Estimation of Ocean Wave Heights from Temporal Sequences of X-Band Marine Radar Images
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... http://elpub.wdcb.ru/journals/rjes/doi/2011ES000507.html Nieto Borge et al., 2006;Groeneweg et al., 2011]. WaMos II developers announced possibility to measure the directional spectrum of sea waves and the surface currents for 2 minutes data accumulation. ...
... The system performance depends on weather conditions as well: the wind speed should be higher than 3 m/s. The significant wave height measurements implemented by such radar systems were tested successfully in a number of works by comparison with the buoy and other independent measurements Nieto Borge et al., 1999;Vogelzang et al., 2000;Hessner et al., 2003;Nieto Borge et al., 2006]. It should be noted that almost all of the cited radar vs. buoy comparisons have been done very close to the radar site (500-850 m). ...
... The ripples are modulated by longer waves that makes the long wave patterns visible on the radar screen. The method includes the following steps (we give here the brief description of the steps only, the full description can be found in Reichert et al., 1999;Hessner et al., 2003;Nieto Borge et al., 2006]): ...
Nautical X-band radar (low-cost option with the antenna beamwidth 1:9o) was applied
for monitoring surface near-shore currents in the Black Sea Field Research Facility of the
Southern Branch of P.P. Shirshov Institute of Oceanology. The radar signal was processed
by an algorithm based on the Nieto-Borge approach. The experimental setup is shown to
allow precise real-time near-surface current measurements up to 4 km o�shore. A case study
of a coherent current pattern resembling a sub-mesoscale vortex passing the experimental
�eld is presented.
... http://elpub.wdcb.ru/journals/rjes/doi/2011ES000507.html Nieto Borge et al., 2006;Groeneweg et al., 2011]. WaMos II developers announced possibility to measure the directional spectrum of sea waves and the surface currents for 2 minutes data accumulation. ...
... The system performance depends on weather conditions as well: the wind speed should be higher than 3 m/s. The significant wave height measurements implemented by such radar systems were tested successfully in a number of works by comparison with the buoy and other independent measurements Nieto Borge et al., 1999;Vogelzang et al., 2000;Hessner et al., 2003;Nieto Borge et al., 2006]. It should be noted that almost all of the cited radar vs. buoy comparisons have been done very close to the radar site (500-850 m). ...
... The ripples are modulated by longer waves that makes the long wave patterns visible on the radar screen. The method includes the following steps (we give here the brief description of the steps only, the full description can be found in Reichert et al., 1999;Hessner et al., 2003;Nieto Borge et al., 2006]): ...
... Example of surface elevation ( ), spectral density (S) and coherence (C) for locations 1 and 2 during incoherent-homogeneous conditions ( 1 2 ≪ 1, 1 − 2 ≈ 0). Huang et al., 2017;Nieto Borge et al., 2006, 2008Young et al., 1985) and stereo-wave cameras (e.g., Benetazzo et al., 2012;Bergamasco et al., 2017;Fedele et al., 2013;Guimarães et al., 2020;Vieira et al., 2020) are more suitable for studying wave homogeneity and coherence at these scales. Smit et al. (2016) used a combination of X-band radar observations and the coupled-mode spectral analysis to identify coherent interference contributions in nearshore wave statistics. ...
A better understanding of wave homogeneity, i.e. the spatial variations of the wave characteristics, and wave coherence in coastal areas and fjords is essential for the design and analysis of sea-crossing infrastructures, such as floating bridge concepts. The wave conditions in fjords that are exposed to the open sea are complex and often characterized by a mixed swell–wind sea state. This study investigates the spatial coherence and homogeneity of ocean waves using two years of unique buoy observations from Sulafjorden – a fjord partly exposed to the open sea. We analyze both long term statistics and four selected cases with different sea states. The most exposed locations are dominated by long waves (swell), while the energy of the wind sea is comparable to the swell energy in the more sheltered locations. Despite the study area being relatively small (ca. 2 km ×1 km), the differences in wave conditions are significant because the complex fjord geometry blocks the incoming offshore waves, and changes in fetch and wind conditions affects the local wave growth. For swell waves we measured an along-crest spatial coherence (ca. 0.6) over a 1–2 km distance. The coherence between consecutive crests for swell was weaker (up to 0.3–0.4) for distances between 0.6 km and 1.3 km (up to about 5 wavelengths). Wind sea (both along crest and between crests) showed no coherence over these distances.
... Finally, the corresponding filtered spectrum is calibrated to the actual value of the significant wave height which is either derived from the radar using Signal-to-Noise Ration (SNR)based approach or using independent in-situ measurements. More details on the individual processing steps can be found in the literature (see e.g., [6][7][8] ). ...
X-band radars are in growing use for various oceanographic purposes, providing spatial real-time information about sea state parameters, surface elevations, currents, and bathymetry. Therefore, it is very appealing to use such systems as operational aids to harbour management. In an installation of such a remote sensing system in Haifa Port, consistent radially aligned spikes of brightness randomly distributed with respect to azimuth were identified. These streak noise patterns were found to be interfering with the common approach of oceanographic analysis. Harbour areas are regularly frequented with additional electromagnetic transmissions from other ship and land-based radars, which may serve as a source of such interference. A new approach is proposed for the filtering of such undesirable interference patterns from the X-band radar images. It was verified with comparison to in-situ measurements of a nearby wave buoy. Regardless of the actual source of the corresponding pseudo-wave energy, it was found to be crucial to apply such filtration in order to improve the performance of the standard oceanographic parameter retrieval algorithm. This results in better estimation of the mean sea state parameters towards lower values of the significant wave height. For the commercial WaMoSII system this enhancement was clearly apparent in the improvement of the built-in quality control criteria marks. The developed prepossessing procedure improves the robustness of the directional spectra estimation practically eliminating pseudo-wave energy components. It also extends the system’s capability to measure storm events earlier on, a fact that is of high importance for harbour operational decision making.
... Furthermore, it must be noted that all of the images share the same polarization (HH), as most of them were acquired under Project COA0158, conceived as a study of the ocean surface, where fusion of data acquired by TerraSAR-X and a marine radar was intended. Since ordinary X-Band marine radars scan the water surface with HH polarization, which is useful to analyze different aspects of the sea surface as ocean waves, wind fields or ocean currents, all of them aspects that determine the sea state [27], SAR images with the same polarization were acquired for this project. In [28], the ocean wave imaging by SAR is studied and compared with data acquired by the marine radar, WaMoS (Wave and Surface Current Monitoring System). ...
Statistical analysis of radar clutter has always been one of the topics, where more effort has been put in the last few decades. These studies were usually focused on finding the statistical models that better fitted the clutter distribution; however, the goal of this work is not the modeling of the clutter, but the study of the suitability of the statistical parameters to carry out a sea state classification. In order to achieve this objective and provide some relevance to this study, an important set of maritime and coastal Synthetic Aperture Radar data is considered. Due to the nature of the acquisition of data by SAR sensors, speckle noise is inherent to these data, and a specific study of how this noise affects the clutter distribution is also performed in this work. In pursuit of a sense of wholeness, a thorough study of the most suitable statistical parameters, as well as the most adequate classifier is carried out, achieving excellent results in terms of classification success rates. These concluding results confirm that a sea state classification is not only viable, but also successful using statistical parameters different from those of the best modeling distribution and applying a speckle filter, which allows a better characterization of the parameters used to distinguish between different sea states.
Wind waves play an important role in the climate system, modulating the energy exchange between the ocean and the atmosphere and effecting ocean mixing. However, existing ship-based observational networks of wind waves are still sparse, limiting therefore the possibilities of validating satellite missions and model simulations. In this paper we present data collected on three research cruises in the North Atlantic and Arctic in 2020 and 2021 and the SeaVision system for measuring wind wave characteristics over the open ocean with a standard marine navigation X-band radar. Simultaneously with the SeaVision wind wave characteristic measurements, we also collected data from the Spotter wave buoy at the same locations, and we ran the WaveWatch III model in a very high-resolution configuration over the observational domain. SeaVision measurements were validated against co-located Spotter wave buoy data and intercompared with the output of WaveWatch III simulations. Observations of the wind waves with the navigation X-band radar were found to be in good agreement with buoy data and model simulations with the best match for the wave propagation directions. Supporting datasets consist of significant wave heights, wave directions, wave periods and wave energy frequency spectra derived from both SeaVision and the Spotter buoy. All supporting data are available through the PANGAEA repository – https://doi.org/10.1594/PANGAEA.939620 (Gavrikov et al., 2021). The dataset can be further used for validation of satellite missions and regional wave model experiments. Our study shows the potential of ship navigation X-band radars (when assembled with SeaVision or similar systems) for the development of a new near-global observational network providing a much larger number of wind wave observations compared to e.g. Voluntary Observing Ship (VOS) data and research vessel campaigns.
The global coverage of the observational network of the wind waves is still characterized by the significant gaps in in situ observations. At the same time wind waves play an important role into the Earth’ climate system specifically in the air-sea interaction processes and energy exchange between the ocean and the atmosphere. In this paper we present the SeaVision system for measuring wind waves’ parameters in the open ocean with navigational marine X-band radar and prime data collection from the three research cruises in the North Atlantic (2020 and 2021) and Arctic (2021). Simultaneously with SeaVision observations of the wind waves we were collecting data in the same locations and time with Spotter wave buoy and running WaveWatch III model over our domains. Measurements with SeaVision were quality controlled and validated by comparison with Spotter buoy data and WaveWatch III experiments. Observations of the wind waves with navigational Xband radar are in agreement among these three sources of data, with the best agreement for wave propagation directions. The dataset that supports this paper consists of significant wave height, wave period and wave energy frequency spectrum from both SeaVision and Spotter buoy. Currently the dataset is available through the temporary link (https://sail.ocean.ru/tilinina2021/) while supporting dataset (Tilinina et al., 2021) is in technical processing at PANGAEA repository. The dataset can be used for validation of satellite missions as well as model outputs. One of the major highlights in this study is potential of all ships navigating into the open ocean and equipped with X-band marine radar to participate into the development of another observational network for the wind waves in the open ocean once cheap and independently operating version of the SeaVision (or any other system) is available.
Accurate predictions of coastal and fjord wave conditions are vital for several sectors such as fish farming, fisheries, as well as coastal and maritime infrastructure. In complex coastlines such as the Norwegian coast with thousands of islands, islets, and narrow fjords, accurate wave prediction is challenging. In addition to the necessity of using a state-of-the-art nearshore wave model with high spatial resolution, there is a need for high quality and resolution of forcing fields such as wind, surface currents and bathymetry. Moreover, in such areas, satellite remote sensing techniques are not reliable due to the proximity to the land. The observations are thus mainly limited to point measurements, e.g., wave buoys. In recent years, the Norwegian Public Roads Administration has conducted one of the largest measurement campaigns for wind, wave, and current conditions along the Norwegian coast. The campaign aims to obtain the essential data to construct a ferry-free E39 highway route. It provides observations for advanced met-ocean studies in coastal areas and fjords that previously have not been possible. Using these unique measurements together with a high-resolution wave simulation system based on a spectral wave model, this thesis advances our knowledge about coastal and fjord waves.
Papers I-III investigate wind-generated gravity waves in a complex coastal system with narrow fjords, partly exposed to some of the most energetic offshore waves at the western coast of Norway in the Norwegian Sea. In Paper I, we investigate the importance of wind forcing on coastal and fjord wave conditions. The results indicate that a high-resolution wind forcing is essential to obtain a realistic wind field in complex fjord topography. The best model performance is found at the exposed to open sea locations using high-resolution wind forcing. Local phenomena such as lee effects and wind channelling significantly affect the wave estimates. During extreme cases, simulations without wind forcing are unable to predict the wave height accurately in any of the fjord locations. Paper II focuses on the performance of three different deep-water source term formulations in narrow fjords, known as the Komen approach (based on a pressure-pulse white-capping), the saturation-based white-capping approach, and the observation-based scheme (ST6). The results pinpoint that the fetch geometry has a distinct effect on the model's accuracy at inner fjord locations. The saturation-based white-capping approach performs most accurate in fjords with mixed swell-wind sea conditions. In narrow fetch geometries without swell, all source term packages overestimate the wave energy, with ST6 showing the highest sensitivity to fetch geometry and local wind variations. At inner locations and during strong wind conditions, the results illustrate that the white-capping in ST6 is relatively weak compared to its strong wind input. In Paper III, we quantify the impact of surface currents on wave conditions in fjords and coastal areas. The results highlight the significant role of ocean surface currents on wave modulation at inner fjords. In such areas, the incorporation of ocean forcing considerably improves the wave height estimates. Wave-current interaction is also found to have an effect on wave characteristics such as the relative frequency, spectral bandwidth, and directional spreading in narrow fjords.
Marine X-band radar has been suggested to be capable of monitoring significant wave heights in both offshore and open sea areas. In contrast to studies on offshore radar, significant wave height estimations from coastal radar images, which exhibit complicated radar backscattering features, have received little attention. This study proposes a method for retrieving the significant wave height from coastal areas that are often influenced by nononshore winds. The square root of the signal-to-noise ratio in radar images has been widely applied to estimate the significant wave height. However, nononshore wind cases show a poor correlation between the square root of the signal-to-noise ratio and the
in situ
significant wave height. In addition, the spectral shapes from radar images in nononshore wind cases are very different from those in onshore wind cases. To improve the significant wave height estimations from coastal radar images, we implement an artificial neural network algorithm. After training and testing the algorithm, we confirm that the estimated significant wave heights are more reliable for both onshore and nononshore wind cases if the square root of the signal-to-noise ratio, power from nearshore radar subimages, and
in situ
wind components are included in the input layer of the neural network.
the paper explains the process for obtaining ocean variables using one band X radar located ashore. The system includes one algorithm for selecting a sea clutter area with required information to calculate sea variables such as direction, period and significant height of the waves. The research enhanced reported algorithms to extract those variables by including filtering stages. The system has 10% maximum error compared to an in situ sensor. The results motivate the usage of radar ashore for swell monitoring with direct application in coastal erosion studies
A series of spatial wave images recorded by a conventional marine radar is analyzed to determine the three-dimensional E(kx, ky, omega) spectrum. In the absence of a surface current the spectral energy in this three-dimensional wave number frequency space will lie on a shell defined by the dispersion relationship. Any deviation from the expected dispersion relationship can be interpreted as being due to a current induced Doppler shift of the wave frequency. A least squares curve fitting technique is used to determine the surface current required to account for the observed Doppler shift. A comparison of the radar-determined spectra and surface currents with ground truth data indicates that the radar system and analysis technique produces results consistent with conventional instrumentation.
A method to estimate sea surface elevation maps from marine radar image sequences is presented. This method is the extension of an existing inverse modeling technique to derive wave spectra from marine radar images, which assumes linear wave theory with temporal stationarity and spatial homogeneity of the observed sea surface elevation. The proposed technique to estimate wave elevation maps takes into account a modulation transfer function (MTF), which describes the radar imaging mechanisms at grazing incidence and horizontal polarization. This MTF is investigated and empirically determined by wave measurements and numerical simulations. The numerical simulations show that shadowing is the dominant effect in the radar imaging mechanism at grazing incidence and horizontal polarization. Further comparisons of wave spectra, as well as comparisons of the wave height probability distributions obtained by the wave elevation maps and the corresponding buoy measurements with the theoretical Rayleigh distribution, confirm the applicability of the proposed method.
This paper outlines the concept of the modulation transfer function used to relate fluctuations in power received by an active microwave system viewing the ocean to the dominant surface waves on the ocean which cause the fluctuations. Expressions are given for the purely geometric modulations related to surface tilting and changes in range. The manner in which the modulation transfer function enters into the interpretation of real and synthetic aperture radar images, wave spectrometer outputs, scatterometer wind retrievals, and altimeter measurements is reviewed. Finally, limitations of the concept are discussed.
The aim of this work is to present an approach to describe complex sea states including the ones consisting of superpositions of swell and wind sea components, using a nautical radar in X-band as a remote sensing technique. In the present article, the inversion method to obtain the spectral representation of the wave fields is described. The method is applied to analyse data obtained from simulation techniques, as well as from measurements obtained during oceanographic campaigns in the Bay of Biscay and North Sea.
Common marine X-Band radars can be used as a sensor to survey ocean wave fields. The wave field images provided by the radars are sampled and analysed by a wave monitoring system (called WaMoS II) developed by the German research institute GKSS. This measuring system can be mounted on a ship, on offshore stations or at coastal locations. The measurement is based on the backscatter of microwaves from the ocean surface, which is visible as `sea clutter' on the radar screen. From this observable sea clutter, a numerical analysis is carried out. The unambiguous directional wave spectrum, the surface currents and sea state parameters such as wave periods, wave lengths, and wave directions can be derived. To provide absolute wave heights, the response of the nautical radar must be calibrated. Similar to the wave height estimations for Synthetic Aperture Radars, the so-called `Signal to Noise Ratio' leads to the determination of the significant wave height (HS). In this paper, WaMoS II results are compared with directional buoy data to show the capabilities of nautical microwave radars for sea state measurements.
The high wavenumber detection cut-off is determined above which the spectrum of ocean waves imaged by a synthetic aperture radar (SAR) is lost in the background noise spectrum consisting of the clutter noise associated with the Rayleigh statistics of the backscattering surface and the thermal noise originating in the SAR system itself. For given power, the maximum detection cut-off wavenumber is attained if the SAR resolution is chosen such that the clutter and noise spectra are equal at the cut-off wavenumber. Assuming a constant modulation transfer function relating the image modulation and wave slope spectra, the cut-off wavenumber is in this case proportional to (ρaρg), where ρa and ρg represent the full bandwidth (single look) azimuthal and ground range resolutions, respectively. The same proportionality holds (but with a cut-off wavenumber increased by a factor √2) for a clutter limited cut-off, the normal operating condition of an SAR. To first order, incoherent multilook averaging has no influence on the signal-to-background detection cut-off wavenumber, provided the reduced Nyquist cut-off wavenumber resulting from the reduced multilook spatial resolution remains greater than the signal-to-background cut-off wavenumber. Estimates of the detection cut-off wave-numbers are given for the Seasat SAR and the SAR proposed for the European Remote Sensing Satellite ERS-1.
