Wave Model Data Research Articles

Statistical downscaling of coastal directional wave spectra using deep learning

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

Extraction of ocean wave parameters from video images

ABSTRACT In this study, we proposed a method for estimating windsea parameters from the images captured by the surveillance camera installed in coastal areas for practical use. Camera images were acquired in the observation room of the Tokyo Bay Marine Traffic Center at Cape Kannon in Tokyo Bay, Japan for approximately 4 months. Video images of the sea surface were analyzed using in-situ wave observations and wave model data. The brightness spectrum in the frequency domain and the brightness period were defined and introduced from the time series of brightness values in the obtained video images. The novelty of this study lies in the method proposed for estimating the wave parameters using the brightness period. Although this is a simple method, we confirmed that the wave parameters for windsea, such as the significant wave height and significant wave period, can be estimated with high accuracy using the brightness period. Furthermore, we confirmed that the brightness spectrum obtained from camera images and the slope spectrum of wind waves exhibited extremely similar shapes.

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.

Periodicity of wave-driven flows and lagoon water renewal for 74 Central Pacific Ocean atolls

French Polynesia atolls are spread on a vast 2300 by 1200 km Central Pacific Ocean area exposed to spatially and temporally dependent wave forcing. They also have a wide range of closed to open morphologies and several have been suitable to develop from black-lipped pearl oysters a substantial pearl farming activity in the past 30 years, representing nowadays the 2nd source of income for French Polynesia. Considering here only the component of lagoon renewal that is driven by waves, we investigate for 74 atolls different lagoon renewal metrics using 20 years of wave model data at 0.05° spatial resolution. Wavelet spectral analyses highlight that atolls, even in close vicinity, can be exposed to different and characteristic periodicities in wave-driven flows and water renewal. These characteristics are discussed in relation to pearl farming atolls, including atolls known to be efficient oyster spat producers, a critical activity for pearl farming sustainability.

Validation of significant wave height from Jason-3 and Sentinel-3A/B and relation to tidal currents in coastal regions of the Korean Peninsula

ABSTRACT As climate change and oceanic warming have an increasing impact on coastal waves, accurate observation of satellite significant wave height (SWH) in coastal regions is becoming more important. In addition to the validation of satellite SWHs in the global ocean, the SWHs should also be validated in coastal regions for their diverse applications. In this study, the accuracy and error characteristics of altimeter SWH data, Jason-3 Geophysical Data Records Version D and Sentinel-3A/B Level-2 marine scientific products, were investigated using SWH measurements from 18 buoy stations in the coastal region of the Korean Peninsula from 2017 to 2019. Results showed that the Jason-3 and Sentinel-3A/B SWHs exhibited root mean square errors (RMSE) and bias errors of 0.59–1.60 m and 0.03–0.42 m, respectively. Cross-validation between Jason-3 and Sentinel-3A/B exhibited a consistency comparable to that of the global ocean. The spatial distribution of comparison between altimeter SWH and wave model data was similar to that obtained using altimeter SWH and in situ measurements. Validation of altimeter SWH from the buoy stations yielded the highest error in the southwestern coastal region of the Korean Peninsula, with high tidal currents in the Yellow Sea. The stronger the tidal current speed, the more amplified the satellite SWH errors. The SWH differences exhibited a high dependence on the tidal index, including the elements of tidal current speed, water depth, and tide height. A relationship was found between the tidal index and the altimeter SWH differences from the in situ measurements. Based on this relation, formulas were proposed to reduce satellite SWH errors due to tidal currents in the coastal regions of the Korean Peninsula. For the matchups with tidal index less than 2, RMSE and bias errors of each satellite sensor were improved by up to 14.25% (0.28 m) and 94.40% (0.84 m), respectively.

CryoSat-2 Significant Wave Height in Polar Oceans Derived Using a Semi-Analytical Model of Synthetic Aperture Radar 2011–2019

This paper documents the retrieval of significant ocean surface wave heights in the Arctic Ocean from CryoSat-2 data. We use a semi-analytical model for an idealised synthetic aperture satellite radar or pulse-limited radar altimeter echo power. We develop a processing methodology that specifically considers both the Synthetic Aperture and Pulse Limited modes of the radar that change close to the sea ice edge within the Arctic Ocean. All CryoSat-2 echoes to date were matched by our idealised echo revealing wave heights over the period 2011–2019. Our retrieved data were contrasted to existing processing of CryoSat-2 data and wave model data, showing the improved fidelity and accuracy of the semi-analytical echo power model and the newly developed processing methods. We contrasted our data to in situ wave buoy measurements, showing improved data retrievals in seasonal sea ice covered seas. We have shown the importance of directly considering the correct satellite mode of operation in the Arctic Ocean where SAR is the dominant operating mode. Our new data are of specific use for wave model validation close to the sea ice edge and is available at the link in the data availability statement.

Segmentation of Marine Forecast Zone in Korea by Using Wave Data: Based on K-Means Clustering Algorithm

Kim, M.-S.; Kim, D.; Eom, H.; You, S.H.; Jeong, J.-S., and Woo, S.-B., 2021. Segmentation of marine forecast zone in Korea by using wave data: Based on k-means clustering algorithm. In: Lee, J.L.; Suh, K.-S.; Lee, B.; Shin, S., and Lee, J. (eds.), Crisis and Integrated Management for Coastal and Marine Safety. Journal of Coastal Research, Special Issue No. 114, pp. 251–255. Coconut Creek (Florida), ISSN 0749-0208. The Korea Meteorological Administration (KMA) classified the forecast zone based on the administrative districts, weather characteristics, civil complaints, and location of observation points. A cluster analysis was performed using the wave observation and model data to check the validity of the current forecast zone for the wave forecast. The cluster analysis is a method used to group areas with common characteristics. In this study, a set of n objects was decomposed into k clusters (groups) and a distance-based method, k-means clustering algorithm, was used. As the data structure of the cluster analysis, the mean and standard deviation of the significant wave height and wave period during the period of 2014 to 2016 were selected. When performing the cluster analysis with wave heights and wave periods observed at ocean buoys, 11–12 cluster conditions have relatively significant internal evaluation indices suggesting the distance and variance ratio between points and points within each cluster or between distinct clusters. However, since there were few points to perform the segmentation of the marine forecast zone using only the observation data, there was a problem that clusters concentrated in a specific area were distributed. Therefore, it is necessary to perform the cluster analysis by using the KMA wave model for the segmentation of the marine forecast zone. As a result, 10 cluster condition showed the best internal evaluation indices. Although the results of the cluster analysis did not differ significantly from the current forecast zone, the segmentation was made more distinct and detailed.

Directional and Frequency Spread of Surface Ocean Waves From SWIM Measurements

AbstractThe “China France Oceanography Satellite” (CFOSAT) launched in 2018 now routinely provides directional ocean wave spectra at the global scale. It consists of analyzing the normalized radar cross‐section measured by the near‐nadir pointing Ku‐Band real‐aperture scanning radar SWIM (Surface Waves Investigation and Monitoring). The significant wave height, dominant wavelength and direction are provided as the main parameters, but here, we analyze additional parameters, namely the frequency width of the omni‐directional spectra, the directional spread of the dominant waves, and the related Benjamin‐Feir index. This latter was proposed in the literature to estimate the probability of extreme waves. We discuss the geographical distributions of these parameters, their relation with sea‐state conditions, and their similarities and differences with respect to the same parameters obtained from the MFWAM numerical wave model and buoy data. We find that the SWIM omni‐directional spectra are narrower and more peaked than the model spectra and that these differences are more obvious in the high sea‐state conditions encountered in the Southern Ocean. We find that under the intense conditions of the Southern Ocean, the SWIM directional spread at the peak is the smallest for swell, the largest for young wind seas, and takes intermediate values for mature wind seas. The directional Benjamin‐Feir index is similar for SWIM and MFWAM, but this is mainly due to compensating effects in the parameters contributing to this index. The results indicate that these shape parameters may be used in the future to better describe the wave space‐time evolution.

Joint Modelling of Wave Energy Flux and Wave Direction

In the context of wave resource assessment, the description of wave climate is usually confined to significant wave height and energy period. However, the accurate joint description of both linear and directional wave energy characteristics is essential for the proper and detailed optimization of wave energy converters. In this work, the joint probabilistic description of wave energy flux and wave direction is performed and evaluated. Parametric univariate models are implemented for the description of wave energy flux and wave direction. For wave energy flux, conventional, and mixture distributions are examined while for wave direction proven and efficient finite mixtures of von Mises distributions are used. The bivariate modelling is based on the implementation of the Johnson–Wehrly model. The examined models are applied on long-term measured wave data at three offshore locations in Greece and hindcast numerical wave model data at three locations in the western Mediterranean, the North Sea, and the North Atlantic Ocean. A global criterion that combines five individual goodness-of-fit criteria into a single expression is used to evaluate the performance of bivariate models. From the optimum bivariate model, the expected wave energy flux as function of wave direction and the distribution of wave energy flux for the mean and most probable wave directions are also obtained.

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.

An Application of NEMOVAR for Regional Wave Model Data Assimilation

The benefits of applying data assimilation to a regional wave model for the northwest European continental shelf have been assessed. The assimilation method uses a 3D variational scheme commonly used in ocean model data assimilation, NEMOVAR, coupled to a regional configuration of WAVEWATCH III deployed on a Spherical Multiple-Cell grid with rotated pole. Significant wave height forecast skill was positively impacted over lead times of up to 12 hours, in both offshore and coastal areas of the model. Assimilation experiments tested the impacts of applying both satellite altimeter and in-situ platform observations of significant wave height as the state variable. Addition of in-situ data introduced an improvement in model skill versus assimilating altimeter measurements only, but these improvements were relatively short-lived in lead time (on the order of a few hours). Extending observation coverage into the coastal zone introduced a benefit for only the first 1-2 hours of forecast lead time at coastal sites. Although impacts on significant wave height forecasts were substantial at short lead times, the relatively short ‘system memory’ of the assimilation suggests that, in the case of the northwest European shelf region, the best application of wave data assimilation is in a rapidly cycling short range forecast system used to generate nowcasts, or for a reanalysis product.

New Observations From the SWIM Radar On-Board CFOSAT: Instrument Validation and Ocean Wave Measurement Assessment

This article describes the first results obtained from the Surface Waves Investigation and Monitoring (SWIM) instrument carried by the China France Oceanography Satellite (CFOSAT), which was launched on October 29, 2018. SWIM is a Ku-band radar with a near-nadir scanning beam geometry. It was designed to measure the spectral properties of surface ocean waves. First, the good behavior of the instrument is illustrated. It is then shown that the nadir products (significant wave height, normalized radar cross section, and wind speed) exhibit an accuracy similar to standard altimeter missions, thanks to a new retracking algorithm, which compensates a lower sampling rate compared to standard altimetry missions. The off-nadir beam observations are analyzed in detail. The normalized radar cross section varies with incidence and wind speed as expected from previous studies presented in the literature. We illustrate that, in order to retrieve the wave spectra from the radar backscattering fluctuations, it is crucial to apply a speckle correction derived from the observations. Directional spectra of ocean waves and their mean parameters are then compared to wave model data at the global scale and to in situ data from a selection of case studies. The good efficiency of SWIM to provide the spectral properties of ocean waves in the wavelength range [70–500 m] is illustrated. The main limitations are discussed, and the perspectives to improve the data quality are presented.

Performance of Hindcast Wave Model Data used in UK Coastal Waters

Sifnioti, D.E.; Dolphin, T.J., and Vincent, C.E., 2020. Performance of hindcast wave model data used in UK coastal waters. In: Malvarez, G. and Navas, F. (eds.), Global Coastal Issues of 2020. Journal of Coastal Research, Special Issue No. 95, pp. 1284–1290. Coconut Creek (Florida), ISSN 0749-0208.Long-term wave data are a necessity for various marine applications, but wave observations can be temporally and spatially sparse, are often absent in the required locations and can be costly and often cannot be obtained in a short notice. The models used are the UK's Met Office's European 38-year wave hindcast “ReMap”, which has spatial resolution of 8 km and temporal step of 3 hours, and ECMWF's (European Centre for Medium-Range Weather Forecasts) “ERA5” reanalysis dataset from 1979 to present with a spatial resolution of 31 km and a temporal step of 1 hour. Both datasets include wave and wind parameters, thus providing valuable long-term data that can be used in coastal regions where wave observations are unavailable. In this paper we assess the errors in performance of the ReMap and ERA5 data by comparing these with wave observations made at two coastal sites in the southern North Sea. ReMap and ERA5 showed a strong correlation with measured waves for significant wave height (Hs) (r = 0.84 - 0.9), fair correlation for peak wave period (Tp) (r = 0.58 - 0.68) and good correlation for mean zero up-crossing wave period (Tz) (r = 0.73 – 0.79), with different error statistics per location and per dataset. Analysis of the 90th percentile shows that the ReMap and ERA5 overestimated Hs by approximately 0.5 m, and by up to 1.2 s for Tp and 0.6s for Tz. Monthly and annual means are also presented and discussed. Overall the performances of ReMap and ERA5 against the measured waves were similar. It is concluded that the ReMap and ERA5 data can be used with reasonable confidence in coastal regions where no direct measurements are available for ‘average’ conditions, but caution needs to be taken for extreme conditions as an over prediction for extremes is likely.

Long-Term Estimation of Wave Climate Variability in the Western Bay of Bengal

The ocean plays a major role in climate change, and its impact has an extreme effect on the process of coastal and other activities around the globe. The comprehensive analysis of long-term wave climate is carried out at selected locations in the western Bay of Bengal (BoB), by observing the distribution of significant wave height (SWH) using monthly averaged satellite and WAM model for 20 years (1996–2015). The data used in this study were compared with two in situ buoy observations obtained from the National Institute of Ocean Technology (NIOT), Chennai, India. The satellite and wave model (WAM) data show a good correlation with both in situ buoy observations. The probability distribution functions indicated a significant increase in the higher range of SWH during the recent decade. The analysis of 20-year SWH indicates a positive trend with significant annual and semiannual periodicities. The northwestern bay depicts higher energy in the annual cycle, whereas the southwestern bay exhibits equal contributions from annual and semiannual cycles. The study also reveals that the increasing trends in local SWH in the western BOB are modified due to the number of tropical cyclones as well as the migrating swells generated in the southern Indian Ocean.

A multi-collocation method for coastal zone observations with applications to Sentinel-3A altimeter wave height data

Abstract. In many coastal areas there is an increasing number and variety of observation data available, which are often very heterogeneous in their temporal and spatial sampling characteristics. With the advent of new systems, like the radar altimeter on board the Sentinel-3A satellite, a lot of questions arise concerning the accuracy and added value of different instruments and numerical models. Quantification of errors is a key factor for applications, like data assimilation and forecast improvement. In the past, the triple collocation method to estimate systematic and stochastic errors of measurements and numerical models was successfully applied to different data sets. This method relies on the assumption that three independent data sets provide estimates of the same quantity. In coastal areas with strong gradients even small distances between measurements can lead to larger differences and this assumption can become critical. In this study the triple collocation method is extended in different ways with the specific problems of the coast in mind. In addition to nearest-neighbour approximations considered so far, the presented method allows for use of a large variety of interpolation approaches to take spatial variations in the observed area into account. Observation and numerical model errors can therefore be estimated, even if the distance between the different data sources is too large to assume that they measure the same quantity. If the number of observations is sufficient, the method can also be used to estimate error correlations between certain data source components. As a second novelty, an estimator for the uncertainty in the derived observation errors is derived as a function of the covariance matrices of the input data and the number of available samples. In the first step, the method is assessed using synthetic observations and Monte Carlo simulations. The technique is then applied to a data set of Sentinel-3A altimeter measurements, in situ wave observations, and numerical wave model data with a focus on the North Sea. Stochastic observation errors for the significant wave height, as well as bias and calibration errors, are derived for the model and the altimeter. The analysis indicates a slight overestimation of altimeter wave heights, which become more pronounced at higher sea states. The smallest stochastic errors are found for the in situ measurements. Different observation geometries of in situ data and altimeter tracks are furthermore analysed, considering 1-D and 2-D interpolation approaches. For example, the geometry of an altimeter track passing between two in situ wave instruments is considered with model data being available at the in situ locations. It is shown that for a sufficiently large sample, the errors of all data sources, as well as the error correlations of the model, can be estimated with the new method.

Determination of Significant Wave Heights Using Damping Coefficients of Attenuated GNSS SNR Data from Static and Kinematic Observations

Currently, GNSS reflectometry based on the signal-to-noise ratio (SNR) has become an established tool in ocean remote sensing. Here, the distance between an antenna and the water surface is measured by analyzing the oscillation of the SNR observation. Due to the antenna gain pattern, this oscillation is more pronounced for satellite signals coming from low elevation angles. Additionally, the sea surface roughness is related to the attenuation of the SNR oscillation. Hence, the significant wave height (SWH) can be estimated by analyzing the SNR signal. In this work, a method is presented with which the SWH can be calculated from the attenuation’s damping coefficient of the SNR observations measured with surface-based receivers. The method’s usability is demonstrated using data from a static antenna operated in the German Bight and with data from a ship-based antenna. The estimated SWH values were validated against numerical wave model data. For both experiments, a high correlation was found.

Investigation of trends in extreme significant wave heights in the South China Sea

The focus of this study was the temporal and spatial variations of extreme significant wave heights and nearshore return-period wave heights in the South China Sea, based on the wave model and European Reanalysis-Interim data from 1979–2016. Measured buoy data were available from near Yongxing Island for comparison with the reanalysis data during the passage of typhoons. Annual maximum significant wave heights showed a statistically significant increasing trend over most of the South China Sea, whereas statistically insignificant decreasing or increasing trends were observed for seasonal maximum significant wave heights associated with the different characteristics of the typhoon events. Nonstationary generalized extreme value analysis was used to investigate the influence of nearshore extreme significant wave heights derived from the Simulating Waves Nearshore model at four offshore locations in the South China Sea. The fastest increase of 100-year return significant wave heights was found to be 0.0033 m yr−1 in the western South China Sea. The results show that, in recent decades, no significant variation trends can be found in return-period wave heights at nearshore of South China Sea, which may be related to tracks of intense typhoons in the region.

HF radar for wind waves measurements in the Malta-Sicily Channel

The CALYPSO HF radar network is a permanent and fully operational observing system currently composed of four CODAR SeaSonde stations. The system is providing real-time hourly maps of sea surface currents and waves data in the Malta-Sicily Channel. The present work aims to compare significant wave height measurements by HF Radar to wave data from numerical models and satellite altimeter. This is the first time that this set of wave data are analysed since the four HF radars were installed between 2012 and 2015. Results suggest that CODAR HF Radar wave data are a reliable source of wave information even in the case of extreme events, providing an avenue to improve and complete the offer of services deriving from the CALYPSO system. Comparisons of HF radar data with both numerical sea wave model and satellite altimeter data confirm agreement, in particular for radar measurements in the annular sectors within the central range-cells which are also characterized by a more reliable and homogeneous temporal behaviour.

Evaluation of bias correction methods for wave modeling output

Models that seek to predict environmental variables invariably demonstrate bias when compared to observations. Bias correction (BC) techniques are common in the climate and hydrological modeling communities, but have seen fewer applications to the field of wave modeling. In particular there has been no investigation as to which BC methodology performs best for wave modeling. This paper introduces and compares a subset of BC methods with the goal of clarifying a “best practice” methodology for application of BC in studies of wave-related processes. Specific focus is paid to comparing parametric vs. empirical methods as well as univariate vs. bivariate methods. The techniques are tested on global WAVEWATCH III historic and future period datasets with comparison to buoy observations at multiple locations. Both wave height and period are considered in order to investigate BC effects on inter-variable correlation. Results show that all methods perform uniformly in terms of correcting statistical moments for individual variables with the exception of a copula based method underperforming for wave period. When comparing parametric and empirical methods, no difference is found. Between bivariate and univariate methods, results show that bivariate methods greatly improve inter-variable correlations. Of the bivariate methods tested the copula based method is found to be not as effective at correcting correlation while a “shuffling” method is unable to handle changes in correlation from historic to future periods. In summary, this study demonstrates that BC methods are effective when applied to wave model data and that it is essential to employ methods that consider dependence between variables.

Ocean swell variability along the northern coast of the Gulf of Guinea

This study analyses a 4.5 year (September 2009–March 2014) time-series of remotely-sensed data of altimeter significant wave heights to describe the temporal and spatial variability of ocean swells along the northern coast of the Gulf of Guinea. The NOAA WAVEWATCH III (NWW3) wave model data were used with altimeter data to determine the origin of the swells that occur along the coast of Côte d'Ivoire in West Africa. We show that the ocean swells along the northern coast of the Gulf of Guinea are generated in the Southern Ocean and then propagate from south to north in the South Atlantic Ocean, before turning south-west to north-east close to the coast. This finding corroborates previous studies in this area. The remotely-sensed and NWW3 significant wave height data captured the strong swells observed along the coast of Côte d'Ivoire from the period 28 August–3 September 2011, which were responsible for an extreme erosion event of more than 12 m along that country's coastline. This extreme event was triggered by a strong storm in the region between 40° and 60° S that occurred eight days previously in the South Atlantic. The waves propagated as swells at a speed of about 875 km day–1 before reaching the northern African coast.