speed, providing a clear evidence of the impacts of the sea level rise combined with extreme wave events.

Use of remote sensing results in the tasks of navigational and hydrographic situation monitoring

The paper presents the possibilities of using modern online sources of satellite information in the tasks of monitoring the ecological and navigational-hydrographic situation and building on their basis methods and information technologies to reflect the state of marine waters and operational forecasting of changes in this state. For the tasks of monitoring the ecological and navigational-hydrographic situation, one of the most convenient and informative is the marine environment monitoring service COPERNICUS (CMEMS). This service collects and presents data on observations of spatio-temporal variability of sea water temperature and salinity values, streams parameters, etc. by using specialized artificial satellites Sentinel-1 and Sentinel-2, which intended for use in the mission of a dual satellite with high viewing frequency and high resolution. CMEMS provides, on a regular and systematic basis, information on the physical condition, variability and dynamics of the oceanic and marine ecosystems. The principle of measurement uses natural microwave emissions on the sea surface, which vary depending on the degree of roughness of the sea surface. You can get the parameters of wind direction, atmospheric water vapor, rain speed, sea ice (age, concentration and limit), the length of the snow cover and the water content in the snow. High-resolution ice mapping services provide ice classification and floating ice data to navies and shipping companies to ensure safe year-round shipping. The ability of the Sentinel-1 to conduct observations in any weather and during the day or night makes it ideal for accurately determining the location and movement of the vessel at sea. Oil detection applications are used to gather evidence of illegal discharges, analyze the spread of oil spills and search for oil reserves by detecting natural infiltration. Sentinel-1 marine products, in combination with global sea wave models, help determine the direction, wavelength and height of waves on the open sea, as well as help predict the weather, the movement of ships and the use of wave energy. In addition, Sentinel-1 can provide data on the interaction of ocean waves and streams, which allows you to visualize large-scale ocean streams, cold/warm water massifs, coastal streams and internal waves. Software and hardware complexes and information-analytical systems created with the use of these methods and technologies can significantly increase the efficiency and effectiveness of solving problems of environmental monitoring, navigation and hydrographic support of navigation, search and rescue operations in marine waters.

Seasonal variation in longshore sediment transport rate and its impact on sediment budget along the wave-dominated Karnataka coast, India

The Karnataka coast is subjected to high wave activity during the southwest monsoon when most of the sandy beaches undergo erosion. Based on the littoral cell concept, the Karnataka coast is broadly divided into 14 major littoral cells and 26 stations are selected in the present study. WaveWatch III global wave model data at 0.5° interval were used to derive the nearshore wave characteristics from XBeach numerical model. The model results were validated with the measured wave rider buoy data of the Indian National Centre for Ocean Information Services. The beach orientation, nearshore slope, median sediment size, significant wave height, mean wave direction, and the peak wave period were used in the estimation of longshore sediment transport rate. The mean significant wave height along the Karnataka coast was about 0.86 m, wave direction was about 210° and peak wave period was about 13 sec. The wave height during southwest monsoon (June–September) was higher, post-monsoon (October–December) was moderate and pre-monsoon (January–May) was the calmest period. Direction of longshore sediment transport was southwards during pre- and post-monsoons when waves were from the south–southwest. Whereas, northwards during monsoon when the wave approach from west–southwest to west. The annual net longshore sediment transport rate estimated was about 0.65×106 m3 towards the south and the sediment budget investigation depicts the loss of 0.067×106 m3 during the study period.

SWRL Net: A Spectral, Residual Deep Learning Model for Improving Short-Term Wave Forecasts

AbstractSkillful nearshore wave forecasts are critical for providing timely alerts of hazardous wave events that impact navigation or recreational beach use. While typical forecasts provide bulk wave parameters (wave height and period), spectral details are needed to correctly predict wave and associated circulation dynamics in the nearshore region. Currently, global wave models, such as WAVEWATCH III (WW3), make spectral predictions, but do not assimilate regional buoy observations. Here, Spectral Wave Residual Learning Network (SWRL Net), a fully convolutional neural network, is trained to take recent WW3 forecasts and buoy observations, and produce corrections to frequency-directional WW3 spectra, transformed into directional buoy moments, for up to 24 h in the future. SWRL Net is trained with 10 years of collocated NOAA’s WW3 CFSR reanalysis predictions and buoy observations at three locations offshore of the U.S. western coast. At buoy locations SWRL Net residual corrections result in wave height root-mean-square error (RMSE) reductions of 23%–50% in the first 6 h and 10%–20% thereafter. Sea frequencies (5–10 s) show the most improvement compared to swell (12–20 s). SWRL Net reduces mean direction RMSE by 28%–54% and mean period RMSE by 20%–56% over 24 forecast hours. While each model is trained and tested at independent locations, SWRL Net exhibits generalization when introduced to data from other locations, suggesting future development may be composed of training sets from multiple locations.

Southern African Wave Model Sensitivities and Accuracies

Numerous studies have identified the complexities of the wave climatology around the South African coast, but limited studies have investigated these complex dynamics in the available literature. Several freely available parameterized wave boundary conditions are produced around southern Africa. However, none of these are fully spectral outputs from global or larger regional spectral wave models. This constraint results in local engineering and scientific organizations, reconstructing their own spectral boundary conditions. For coastal models, this is a reasonable assumption, assuming that the single parameterization is accurate and a representation of a non-multimodal sea state. The South African Weather Service (SAWS) Marine unit recently launched their coupled, operational wave and storm surge forecasting model. The aim of the SAWS Wave and Storm Surge (SWaSS) platform was to provide accurate, high-resolution coastal forecasts for the entire South African coastline. The present investigation thus presents the validation of the spectral wave component of the coupled system, developed in Delft3D. Various wave boundary reconstructions are investigated together with the two most used and well-known whitecapping formulations. Validation is performed with both in situ wave-rider buoy data (at nine locations along the coastline) and regional remotely sensed, along track, altimetry data. Full model performance statistics are provided, and the accuracy of the model is discussed.