Rough Sea Surface Research Articles

Establishing and Investigating an Effective Atmosphere‐Ocean Cross‐Medium Propagation Model Based on GSM Beams

AbstractUsing the Gaussian‐Schell model (GSM) beam as the light source, this study creates a blue‐green beam atmosphere‐ocean cross‐medium propagation model (AOCPM) based on the extended Huygens‐Fresnel principle and the linear filtering method. The closed expression of the Cross Spectral Density Function (CSDF) of the GSM beam after propagation through atmospheric turbulence, atmosphere‐ocean interface, and ocean turbulence are derived. And the correctness of the model is verified by simulating the changes in intensity, relative beam width, and speckle normalized intensity autocorrelation function during the propagation of the GSM beam through the atmosphere‐ocean cross‐medium under different parameters. The findings demonstrate that the intensity of the GSM beam after being propagated through the atmosphere‐ocean medium presents a speckle‐like Gaussian distribution, the intensity of the GSM beam gradually attenuates, the relative beam width gradually grows, and the speckle normalized intensity autocorrelation function gradually decreases as the turbulence intensity and propagation distance increase. In addition, the increase in wind speed at rough sea surface mainly leads to the attenuation of intensity. The work of this paper provides a new idea for the study of atmosphere‐ocean cross‐medium propagation of blue‐green beams.

Addendum to ‘Assessment of offshore wind power including effects of sea surface roughness using observed wind statistics’: Assessment using sea surface roughness depending on spectral bandwidth of ocean waves

This addendum addresses the assessment of offshore wind power based on observed mean wind speed statistics from 11 sites in the North Sea and the North Atlantic. The wind gust and the ambient turbulence level are also estimated by adopting a wind gust spectrum. The sea surface roughness is specified using a recently published sea surface drag coefficient given in terms of the spectral bandwidth of ocean waves. The results are supplementary to those in a recent paper published by the present authors using the same mean wind speed statistics and wind gust spectrum, but with a different sea surface roughness formula depending on significant wave height and spectral wave steepness. It is demonstrated how the spectral bandwidth affects the assessment of the offshore wind power and the ambient turbulence level. Thus, since the spectral bandwidth is a frequently used parameter to characterize wave conditions the results should be useful.

A multi‐tagged SAR ocean image dataset identifying atmospheric boundary layer structure in winter tradewind conditions

AbstractA dataset of multi‐tagged sea surface roughness synthetic aperture radar (SAR) satellite images was established near Barbados from January to June 2016 to 2019. It is an advancement of the Sentinel‐1 Wave Mode TenGeoP‐SARwv (a labelled SAR imagery dataset of 10 geophysical phenomena from Sentinel‐1 wave mode) dataset that targets SAR marine atmospheric boundary layer (MABL) coherent structures. Twelve tags define roll vortices, convective cells, mixed rolls and convective cells, fronts, rain cells, cold pools and low winds. Examples are provided for each signature. The final dataset is comprised of 2100 Sentinel‐1 wave mode SAR images acquired at 36 incidence angle over an 8° × 8°region centered at 51° W, 15° N. Each image is tagged with one or multiple phenomena by five experts. This strategy extends the TenGeoP‐SARwv by identifying coexisting phenomena within a single SAR image and by the addition of mixed roll/cell states and cold pools. The dataset includes PNG‐formatted SAR image files along with two text files containing the file name, the central latitude/longitude, expert tags for each image, and all dataset metadata. There is a high degree of consensus among expert tags. The dataset complements existing hand‐labelled ocean SAR image datasets and offers the potential for new deep‐learning SAR image classification model developments. Future use is also expected to yield new insights into the tradewind MABL processes such as structure transitions and their relation to the stratification.

Simulation of time-invariant three-dimensional Freak waves and their scattering identification based on the JONSWAP spectrum and Donelan direction function

Abstract Investigations into recent maritime accidents have revealed that a significant number are linked to the impact of freak waves, which possess the potential to capsize vessels rapidly due to their immense energy. In order to detect and predict these freak waves with precision, thereby mitigating the risks they pose to maritime infrastructure and human safety, we have delved deeper into the study of such waves. This paper introduces an effective and time-invariant three-dimensional (3D) computational model for freak waves. The model is designed to analyze the scattering properties of these waves and to provide a clearer understanding of the variations in their scattering behavior. First, this paper adopts linear superposition method to simulate a 3D random rough sea surface based on the JONSWAP wave spectrum and Donelan directional distribution function. Moreover, we analyze the effect of different angular frequencies on the simulated sea surface. At the same time, the Monte Carlo method is used to further simulate the capillary waves based on the random sea surface, and this method vastly improves the computational accuracy of the random sea surface. Then, based on the two-wave train superposition wave energy focusing mode, a numerical calculation method of time-invariant 3D freak waves is proposed, and the time-series evolution process of 3D freak waves is simulated. Finally, the backscattering coefficients of the 3D freak wave surface are calculated using the two-scale method, and the electromagnetic scattering characteristics of the freak wave surface under different evolution stages, wind speeds, and radar incidence angles are analyzed. By this method, we conclude that the recognition of freak waves is more accurate under medium-high wind speed and significant incidence angle conditions. Our experimental findings demonstrate that the methodologies presented in this study are capable of enhancing the identification and prediction of freak waves. This advancement is expected to bolster the precision of future freak wave forecasts and holds potential for practical application in the realm of freak wave risk alert systems.

Seasonal mobility of transverse finger bars within a mixed sand-gravel bay measured using X-band Radar

Transverse finger bars have largely been associated with sandy coasts. Here we show that these features persist within a wider mixed sediment environment, adjacent to a shingle cuspate foreland, which has not been previously reported. Details of the bars' characteristics were gleaned from analysis of bathymetry data, whilst weekly migration rates were inferred from remote sensing of the sea surface roughness as a proxy of undulating bedforms, using X-band radar reflectance data. The bars were on average ~380 m long, had wavelengths of ~160 m, amplitudes of approximately 0.2 to 0.6 m and were orientated 30° to shore normal. They were found in water depths between −3.3 and −5.8 m Ordnance Datum. The bars migrated by approximately 150 m over the first ‘winter’ observation period (15/11/2020–02/04/2021) and 70 m in the following winter period (Sept 2021–Feb 2022) but showed virtually no signs of movement during the intervening summer months. Analysis of hydrodynamic conditions suggested the bar mobility was related to the dominant longshore currents resulting from high angle, south westerly waves. Low amplitude rhythmic bedforms were also found in the upper beach, migrating at a similar rate to the nearshore bars, which are thought to be driven by high-angle wave instability.

Improved tropical cyclone boundary layer model and its application in floating offshore wind turbine structural response analysis

The accurate simulation of tropical cyclone (TC) wind fields is essential for analyzing structural responses, given the potential severe damage to infrastructure caused by TCs. An improved TC boundary layer mean wind field model is proposed, building upon the Kepert model, by introducing a two-dimensional pressure field that varies with height and radius, a surface turbulent diffusivity influenced by wind speed at the lower boundary, and a surface roughness length affected by waves and spray. The accuracy of the improved model is validated through comparisons with TC observational data. Comparative analysis indicates that the Kepert model overestimates the tangential wind speed component. The two-dimensional pressure field employed in the improved model more accurately simulates the central pressure difference within the TC boundary layer. Furthermore, the incorporation of a surface turbulent diffusivity and sea surface roughness length that better reflects physical phenomena further enhances the accuracy of the improved wind field model. The structural responses of a floating offshore wind turbine (FOWT) situated in the TC eyewall region under emergency shutdown conditions are computed using both wind field models separately. The results indicate that blade tip displacement, tower top displacement, platform translation, and rotation responses (pitch and yaw) are overestimated by the Kepert model. In conclusion, the improved model accurately represents the TC structure, offering precise wind field data for assessing FOWT structural responses in TC conditions.

Genesis and Propagation of Low‐Frequency Abyssal T‐Waves

AbstractAbyssal T‐waves are seismo‐acoustic waves originating from abyssal oceans. Unlike subduction‐zone‐generated slope T‐waves which are generated through multiple reflections between the sea surface and the gently dipping seafloor, the genesis of abyssal T‐waves cannot be explained by the same theory. Several hypotheses, including seafloor scattering, sea surface scattering, and internal‐wave‐induced volumetric scattering, have been proposed to elucidate their genesis and propagation. The elusive mechanism of abyssal T‐waves, particularly at low‐frequencies, hinders their use to quantify ocean temperatures through seismic ocean thermometry (SOT) and estimate oceanic earthquake parameters. Here, using realistic geophysical and oceanographic data, we first conduct numerical simulations to compare synthetic low‐frequency abyssal T‐waves under different hypotheses. Our simulations for the Romanche and Blanco transform faults suggest seafloor scattering as the dominant mechanism, with sea surface and internal waves contributing marginally. Short‐scale bathymetry can significantly enhance abyssal T‐waves across a broad frequency range. Also, observed T‐waves from repeating earthquakes in the Romanche, Chain, and Blanco transform faults exhibit remarkably high repeatability. Given the dynamic nature of sea surface roughness and internal waves, the highly repeatable T‐wave arrivals further support the seafloor scattering as the primary mechanism. The dominance of seafloor scattering makes abyssal T‐waves useable for constraining ocean temperature changes, thereby greatly expanding the data spectrum of SOT. Our observations of repeating abyssal T‐waves in the Romanche and Chain transform faults could provide a valuable data set for understanding Equatorial Atlantic warming. Still, further investigations incorporating high‐resolution bathymetry are warranted to better model abyssal T‐waves for earthquake parameter estimation.

Identifying the spatio-temporal distribution characteristics of offshore wind turbines in China from Sentinel-1 imagery using deep learning

ABSTRACT Offshore wind power is a crucial clean energy source for coastal countries and advances blue economies. Accurate spatial mapping of offshore wind turbines supports energy assessment and the sustainable development of marine resources. Offshore wind power has developed rapidly in China, but the spatiotemporal distribution characteristics of offshore wind turbines and farms remain poorly understood. Extracting nearshore small targets from Sentinel-1 synthetic aperture radar images remain challenging due to noise from the rough sea surface background and the diverse substrate types in offshore wind turbine (OWT) distributions. Here, to address these issues, we first created a dataset of China’s offshore wind turbines (COWTs) that contained typical substrate types (e.g. seawater and tidal flats). Then, a deep learning model integrating an attention mechanism and receptive fields was used to accurately detect COWTs. The well-trained model was used to detect the changes in COWTs from 2015 to 2022. Our model detected an increase in the number of offshore wind farms in China from 7 to 114 (clusters counted as an offshore wind farm), while COWTs increased from 305 to 6451 from 2015 to 2022. The Taiwan Strait exhibited the highest wind speeds, yet the Jiangsu and Guangdong regions had the most installed turbines. Over 90% of COWTs were located in areas with offshore wind energy resources less than 500 W/m3, indicating a mismatch between COWT installation and wind resource distribution. This study demonstrated temporal and geographical generalizability, which is promising for global wind power detection. Our analysis of spatiotemporal variation in COWTs facilitates informed decision-making for future field establishment and sustainable marine planning.

Wind Speed Shear Exponent: Effects of Sea Surface Roughness in Terms of Spectral Bandwidth

Abstract This note presents results on the wind speed shear exponent demonstrating the effects of the sea surface roughness in terms of the spectral bandwidth of ocean surface waves. The wind speed shear exponent is obtained by combining the logarithmic mean wind speed profile and the power law mean wind speed profile containing the sea surface roughness as a parameter, as used by Viselli et al. (2022, “LiDAR Measurements of Wind Shear Exponents and Turbulence Intensity Offshore the Northeast United States,” ASME J. Offshore Mech. Arct. Eng., 144(4), p. 042001). In the present article, the sea surface roughness is given in terms of the spectral bandwidth as presented by Zhao and Li (2024, “Dependence of Drag Coefficient on the Spectral Width of Ocean Waves,” J. Oceanogr., 80(2), pp. 129–143). Examples of results representing realistic wave conditions are provided. The results should be useful demonstrating how the wind speed shear exponent can be estimated using the spectral bandwidth, which is a frequently used parameter to characterize wave conditions.

Near‐Surface Wind Convergence Along the Sea Ice Edge in the Greenland Sea: Its Mean State and Shaping Process

AbstractAt mid‐latitudes, a narrow band of near‐surface wind convergence (NSWC) overlies the western boundary currents in long‐term climatology as a response to steep sea surface temperature gradients. The underlying dynamics shaping mean convergence in the mid‐latitude region have been investigated in detail. In polar regions, surface temperature gradients are intense along the sea ice edges. However, literature concerning NSWC near sea ice edges is limited. This study investigates time‐mean NSWC along sea ice edges and its shaping processes, focusing on the Greenland Sea, based on atmospheric reanalysis. In cold‐season climatology, positive NSWC overlies the sea ice edge, resulting in a localized upward motion reaching the free atmosphere. The mean NSWC was insensitive to sea ice thickness and surface roughness in the regional model. This study suggests that, in addition to local atmospheric boundary processes, extreme NSWC events play a vital role in shaping the mean distribution. Although these features are similar to those along the Gulf Stream, atmospheric fronts appear to play a relatively minor role in the Greenland Sea. Instead, the frequent cyclone generation near the sea ice edge and the anticyclonic circulation over Greenland in conjunction with the transient synoptic circulation seem essential. In the warm season, positive NSWC was virtually missing in the Greenland Sea, unlike in the Gulf Stream region, reflecting the shallow virtual temperature response to the surface thermal forcing. This study contributes to understanding the mechanisms by which sea ice variability affects large‐scale atmospheric circulation in remote regions.

Surface properties of the seas of Titan as revealed by Cassini mission bistatic radar experiments

Saturn’s moon Titan was explored by the Cassini spacecraft from 2004 to 2017. While Cassini revealed a lot about this Earth-like world, its radar observations could only provide limited information about Titan’s liquid hydrocarbons seas Kraken, Ligeia and Punga Mare. Here, we show the results of the analysis of the Cassini mission bistatic radar experiments data of Titan’s polar seas. The dual-polarized nature of bistatic radar observations allow independent estimates of effective relative dielectric constant and small-scale roughness of sea surface, which were not possible via monostatic radar data. We find statistically significant variations in effective dielectric constant (i.e., liquid composition), consistent with a latitudinal dependence in the methane-ethane mixing-ratio. The results on estuaries suggest lower values than the open seas, compatible with methane-rich rivers entering seas with higher ethane content. We estimate small-scale roughness of a few millimeters from the almost purely coherent scattering from the sea surface, hinting at the presence of capillary waves. This roughness is concentrated near estuaries and inter-basin straits, perhaps indicating active tidal currents.

Underwater acoustic propagation and scattering from a dynamic rough sea surface using the Finite-Difference Time-Domain method.

Models for underwater acoustic propagation typically assume that the sea surface is smooth or rough but frozen in time. Long-duration transmissions on the order of tens of seconds are being considered for next-generation SONAR. These types of signals improve target resolution and tracking. However, they can interact with the sea surface at many different wave displacements during transmission. This violates the "frozen" boundary assumption and causes additional transmission losses and Doppler effects on the received signal. Full-wave propagation models can be used to better understand the mechanisms behind these phenomena. This understanding leads to better system design without having to perform expensive at-sea experiments. In this paper, a finite-difference time-domain (FDTD) method is implemented to model the impact of roughness and motion on the sea surface. The FDTD method is a full-wave numeric technique that allows an arbitrary function to define the boundary points. Surface motion is accomplished by modifying these boundary points at each time step. A variable subgrid sea surface boundary technique is developed to improve the accuracy of these FDTD simulations. The rough, time-evolving sea surface is modeled using a Pierson-Moskowitz frequency spectrum, which is simple to implement and fully defined by wind speed and direction.

Georectifying drone image data over water surfaces without fixed ground control: Methodology, uncertainty assessment and application over an estuarine environment

Light-weight consumer-grade drones have the potential to provide geospatial image data to study a broad range of oceanic processes. However, rigorously tested methodologies to effectively and accurately geolocate and rectify these image data over mobile and dynamic water surfaces, where temporally fixed points of reference are unlikely to exist, are limited. We present a simple to use automated workflow for georectifying individual aerial images using position and orientation data from the drone's on-board sensor (i.e. direct-georectification). The presented methodology includes correcting for camera lens distortion and viewing angle and exploits standard mathematics and camera data processing techniques. The method is used to georectify image datasets from test flights with different combinations of altitude and camera angle. Using a test site over land, directly-georectified images, as well as the same images georectified using standard photogrammetry software, are evaluated using a network of known ground control points. The novel methodology performs well with the camera at nadir (both 10 m and 25 m above ground level) and exhibits a mean spatial accuracy of ±1 m. The same accuracy is achieved when the camera angle is 30° at 10 m above ground level but decreases to ±2.9 m at 30° and 25 m. The accuracy changes because the uncertainties are a function of the altitude and angle of the camera versus the ground. Drone in-flight positioning errors can reduce the accuracy further to ±5 m with the camera at 30° and 25 m. An ensemble approach is used to map the uncertainties within the camera field-of-view to show how they change with viewing distance and drone position and orientation. The complete approach is demonstrated over an estuarine environment that includes the shoreline and open water, producing results consistent with the land-based field-tests of accuracy. Overall, the workflow presented here provides a low cost and agile solution for direct-georectification of drone-captured image data over water surfaces. This approach could be used for collecting and processing image data from drones or ship-mounted cameras to provide observations of ocean colour, sea-ice, ocean glitter, sea surface roughness, white-cap coverage, coastal water quality, and river plumes. The Python scripts for the complete image georectification workflow, including uncertainty map generation, are available from https://github.com/JamieLab/SArONG.

Atmospheric Sound Propagation over Rough Sea: Numerical Evaluation of Equivalent Acoustic Impedance of Varying Sea States

This work presents a numerical study on atmospheric sound propagation over rough water surfaces with the aim of improving predictions of sound propagation over long distances. A method for generating pseudorandom sea profiles consistent with sea wave spectra is presented. The proposed method is suited for capturing the logarithmic nature of the energy distribution of the waves. Sea profiles representing fully developed seas for sea states 2, 3, 4, and 5 are generated from the Elfouhaily et al. (ECKV) sea wave spectra. Excess attenuation caused by refraction and surface roughness is predicted with a parabolic equation (PE) solver. A novel method for estimating equivalent effective impedance based on PE predictions at different sea states is presented. Parametric expressions using acoustic frequency and significant wave height are developed for effective surface impedances. In this work, sea surface roughness is on a scale comparable with the acoustic wavelength. Under this condition, the acoustic scattering is primarily incoherent. This work shows the limitations of using an equivalent surface impedance in such incoherent scattering cases.

Rainband‐Occurrence Probability in Northern Hemisphere Tropical Cyclones by Synthetic Aperture Radar Imagery

AbstractRainbands are essential to tropical cyclones (TCs), significantly affecting TC structure and intensity change. High‐resolution synthetic aperture radar (SAR) imagery can capture the footprints of rainbands caused by rain‐induced sea surface roughness modification. Using 464 SAR TC images, we investigated the rainband‐occurrence probability of TCs under different hemispheres, local times (LTs), intensities, and ocean basins. Results show that the rainband‐occurrence probability is highest in the downshear‐left quadrant for Northern Hemisphere TCs (downshear‐right quadrant for Southern Hemisphere TCs). For Northern Hemisphere TCs, the rainband‐occurrence probability is overall higher in the early morning (LT), and the peak region of rainband‐occurrence probability appears farther from the TC center in the evening (LT). Compared with weak TCs, the rainband‐occurrence probability becomes higher for strong TCs in the Northern Hemisphere. Furthermore, TCs have a higher rainband‐occurrence probability in the Northwest Pacific than in the North Atlantic and Northeast Pacific.

Physics schemes in the first version of NCEP operational hurricane analysis and forecast system (HAFS)

This document summarizes the physics schemes used in two configurations of the first version of the operational Hurricane Analysis and Forecast System (HAFSv1) at NOAA NCEP. The physics package in HAFSv1 is the same as that used in NCEP global forecast system (GFS) version 16 except for an additional microphysics scheme and modifications to sea surface roughness lengths, boundary layer scheme, and the entrainment rate in the deep convection scheme. Those modifications are specifically designed for improving the simulation of tropical cyclones (TCs). The two configurations of HAFSv1 mainly differ in the adopted microphysics schemes and TC-specific modifications in addition to model initialization. Experiments are made to highlight the impacts of TC-specific modifications and different microphysics schemes on HAFSv1 performance. Challenges and developmental plans of physics schemes for future versions of operational HAFS are discussed.

Simulating the Gain of a Vertical Array in a Shallow Waveguide with a Rough Sea Surface

Simulating the Gain of a Vertical Array in a Shallow Waveguide with a Rough Sea Surface

Research on optimization method of evaporation duct prediction model based on particle swarm algorithm

The sea surface roughness parameterization and universal stability functions, as key components of the evaporation duct prediction models rooted in the Monin-Obukhov similarity theory, dictate the model performance which further impacts the efficiency and accuracy of offshore electromagnetic applications. In this paper, layered meteorological and hydrological observations are collected during two cruises and processed to obtain the reference modified refractivity profiles close to the sea surface, and then particle swarm algorithm is utilized to optimize the parameters of the sea surface roughness parameterization and universal stability functions. The results show that compared with the pre-optimization model, the prediction accuracy of the optimized model is improved by 5.09% and 8.12% under stable conditions, and by 9.97% and 31.51% under unstable conditions for observation dataset from each cruise, which proves the feasibility of the proposed method for evaporation duct prediction model optimization.

Predicting rapid intensification of tropical cyclones in the western North Pacific: a machine learning and net energy gain rate approach

In this study, a machine learning (ML)-based Tropical Cyclones (TCs) Rapid Intensification (RI) prediction model has been developed by using the Net Energy Gain Rate Index (NGR). This index realistically captures the energy exchanges between the ocean and the atmosphere during the intensification of TCs. It does so by incorporating the thermal conditions of the upper ocean and using an accurate parameterization for sea surface roughness. To evaluate the effectiveness of NGR in enhancing prediction accuracy, five distinct ML algorithms were utilized: Decision Tree, Logistic Regression, Support Vector Machine, K-Nearest Neighbors, and Feed-forward Neural Network. Two sets of experiments were performed for each algorithm. The first set used only traditional predictors, while the second set incorporated NGR. The outcomes revealed that models trained with the inclusion of NGR exhibited superior performance compared to those that only used traditional predictors. Additionally, an ensemble model was developed by utilizing a hard-voting method, combining the predictions of all five individual algorithms. This ensemble approach showed a noteworthy improvement of approximately 10% in the skill score of RI prediction when NGR was included. The findings of this study emphasize the potential of NGR in refining TC intensity prediction and underline the effectiveness of ensemble ML models in RI event detection.

Dependence of drag coefficient on the spectral width of ocean waves

The sea-surface roughness or drag coefficient is ascribed to the effect of various components of ocean waves. Many studies have been focused on the investigation of the dependence of drag coefficient on sea states that are usually denoted by wave age. However, no universally accepted relationship has been obtained up to now and the results are significantly scattered or even contradicted. We reviewed the parameterizations of sea-surface roughness as a function of wave age, and found that the phase speed at spectral peak cp is an important parameter to characterize the drag coefficient. For the same wave age, drag coefficient increases with increasing cp. Contrary to the traditional concept, the older waves with greater cp possesses higher sea-surface roughness for the same wind speed because more wave components participate the air–sea interaction and intensify the wind stress. With the buoy meansurements and the theory of equilibrium range of wind waves, we estimated fricition velocity and proposed that the frequency bandwidth and spectral width of the wave spectrum are more suitable parameters than the traditional wind speed and wave age to be used to parameterize drag coefficient. This study provides a new way to estimate wind stress through the reliable spectra of ocean waves.