Wave Forecasting Research Articles

How does the selection of wave hindcast datasets and statistical models influence the probabilistic design of offshore scour protections?

Offshore wind structures often use bottom-fixed foundations that are susceptible to scour phenomena. The design of scour protections for such structures requires defining the physical actions that they must be able to withstand during their lifetime. In this context, hindcasts providing estimates of necessary metocean variables play a key role. However, it is unclear how the selection of hindcast datasets can influence the design of scour protections at a given location. Moreover, defining design actions requires estimating extreme sea states with return periods exceeding the length of hindcast time series, which introduces additional uncertainty in the modelling process, as different statistical models may perform differently when applied to different hindcast datasets. A better understanding of the effects of these modelling choices is therefore necessary for the design and optimization of scour protections. The present study compares different wave hindcasts for the North Sea, and analyses how their selection and the subsequent application of statistical models affect the estimated probabilities of failure of the armour layer for bottom-fixed monopile foundations. Results show that the selection of different hindcast datasets and statistical models can significantly influence the probabilistic-based design of this type of scour protection, based on which practical recommendations are provided.

Nearshore Wave Energy Resource Assessment for Off-Grid Islands: A Case Study in Cuyo Island, Palawan, Philippines

Electrifying off-grid and isolated islands in the Philippines remains one of the challenges that hinders community development, and one of the solutions seen to ensure energy security, energy access and promote a low-carbon future is the use of renewable energy sources. This study determines the nearshore wave energy resource during monsoon seasons in Cuyo Island using a 40-year wave hindcast and 8-year on-site wind speed data as inputs to develop a high-resolution wave energy model using SWAN and assesses its annual energy production through matching with wave energy devices. The results show that the average significant wave height (Hs), peak period (Tp) and wave power density (Pd) during a northeast monsoon are Hs = 1.35 m, Tp = 4.79 s and Pd = 4.05 kW/m, respectively, while a southwest monsoon, which is sheltered by the mainland, results in Hs = 0.52 m, Tp = 3.37 s and Pd = 0.34 kW/m. While the simulated model was observed to overestimate the significant wave height (bias = 0.398, RMSE = 0.54 and SI = 1.34), it has a strong relationship with the “observed values” (average r = 0.9). The annual energy production for Wave Dragon, Archimedes Wave Swing and Seawave Slot-Cone Generator are highest at 1970.6 MWh, 2462.04 MWh, 62.424 MWh and 4099.23 MWh, respectively.

Application of Delft3D-WAVE in the investigation of the typhoon-induced high wave in Central Vietnam

With a long coastline of around 3260 km, tropical typhoons induce the deadliest disasters in Central Vietnam’s highly populated coastal area. The focus of this paper is two-fold. The first objective is to investigate the feasibility of utilizing Delft3D-WAVE in forecasting typhoon-induced high wave events in Central Vietnam. By running a WRF model simultaneously in multiple domains with different grid resolutions, wind field distribution data during typhoon events were reproduced. Using the generated wind field data, typhoon Kaemi (2000) was employed as the case study to calibrate and validate the wave models. Comparisons between simulated results and observed data showed a good agreement, especially during peak wave height. The combined wind-wave model was applied to estimate the temporal and spatial impacts of high waves caused by the typhoon Ketsana (2009). The second objective is to assess the potetial risk from typhoon-induced wave in Central Vietnam based on the models output. From these results, this study recommends the application of Delft3D-WAVE to investigate high waves induced by typhoon events in Vietnam’s coastal areas.

Modelling and simulation of extreme wave heights around agatti island of lakshadweep, west coast of India

Small islands experience variation in wave energy regime along shorter shoreline due to refraction, diffraction and variability in direction of wave travel. Nearshore wave climate around these islands is quite complex due to wave transformation associated with configuration of islands, and high steepness in the bathymetry. Understanding wave energy will help in identifying suitable locations for coastal structures, wave energy devices and sediment budgeting. Assessment of extreme wave climate around the islands play a crucial role in the design and survival of coastal infrastructure for any developmental activities in islands. This study investigates the extreme wave climate around the Agatti island of Union Territory of Lakshadweep located in west coast of India. The spatial variability of wave power distribution around the Agatti island is analyzed based on 10 years of wave hindcast data from spectral wave modelling for the period between 2011 and 2020 using spectral wave model. In this study, third-generation spectral wave model was used to simulate the wave climate over the Arabian Sea. Regional model was established using ECMWF model winds and the offshore wave parameters were validated with deep water ocean observations AD07 and AD09 deployed in the Arabian Sea. To understand the nearshore wave transformation along the Agatti island, local wave model of fine resolution was established using high resolution bathymetry and is validated using the coastal buoy (CB02) deployed near Agatti. Nearshore wave parameters such as Significant wave height, Peak wave period (Tp) and Mean wave direction (θm) were analyzed. The wave power distribution around the island was quantified and demarcated for siting of offshore structures for developmental activities. The study reveals that the south zone of the island is more energetic with annual mean wave power of 8–12 kW/m and during monsoon, wave power more than 28 kW/m is observed. The extreme value theory based on Generalized Extreme Value distribution is applied to the extreme wave height estimate based on modelled wave data around the Agatti island. The extreme wave heights for different return periods were estimated around the Agatti island for design and analysis of coastal infrastructure.

The impact of wind–wave coupling with WBLM on coastal storm simulations

A wave boundary layer model (WBLM) that is applied as a wind-input source function for the ocean surface wave model SWAN is further used in a wind–wave coupling system for the wind stress calculation in the atmospheric model WRF. The coupling system is used for the investigation of the modeling of coastal wind, wave, and wind stress during storms in the North Sea. In comparison with measurements and other studies in the literature, the modeled magnitude and spatial distribution of the drag coefficient and roughness length by WBLM outperforms the other four popular parameterization and physics-based methods. In addition, the WBLM coupling method produces a spatial variation of the surface wind speed in shallow waters affected by the bathymetry. Furthermore, the wind and waves simulated by the WBLM coupling method during 321 storms in the North Sea are evaluated with measurements. Results show that WBLM coupling method is reliable for the simulation of wind, wave, and wind stress in shallow waters during storms.

Evaluation of ship operational effect based on long-term encountered sea states using wave hindcast combined with storm avoidance model

Ships avoid severe sea states for the safety of lives and property and to save energy. Therefore, the actual sea states encountered by ships differ from nature and are affected by human operational effects that we refer to as the “ship operational effect”. The evaluation of the ship operational effect is crucial for the rational design of structural strength of the hull. In this study, we derived the sea states encountered over 25 years in the North Atlantic, by considering storm avoidance by merchant ships. We then performed long-term predictions of the ship motions and wave loads of 75 merchant ships. Existing pioneering studies used automatic identification system (AIS)-based ship locations and timestamps to subsample wave hindcast data along ship tracks for a limited duration. Unlike existing studies, we used a statistical storm avoidance model of merchant ships to reconstruct the sea states, encountered over 25 years, in the North Atlantic. Owing to the ship operational effect, the long-term prediction values of roll motion, pitch motion, vertical bending moment amidships, and hydrodynamic pressure on bottom centerline amidships of merchant ships, at the probability level 10−8, were reduced by approximately 10–20%, and the coefficient of the ship operational effect was 0.8–0.9. Furthermore, we evaluated the uncertainties in the ship operational effect. The values deviated by as much as 6%, depending on the choice of wave hindcast products (TodaiWW3-NK, IOWAGA, and ERA5). The variation based on the parameter settings of the storm avoidance model (severity of the storm, radius of influence of the storm, and number of ship locations) was up to approximately 1 m at a probability level of 10−5 of the encountered sea states for 25 years.

Study on the Wind and Wave Environmental Conditions of the Xisha Islands in the South China Sea

Wind and waves are the main factors of environmental loading on ships and offshore structures. Thus, detailed understanding of wind and wave conditions can improve the design and maintenance of these structures. This paper developed a validated long-term wind and wave hindcast database covering the recent 32 years from 1988 to 2019. The spatial distribution of wind and wave characteristics for the whole Xisha Islands’ domain were analyzed. Frequency and directional distributions of wind speeds and significant wave heights were investigated at several locations around typical islands. Extreme value models were used to estimate the wind speed for 100-year return levels, whereas environmental contour approaches were utilized to establish the extreme sea-state parameters for 50- and 100-year return periods. It was found that the Weibull distribution was better fitted to the significant wave heights of the Xuande Atoll’s sites in the open sea, while the exponential Weibull distribution provided a better fit at the Yongle Atoll’s sites where waves are sheltered.

A Deep Learning Model for Improved Wind and Consequent Wave Forecasts

Abstract The paper presents a combined numerical–deep learning (DL) approach for improving wind and wave forecasting. First, a DL model is trained to improve wind velocity forecasts by using past reanalysis data. The improved wind forecasts are used as forcing in a numerical wave forecasting model. This novel approach, used to combine physics-based and data-driven models, was tested over the Mediterranean. The correction to the wind forecast resulted in ∼10% RMSE improvement in both wind velocity and wave height over reanalysis data. This significant improvement is even more substantial at the Aegean Sea when Etesian winds are dominant, improving wave height forecasts by over 35%. The additional computational costs of the DL model are negligible compared to the costs of either the atmospheric or wave numerical model by itself. This work has the potential to greatly improve the wind and wave forecasting models used nowadays by tailoring models to localized seasonal conditions, at negligible additional computational costs. Significance Statement Wind and wave forecasting models solve a set of complicated physical equations. Improving forecasting accuracy is usually achieved by using a higher-resolution, empirical coefficients calibration or better physical formulations. However, measurements are rarely used directly to achieve better forecasts, as their assimilation can prove difficult. The presented work bridges this gap by using a data-driven deep learning model to improve wind forecasting accuracy, and the resulting wave forecasting. Testing over the Mediterranean Sea resulted in ∼10% RMSE improvement. Inspecting the Aegean Sea when the Etesian wind is dominant shows an outstanding 35% improvement. This approach has the potential to improve the operational atmospheric and wave forecasting models used nowadays by tailoring models to localized seasonal conditions, at negligible computational costs.

A WAVEWATCH III® model approach to investigating ocean wave source terms for West Africa: Non-linear wave-wave interaction source terms

• Investigation of ocean wave model parameterization schemes for West Africa. • Selection of appropriate non-linear source terms for ocean wave projections. • DIA term best estimates wave heights for sub-sections of West Africa. • Application of no non-linear term best estimates wave heights for the entire region. Ocean wave forecasting for West Africa has mostly relied on global parameterization schemes and data outputs. Investigating non-linear ocean wave-wave interaction source terms ( S nl ) for the region, that forms part of the wave model parameterization schemes, is a contribution of this study towards developing a regional wave modelling scheme for West Africa. The study evaluates five non-linear source term configurations with the WAVEWATCH III® (WW3) numerical ocean wave model version 5.16; (i) No source term applied - NL0, (ii) Discrete Interaction Approximation (DIA) – NL1, (iii) Exact Interaction Approximation – NL2, (iv) Generalized Multiple DIA – NL3, and (v) Two-Scale Approximation – NL4, in order to determine best performing S nl for projecting significant wave heights and directions for the region. The wave simulations were run on three separate grids comprising a low resolution West Africa grid (latitudes 10 o S – 30 o N; longitudes 35 o W – 15 o E), high resolution south-eastern (latitudes 2 o S – 8 o N; longitudes 10 o W – 10 o E) and north-western (latitudes 10 o N – 25 o N; longitudes 30 o W – 10 o W) sub-grids. Simulations for the entire West Africa grid produced higher accuracies for the wave parameters compared to the higher resolution sub-grids. NL0 best estimates significant wave height whereas NL3 best estimates wave directions for the West Africa grid. For combined stations of the south-eastern sub-grid, while NL1 best estimates wave heights, NL3 best estimates the wave directions with possible alternation with NL1 due to insignificant differences. Similarly, for the north-western sub-grid, while NL1 best estimates significant wave heights, NL0 best estimates wave directions with similar ability to alternate with NL2 in projecting wave directions. Generally, insignificant differences exist between the source terms in projecting wave directions, especially for the north-western sub-region, implying that any of the source terms may be used in projecting wave directions without significantly compromising on accuracy. Thus, NL1 which best estimates significant wave heights for both south-eastern and north-western sub-regions can also be used in estimating the wave directions. Similarly, NL0 would be appropriate for the entire West Africa region.

Dynamical Mechanism of the Summer Circulation Trend Pattern and Surface High Temperature Anomalies over the Russian Far East

Abstract This study investigates the mechanism behind the recent boreal summer circulation trend pattern and associated high surface temperature anomalies over the Russian Far East. This circulation pattern includes a prominent anticyclone over the Kamchatka Peninsula where heat extremes have been trending upward. Observational analysis and numerical model simulations indicate that latent heating anomalies centered over Yakutia, west of Kamchatka Peninsula, can excite this anticyclone and the downstream circulation trend pattern. However, this anticyclone alone is insufficient for generating the anomalously high temperature over the region. Instead, the high temperature emerges when there is an upstream precursor that resembles the Eurasian circulation trend pattern. Warm advection by this upstream circulation initiates a positive temperature anomaly over the Russian Far East, one week prior to the onset of the anticyclone in this region. As this anticyclone develops, the temperature anomalies further intensify by adiabatic warming and shortwave radiative heating. If upstream circulation anomalies are opposite to those of the Eurasian trend pattern, the initial temperature over the Russian Far East is anomalously negative. As a result, the adiabatic warming and shortwave radiative heating within this anticyclonic region are unable to bring the temperature to an extreme condition. These findings indicate that the temperature extremes over the Russian Far East are contributed by a combination of remote and local circulation forcings and provide insights into subseasonal forecasts of heat waves over this region.

NUMERICAL DISTRIBUTION SIMULATION OF TYPHOONS’ WAVE ENERGY IN THE TAIWAN STRAIT AND ITS ADJACENT WATERS

As new energy technologies boom in recent years, marine renewable energy, especially wave power is one potential trend. However, few relevant studies focus on extreme sea conditions. In this paper, a numerical model of typhoon waves in the Taiwan Strait is established based on the third-generation ocean wave model SWAN and then calculated by the wave energy empirical equation. Typhoon No. 200808 Fung-wong, strong typhoon No. 200815 Jangmi and strong typhoon No. 201808 Maria are used for verification and analysis. Finally, the results show that most concentrated wave energy values are more than 300 kW/m for typhoon and more than 900 kW/m for strong typhoons, over 60 times and 180 times the annual average (5 kW/m) in the Chinese sea area, respectively. In terms of other locations, corresponding values are more than 50 kW/m and over 100 kW/m. Therefore, typhoons’ wave energy is certainly a huge asset if fully utilized.

Phase-resolved ocean wave forecast with simultaneous current estimation through data assimilation

In Wang & Pan (J. Fluid Mech., vol. 918, 2021, A19), the authors developed the first ensemble-based data assimilation (DA) capability for the reconstruction and forecast of ocean surface waves, namely the EnKF-HOS method coupling an ensemble Kalman filter (EnKF) and the high-order spectral (HOS) method. In this work, we continue to enrich the method by allowing it to simultaneously estimate the ocean current field, which is in general not known a priori and can (slowly) vary in both space and time. To achieve this goal, we incorporate the effect of ocean current (as unknown parameters) on waves to build the HOS-C method as the forward prediction model, and obtain a simultaneous estimation of (current) parameters and (wave) states via an iterative EnKF (IEnKF) method that is necessary to handle the complexity in this DA problem. The new algorithm, named the IEnKF-HOS-C method, is first tested in synthetic problems with various forms (steady/unsteady, uniform/non-uniform) of current. It is shown that the IEnKF-HOS-C method is able to not only estimate the current field accurately, but also boost the prediction accuracy of the wave field (even) relative to the state-of-the-art EnKF-HOS method. Finally, using real data from a shipborne radar, we show that the IEnKF-HOS-C method successfully recovers the current speed that matches the in situ measurement by a floating buoy.

Spatial-temporal wave height forecast using deep learning and public reanalysis dataset

A novel data-driven model is proposed for efficient spatial–temporal significant wave height forecast in West Pacific using deep learning. A multivariate 3-layer LSTM-based architecture following a matrix-to-matrix mapping fashion is applied to establish the underlying spatial–temporal relationship between wind components and significant wave height field for long-term and ocean-scale forecasts. A parallel modeling strategy is employed to reduce the computational expense in which the forecasting domain is flexibly partitioned into finite windows and trained independently as a sub-model. A 30-year hindcast wave dataset released by EMC (Environmental Modeling Center) Operational Wave Models associated with NCEP Climate Forecast System Reanalysis and Reforecast (CFSRR) wind data is used to drive and test the model. A rectangle sea region covering over 9.33 × 106 km2 in West Pacific is regarded as a modeling domain. The data-driven model is validated against a 12-year period historical hindcast data, and global errors are carefully discussed. The forecasting performance of the established model on tropical cyclone wave events is investigated in terms of both spatial pattern and temporal variance of significant wave height. The extreme significant wave height, rough sea occurrence, as well as seasonal and annual characteristics of significant wave height in the West Pacific are carried out using the proposed model and compared with the original hindcast results. The data-driven model shows promising potential to accurately capture the ambiguous patterns and features that are variant in both spatial and temporal dimensions and has obvious superiority over numerical wave modeling in terms of computational efficiency.

Wave energy assessment in the China East Adjacent Seas based on a 25-year wave-current interaction numerical simulation

This study evaluates the wave energy in the China East Adjacent Seas based on a 25-year high temporal and spatial resolution wave-current interaction wave hindcast dataset from 1996 to 2020. The spatial distribution characteristics of the multi-year and monthly average wave energy flux in the China East Adjacent Seas are analyzed. The temporal variability characteristics of wave energy flux are quantified and analyzed by using a coefficient of variation, a seasonal variability index and a monthly variability index. The exploitable wave energy flux was assessed by excluding the waves with energy flux less than 2 kW/m and setting different significant wave height upper thresholds. Then, after considering factors such as wave energy resource abundance, temporal variability, offshore distance and water depth, 12 hotspots in the China East Adjacent Seas are believed relatively suitable for wave energy development. Four types of wave energy converters with different power capacities are selected to analyze their performance in different hotspots to provide a basis for design and selection of wave energy converters suitable to the China East Adjacent Seas. Due to the higher conversion efficiency when the significant wave height is small, Oyster performs best among the four selected wave energy converters.

Systematization of short-term forecasts of regional wave heights using a machine learning technique and long-term wave hindcast

Machine-learning techniques have been applied to wave forecasting to address the implementational and computational challenges of conventional numerical wave models. Most previous studies emphasized the accuracy of approaches based on in situ wave measurements, but such approaches are site-specific and lack the spatial and temporal fidelity required by regional wave climate forecasts. This study presents a novel systematization of forecasting regional wave heights at sufficient spatial resolution and coverage, using long-term wave hindcasts generated by numerical wave models as a training dataset for a long short-term memory (LSTM). This systematization not only ensures the accuracy of the model's forecasts but demonstrates the applicability of long-term wave hindcasts as input data. It involves thoroughly investigating the dependence of model accuracy on multi-task LSTM structures (training variables, input data record periods, and forecast lead times), and on regional wave climates (different wave sites and sea states). The resulting model performance is spatially heterogeneous in global wave forecasts, underlining the need for validation of wave forecasting models to global wave climates. This study reveals that machine-learning techniques that leverage validated long-term wave hindcasts can successfully forecast regional wave climates. As access to high-resolution regional wave hindcast data improves, accuracy will also increase.

Application of Empirical Orthogonal Function Analysis to 1 km Ensemble Simulations and Himawari–8 Observation in the Intensification Phase of Typhoon Hagibis (2019)

An empirical orthogonal function (EOF) analysis was performed for the inner core of Typhoon Hagibis (2019) in the intensification phase. The Himawari–8 geostationary infrared (IR) brightness temperature (BT) collocated at the Hagibis’s center was combined with the IR BT simulated by a radiative transfer model, with 1 km ensemble simulations conducted by an atmosphere model and the coupled atmosphere–wave–ocean model. The ensemble simulations were conducted under one control atmospheric initial condition and the 26 perturbed ones with two different oceanic initial conditions. The first four EOF modes showed symmetric and asymmetric patterns such as a curved band, cloud dense overcast, and eye pattern used in the classification of the Dvorak technique. The influence of ocean coupling on the modes appeared only in the early intensification phase but was relatively small compared to the difference from the Himawari–8 observations. While ocean coupling and different oceanic initial condition quantitatively affected the IR BT, the normalized amplitude for the first EOF mode did not become close to that of the Himawari–8 observation in the late intensification phase. The intensification rate in the late intensification phase was inconsistent between the simulation results and the estimate from the Himawari–8 normalized amplitude by multiple linear regression analysis.

Directional correction of ERA5 and CAWCR wave spectrum partitions in the Southeast Brazil

The use of satellite data to calibrate ocean wave spectrum partitions are being carried out in recent wave hindcast datasets as a way of correcting historical sea state records. The directional correction method aims at establishing separate fitting coefficients, for windsea and swell partition, as well as for the different sectors from which the waves propagate. This technique is able to identify systematic errors reproduced by the wave models using altimeter data. This work calibrates the significant wave height Hs from wave spectrum partitions in the South Brazil’s coast using a directional multiple linear regression, previously introduced and tested in the New Zealand’s coast. The ERA5 and CAWCR hindcasts are considered and corrected based on the Globwave satellite data. In general, at the boundaries of our study area, both raw wave hindcasts underestimate significant wave height Hs regarding altimeter data, thus requiring correction coefficients greater than one. The scatter index analysis exhibits an improvement in the indices post calibration, as well as shows higher values nearshore with respect to offshore points for both databases before and after calibration. While ERA5 correction slightly overestimates, CAWCR correction underestimates to some extent, with respect to satellite data. The corrections were also further validated using buoy data over the continental shelf. Even in a shallow sea location, where the Rio Grande buoy is, the bulk wave parameter Hs provided by CAWCR shows improvements post calibration. Comparisons pre and post calibration per direction present better results for CAWCR than ERA5. All sectors analysed for the CAWCR hindcast display improvements after of the correction. The CAWCR correction provided good results and was used as boundary conditions for a higher-resolution regional hindcast.

Wind-wave forecasting in enclosed basins using statistically downscaled global wind forcing

Accurate wind-wave forecasting in enclosed and semi-enclosed basins is a challenging task, demanding primarily for high-resolution wind forcing at regional scale. This is generally obtained with dynamical downscaling from a low-to-mid resolution atmospheric model. In this context, a new wave forecasting system for the marginal Adriatic Sea is herein presented aimed at proposing an alternative strategy for accurate wind-wave forecasting in (semi-) enclosed basins that does not require an ad-hoc regional atmospheric model. The system is based on the state-of-the-art WAVEWATCH III® spectral wave model forced by the global IFS-ECMWF forecast. At first, wind speed is quantile-corrected to account for the systematic underestimation over the Adriatic Sea. Then, the significant wave height in the target region and for regimes associated with marine storms is calibrated following standard procedure. Wind and wave observations from different sources are used for calibration and validation of the wave forecasts, which achieve satisfactory scores. We also compare results with those of other forecasting systems in the area, highlighting the importance of the wind forcing accuracy and the wave model calibration. Doing so, we discuss the challenges that characterise (semi-) enclosed environments in order to propose effective solutions for them and future developments.

Ocean Surface Roughness from Satellite Observations and Spectrum Modeling of Wind Waves

Abstract Many wind wave spectrum models provide excellent wave height prediction given the input of wind speed and wave age. Their quantification of the surface roughness, on the other hand, varies considerably. The ocean surface roughness is generally represented by the mean square slope, and its direct measurement in open ocean remains a challenging task. Microwave remote sensing from space delivers ocean surface roughness information. Satellite platforms offer global coverage in a broad range of environmental conditions. This paper presents low-pass mean square slope (LPMSS) data obtained by spaceborne microwave altimeters and reflectometers operating at L, Ku, and Ka bands (about 1.6, 14, and 36 GHz). The LPMSS data represent the spectrally integrated ocean surface roughness with 11, 95, and 250 rad m−1 upper cutoff wavenumbers, and the maximum wind speeds are 80, 29, and 25 m s−1, respectively. A better understanding of the ocean surface roughness is important to the goal of improving wind wave spectrum modeling. The analysis presented in this paper shows that over two orders of magnitude of the wavenumber range (0.3–30 rad m−1), the spectral components follow a power function relating the dimensionless spectrum and the ratio between wind friction velocity and wave phase speed. The power function exponent is about 0.38, which is considerably smaller than unity as expected from the classical equilibrium spectrum function. It may suggest that wave breaking is not only an energy sink but also a source of roughness generation covering a wideband of wavelengths about 20 m and shorter. Significance Statement This paper presents low-pass mean square slope (LPMSS) data obtained by spaceborne microwave altimeters and reflectometers operating at L, Ku, and Ka bands (about 1.6, 14, and 36 GHz). The LPMSS data represent the spectrally integrated ocean surface roughness with 11, 95, and 250 rad m−1 upper cutoff wavenumbers, and the maximum wind speeds are 80, 29, and 25 m s−1, respectively. A better understanding of the ocean surface roughness is important to the goal of improving wind wave spectrum modeling that is critical to the investigation of air–sea interaction and ocean remote sensing. The analysis presented in this paper suggests that wave breaking is not only an energy sink but also a generation source of surface roughness covering a wide band of wavelengths about 20 m and shorter.

A deep water and nearshore wave height calibration of the ECOWAVES hindcasting database

Significant wave height (SWH) in shallow waters is assessed by generating two wave hindcasts; the first uses ERA-Interim wind fields and the second one from ERA5 to quantify the improvement of the ERA5 surface winds on the SWH representativeness, both in deep and shallow waters along the Chilean coastline. Additionally, wind field predictions from the Global Forecast System (GFS) were used to assess the representativeness of shallow waters. Oceanographic buoys were used to validate SWH in deep waters, while Acoustic Doppler Current Profiler (ADCPs) was equipped to measure waves in shallow waters. Energy spectrums coupling Wavewatch III and Simulating Waves Nearshore (SWAN) models were transferred to evaluate the performance of shallow water simulations. In general, the SWH from both wave hindcasts showed good performance. Nonetheless, those forced by ERA5 presented a better qualitative comparison of sea state temporal variability, which increased the correlation coefficients (>0.9), coefficients of determination (>0.8), and minor errors (RMSE, MAE, and BIAS) compared to oceanographic buoys and ADCPs. Additionally, in simulations forced by GFS, the temporal variability of the waves in shallow waters was successfully reproduced. Nevertheless, an increase in the RMSE, MAE, and BIAS error was statistically verified compared to ERA-Interim and ERA 5.