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25 results for “water waves”
Dataset for the adjustment of a wave forecasting system for the deep waters of the South Atlantic Ocean and for the southern coast of Brazil: Numerical Wave Experiment in the South of Brazil (NWESB).
<p>This dataset corresponds to the input files of the test domains used for the simulations of the coupled GFS (Global Forecast System) and WAVEWATCH III models in the waters of the South Atlantic Ocean and in waters of the Brazilian Southeastern during the passage of a cold front and the presence of strong pressure gradient between a low-pressure system and a high-pressure system. In the files generated by WAVEWATCH III, wave fields are presented from 2016-03-25 14:00:00, which is the date from when the model it stabilizes. Also contained in this dataset are the files of the GFS model wind fields, the bathymetry files (eTOPO1) and the files of the bathymetry entries in WAVEWATCH III.</p> <p>All files with suffix 2 correspond to the geographic region 70°W to 4°W longitude and 55°S to 13°S latitude and all files with suffix 3 correspond to the geographic region 70°W at 20°W longitude and 55°S at 13°S latitude.</p> <p><strong>ww3-2.inp</strong> and <strong>Bathymetry2.ascii</strong> are the input configuration files for WAVEWATCH III bathymetry and bathymetry (in ASCII format) respectively for the WW3-2 domain. <strong>gfs-2.nc</strong> is the input file of the winds obtained from the outputs of the GFS model (in NetCDF format) for the WW3-2 domain. <strong>ww3-2.nc</strong> is the WAVEWATCH III model output file with the simulated waves for the WW3-2 domain.</p> <p><strong>ww3-3.inp</strong> and <strong>Bathymetry3.ascii</strong> are the input configuration files for WAVEWATCH III bathymetry and bathymetry (in ASCII format) respectively for the WW3-3 domain. <strong>gfs-3.nc</strong> is the input file of the winds obtained from the outputs of the GFS model (in NetCDF format) for the WW3-3 domain. <strong>ww3-3.nc</strong> is the WAVEWATCH III model output file with the simulated waves for the WW3-3 domain.</p> <p>The GFS model files contain data every 6 hours and the WAVEWATCH III model files contain data every 1 hour. All files have a spatial resolution of 0.25° (27.78 km).</p> <p> </p> <p><strong>Other data that complement this dataset:</strong></p> <p><strong><a href="https://figshare.com/articles/figure/Complementary_figures_of_Parameter_adjustments_of_the_GFS_WAVEWATCH_III_coupled_models_in_Southern_Brazil/16726375"><em>Complementary figures of Parameter adjustments of the GFS – WAVEWATCH III coupled models in Southern Brazil.</em></a></strong></p> <p><em><strong><a href="https://figshare.com/articles/dataset/Dataset_for_the_adjustment_of_a_wave_forecasting_system_for_the_deep_waters_of_the_South_Atlantic_Ocean_and_for_the_southern_coast_of_Brazil_Output_files_in_GrADS_format_/16767058">Dataset for the adjustment of a wave forecasting system for the deep waters of the South Atlantic Ocean and for the southern coast of Brazil (Output files in GrADS format).</a></strong></em></p> <p> </p> <p> </p>
Simulations of shallow water wave turbulence
<p><strong>About</strong></p> <p>This dataset curates all the simulations used to reproduce the paper:</p> <blockquote> <p><em>Shallow water wave turbulence</em><br> DOI: <a href="https://doi.org/10.1017/jfm.2019.375">10.1017/jfm.2019.375</a></p> </blockquote> <p>The source code and scripts necessary to generate the manuscript are archived at:</p> <blockquote> <p><a href="https://github.com/ashwinvis/augieretal_jfm_2019_shallow_water">https://github.com/ashwinvis/augieretal_jfm_2019_shallow_water</a></p> </blockquote> <p>See the README in the repository above to generate the manuscript</p> <p><strong>Abstract</strong></p> <p>The dynamics of irrotational shallow water wave turbulence forced at large scales and dissipated at small scales is investigated. First, we derive the shallow water analogue of the ‘four-fifths law’ of Kolmogorov turbulence for a third-order structure function involving velocity and displacement increments. Using this relation and assuming that the flow is dominated by shocks, we develop a simple model predicting that the shock amplitude scales as <span class="math-tex">\((\epsilon d)^{1/3}\)</span>, where <span class="math-tex">\( \epsilon\)</span> is the mean dissipation rate and <span class="math-tex">\(d\)</span> the mean distance between the shocks, and that the <span class="math-tex">\(p\)</span><sup>th</sup>-order displacement and velocity structure functions scale as <span class="math-tex">\((\epsilon d)^{p/3} r/d\)</span>, where <span class="math-tex">\(r\)</span> is the separation. Then we carry out a series of forced simulations with resolutions up to 7680<sup>2</sup>, varying the Froude number,<span class="math-tex">\(F_{f} = (\epsilon L_f)^{1/3}/ c \)</span>, where <span class="math-tex">\(L_f\)</span> is the forcing length scale and <span class="math-tex">\(c\)</span> is the wave speed. In all simulations a stationary state is reached in which there is a constant spectral energy flux and equipartition between kinetic and potential energy in the constant flux range. The third-order structure function relation is satisfied with a high degree of accuracy. Mean energy is found to scale approximately as <span class="math-tex">\(E \sim \sqrt{\epsilon L_f c}\)</span>, and is also dependent on resolution, indicating that shallow water wave turbulence does not fit into the paradigm of a Richardson–Kolmogorov cascade. In all simulations shocks develop, displayed as long thin bands of negative divergence in flow visualizations. The mean distance between the shocks is found to scale as <span class="math-tex">\( d \sim F_f^{1/2} L_f\)</span>. Structure functions of second and higher order are found to scale in good agreement with the model. We conclude that in the weak limit, <span class="math-tex">\(F_f \rightarrow 0 \)</span>, shocks will become denser and weaker and finally disappear for a finite Reynolds number. On the other hand, for a given <span class="math-tex">\(F_f\)</span>, no matter how small, shocks will prevail if the Reynolds number is sufficiently large.</p>
Experimental data for validation of a variational RANS level III flow model: water waves over an array of obstacles and Ogee weir flows
<p>Experimental dataset for the validation of a variational RANS level III flow model. The experimental data correspond to experiments on unsteady of water waves over an array of obstacles and steady curved flows over an Ogee weir. The experiments were conducted at the Hydraulics Laboratory at the Univeristy of Córdoba. </p>
Raw data from Cao et al. (2023) "Electron exchange capacity of pyrogenic dissolved organic matter (DOM): Complementarity of square-wave voltammetry in DMSO and mediated chronoamperometry in water"
<p>Measured and fitted data from square-wave voltammetry (SWV) in DMSO for electron exchange capacities (EECs) of pyrogenic natural organic matter (pyDOM) and natural organic matter (NOM) standards. </p> <p>From Cao, H., A. S. Pavitt, J. M. Hudson, P. G. Tratnyek, and W. Xu. 2023. Electron exchange capacity of pyrogenic dissolved organic matter (DOM): Complementarity of square-wave voltammetry in DMSO and mediated chronoamperometry in water. Environ. Sci. Proc. Impacts: ASAP. [10.1039/d3em00009e]</p> <p>The manuscript reports electron accepting capacity (EAC), electron donating capacity (EDC), and electron exchange capacities (EECs) measured with a new method involving square-wave voltammetry in an aprotic solvent (dimethyl sulfoxide, DMSO). The measurement method, fitting of peak areas, and conversion of peak areas to EECs are described in the main text and supporting information of the manuscript.</p> <p>Here we provide the original measured data, baseline corrected data used in the peak fitting, and fitted peak area data that were used to obtain the final EEC values. The data are provided in one .xlsx file that contains multiple tabs: (i) a table of contents, (ii) a summary of the final fitting results, and (iii) tabs numbered R1-R40 containing raw measured data for each pyDOM/NOM sample.</p> <p>The data provided here should be sufficient to replicate and verify all of the analysis described in the manuscript. If you use these data, please cite this Zenodo record (DOI 10.5281/zenodo.7747020) and the original manuscript (DOI: 10.1039/d3em00009e).</p>
Composite geostationary weather satellite images (second time derivative of water vapor channel) for visualizing Lamb waves
<p>Second time derivative of water vapor channel (6.2 micrometer) brightness temperature from geostationary weather satellites (Units: K s<sup>-2</sup>)</p> <p>Himawari-8 (original data obtained from NICT Science Cloud)</p> <p>GOES-16/17 (original data obtained from Amazon AWS)</p> <p>Meteosat-8/9/10/11 (original data obtained from EUMETSAT)</p> <p> </p> <p>Time interval of the files: 5 minutes</p> <p> </p> <p>Time interval of each satellite, dt for time derivative:</p> <p>Himaawri-8, GOES-16/17: 10 minutes, 10 minutes</p> <p>Meteosat-8/9/11: 15 minutes, 15 minutes</p> <p>Meteosat-10: 5 minutes, 10 minutes</p> <p> </p> <p>Each file contains the latest images from those satellites at that time. The time stamp for each satellite represents the beginning of each full-disk scan.</p> <p> </p> <p>Bias correction:</p> <p>Himawari-8: bias removal for each swath</p> <p>GOES-16/17, Meteosat-11: bias removal for each east-west line</p> <p>Meteosat-8/9/10: bias removal for each east-west line (note: satellite attitude was not stable)</p> <p> </p> <p>Smoothing:</p> <p>Band-pass filter for each full-disk image separately: 2-40 degrees on lat-lon coordinate</p> <p>Stronger smoothing at latitudes higher than 60 degrees north/south</p> <p> </p> <p>Down-sampling:</p> <p>Full-disk images were mapped to a 0.04-degree lat-lon coordinate.</p> <p>Then, composite images were produced at the 0.2-degree resolution.</p> <p> </p> <p>Version 2:</p> <p>Improved interpolation algorithm</p> <p>Himawari-8: improved geolocation</p> <p>Meteosat-8/9: improved treatment of noise near the edge of full disk images</p>
Grib and ASCII data, subset ERA-I for shallow water waves Ocean Science study
<p>Specific output from ERA-I reanalysis (wave model component) containing interated parameters, see https://doi.org/10.5194/os-13-1-2017</p>
Modeled and observed river water temperature and discharge in the paper "Riverine heat waves on the rise, outpacing air heat waves"
<div>The observed and modeled data – mean daily water temperature (WT, °C) and mean daily discharge (Q,<em> </em>ft<sup>3</sup>/s) modeled by an LSTM model (averaged over 5 model runs) – for 1471 sites over 1980-2022 can be found here. Out of these 1471 sites, 1276 sites had good model performance and were used to identify and analyse air and riverine heat waves (RHW) in the paper "Riverine heat waves on the rise, outpacing air heat waves". </div> <div> <div> </div> </div>
Data for Manuscript "Deep-Water Near-Inertial Waves and Turbulence on a Continental Slope in the South China Sea during Typhoon Mangkhut (2018)"
<p>Data for manuscript "Deep-Water Near-Inertial Waves and Turbulence on a Continental Slope in the South China Sea during Typhoon Mangkhut (2018)".</p>
Dataset for the study: Closed-boundary reflections of shallow water waves as an open challenge for physics-informed neural networks
<p>This dataset supports a study on using Physics-Informed Neural Networks (PINNs) to solve the 1D-Shallow Water Equations. It focuses on closed boundary reflection test cases, which are crucial for accurately modeling geophysical fluid dynamics in coastal regions, particularly for storm surge and flood modeling. Properly representing reflections is also essential for accurately modeling related phenomena, such as Kelvin waves and amphidromic systems, which influence coastal water levels during such events. The individual sub-datasets in NetCDF format provide the results used for one figure each. The data arrays within the datasets are named with reference to the figures for ease of comparison. <br><br>Cite as: Demir, K.T.; Logemann, K.; Greenberg, D.S. Closed-Boundary Reflections of Shallow Water Waves as an Open Challenge for Physics-Informed Neural Networks. <em>Mathematics</em> 2024, <em>12</em>, 3315. <a href="https://doi.org/10.3390/math12213315">https://doi.org/10.3390/math12213315</a></p>
Benchmark Experimental Data: Water-Wave Interactions with a Flexible Beam
<div> <h1>Experimental Data for the Experimental Modeling of Water-Wave Interactions with a Flexible Beam</h1> </div> <p>This submission is based on the GitHub repository which was created to share the experimental data presented at the <em>42nd International Conference on Ocean, Offshore and Arctic Engineering (OMAE 2023) in Melbourne, Australia</em> in the form of a conference paper 'Experimental Modeling of Water-Wave Interactions with a Flexible Beam'[1]. The paper has already been published and is available but only behind a paywall. A talk has also been delivered at <a href="https://omae.secure-platform.com/a/solicitations/190/sessiongallery/schedule/items/13635" rel="nofollow">OMAE</a> 2023. The paper presents a series of fluid-structure-interaction (FSI) experiments for studying water-wave interactions with a flexible beam in a wide range of sea conditions thus yielding a variety of FSI test-case data. The experimental campaign is carried out at the Maritime Research Institute Netherlands's (MARIN's) concept basin. The concept basin is a 220m-long, 4m-wide and 3.6m-deep rectilinear basin with a carriage that can transverse along the basin's length. </p> <p>The experimental setup includes a flexible beam which is fixed to the basin's carriage at one end while the other free end is submerged in the water. The setup is designed such that it admits the simultaneous measurements of incident waves and the beam's response. Hence, it is suitable for studying FSI problems. The details about the dimensions of the beam and arrangements of the sensors are described in the form of detailed CAD drawings which are given in <strong>CAD_fsi_beam_exp.pdf</strong>. The shared CAD drawings could be used in the future to reproduce the model. </p> <p>The aim is to use these experimental data to validate FSI solvers commonly employed by the maritime industry in the design of fixed-foundation, offshore wind turbines. The study is divided into three experimental cases which are as follows (click on the case number to read more description):</p> <ul> <li>Case-1 experiments: regular-water-waves interactions with the flexible beam when the carriage is at rest</li> <li>Case-2 experiments: regular-water-waves interactions with the flexible beam when the carriage is moving at a constant speed</li> <li>Case-3 experiments: irregular-water-waves interactions with the flexible beam when the carriage is at rest</li> </ul> <div> <div> <h2>FSI Experiments: Interactions of water-wave with a flexible beam</h2> </div> <p>"<em>All measurements are given in the form of .h5 format files, each of which has a corresponding .pan format file containing details of measurement names, units, frequency, maximum, minimum and standard deviation. The data presented is classified into different folders given as follows:</em>"</p> <ul> <li>Folder <strong>Exp1_carriage_rest_0.25m</strong>;</li> <li>Folder <strong>Exp1_carriage_rest_0.5m</strong>;</li> <li>Folder <strong>Exp2_carriage_moving_0.25m</strong>;</li> <li>Folder <strong>Exp2_carriage_moving_0.5m</strong>;</li> <li>Folder <strong>Exp3_irreg_waves_0.25m</strong>;</li> <li>Folder <strong>Exp3_irreg_waves_0.5m</strong>; and</li> <li>Folder <strong>hammer_test</strong>.</li> </ul> <p>The description of the measurement and corresponding wave parameters are given in each folder.</p> <div> <h2>Data organisation</h2> </div> <p>All the main folders have several sub-folders and each sub-folder consists of mainly two types of files, i.e. <em>.pan</em> and <em>.h5m</em>. The files with extension <em>.pan</em> state the general information about experimental tests and sensors in text format. These <em>.pan</em> have three rows and the third row is divided into several columns. The second row states the information related to the experimental test, for example, the test number (80372_XXCB_XX_XXX_XXX_XX), project name (AEGRE), submerged depth of the beam (Proeven XX), gain, facility name (CB stands for concept basin), and scale (1.000). The first column of the third row shows the abbreviated sensor names which are explained in the table below.</p> <p>TABLE 1: The names and descriptions of the sensors are listed.</p> <table> <tbody> <tr> <th>Name</th> <th>Description</th> </tr> </tbody> <tbody> <tr> <td>C.SPEED</td> <td>Speed of the carriage</td> </tr> <tr> <td>WAVE.FORE</td> <td>Wave elevation measured by the probe located at the front of the beam (26.25 m away from the wavemaker)</td> </tr> <tr> <td>WAVE.SB</td> <td>Wave elevation measured by the probe located parallel to the beam (30 m away from the wavemaker)</td> </tr> <tr> <td>AX_i</td> <td>Accelerations of the beam in x-direction recorded by the accelerometer, where i denotes the accelerometer number</td> </tr> <tr> <td>AY_i</td> <td>Accelerations of the beam in y-direction recorded by accelerometer, where i denotes the accelerometer number</td> </tr> <tr> <td>AZ_i</td> <td>Accelerations of the beam in z-direction recorded by accelerometer, where i denotes the accelerometer number</td> </tr> <tr> <td>Flap 3 Pos</td> <td>Position of the waveflap wavemaker</td> </tr> </tbody> </table> <p>The number with the accelerations, e.g. AX.1, AY.2, and AZ.3, denotes the position of the accelerometer along the beam. The response of the beam is dominant in the direction of wave, i.e. x-direction, therefore the <em>.h5m</em> files contain accelerations in the x-direction for all of the accelerometers. The accelerometers are numbered from 1 to 6, where accelerometer number 1 is at the submerged free end of the beam while accelerometer number 6 is located at the fixed end of the beam. The rest of the accelerometers are numbered 2 to 5 from the free end to the fixed end. The files with extension <em>.h5m</em> contain the actual time-domain measurements obtained from the sensors. Each <em>.h5m</em> from the experimental case contains acceleration signal from all six accelerometers in the x-direction, wave elevation measured by the probe that is 26.25 m away from the wavemaker, wave elevation measured by the probe that is 30 m away from the wavemaker, carriage speed, and variation waveflap position throughout the run. These measurements can be read with the help of post-processing code. The post-processing codes based on MATLAB and Python scripts, with comments, are shared. The names of the MATLAB and Python scripts are <strong>read_model_tst.m</strong> and <strong>read_model_tst.py</strong> respectively. Each script needs the name of the <em>.h5m</em> file as user input. In addition to reading the <em>.h5m</em> file, the script plots the signals from the sensors. For demonstration, the provided MATLAB script is used to plot the comparison of the wavemaker position with the wave elevation measured by the wave probe that is 26.25 m away from the wavemaker.</p> <div> <h2>References</h2> </div> <p>[1] Rehman, W., Bunnik, T., Bokhove, O. and Kelmanson, M. “Experimental Modeling of Water-Wave Interactions with a Flexible Beam.” <em>Proc. ASME 2023 42nd Int. Conf. on Ocean, Offshore and Arctic Eng.</em>: p. 10. 2023. ASME.</p> </div>
Data from: Surface Acoustic Wave-based Lab-On-a-Chip for the fast detection of Legionella pneumophila in water
<p><span>Surface acoustic wave (SAW) -based immuno-biosensors are used for several applications, thanks to their versatility and faster response than conventional analytical methods. SAW immuno-biosensors can be usefully applied to promptly detect bacteria and prevent bacterial infections that can lead to severe diseases. Here, we present a SAW immuno-biosensor to detect <em>Legionella</em> <em>pneumophila</em> in water. Our device, working at ultra-high frequency (740 MHz), is functionalized with an anti-<em>L</em>. <em>pneumophila</em> antibody to maximize the specificity. We report the characteristic curve of the sensor, calculated measuring bacterial samples at known densities, and its related parameters. We also measure <em>L</em>. <em>pneumophila</em> samples contaminated with different Gram-positive and Gram-negative bacterial species (<em>Escherichia</em> <em>coli</em> and <em>Enterococcus</em> <em>faecium</em>) and samples diluted in mains waters. The proposed device is able to detect <em>L</em>. <em>pneumophila</em> in the range from 1</span><span>×</span><span>10<sup>6</sup> to 1</span><span>×</span><span>10<sup>8</sup> CFU/mL, with a limit of blank of 1.22</span><span>×</span><span>10<sup>6</sup> CFU/mL and a limit of detection of 2.01</span><span>×</span><span>10<sup>6</sup> CFU/mL. The nonspecific signal due to contaminant bacteria is very limited and measurements of <em>L</em>. <em>pneumophila</em> are not affected by contamination. We obtain a good detection also in mains water, representing a realistic matrix for <em>L</em>. <em>pneumophila</em>. Our results are encouraging and pave the way to the use of fast, easy-to-use, reliable and precise sensors to prevent bacterial infections in human activities.</span></p>
Compact RINEX dataset of: Optimizing simultaneous water level and wave measurements from multi-GNSS interferometric reflectometry over one year at an exposed coastal site
<p>Compact-RINEX files (Hatanaka-compressed format) for each day of the deployment. Sampling frequency is 1 Hz.</p>
Data from: Surface Acoustic Wave-based Lab-On-a-Chip for the fast detection of Legionella pneumophila in water
Open the record for dataset details and reuse information.
Data on frequency-wavenumber spectra of water waves from videos of the river surface: River Sheaf, UK, Feb-Jun 2019
<p>This data set contains sequences of orthorectified images of the free surface of the River Sheaf, Sheffield, United Kingdom (Latitude: 53.373056$^\circ$ Longitude: -1.463913$^\circ$ (WGS 84)), recorded between February and June 2019, as well as their 3D space-time Fourier power spectrum, and gauging survey data of the stage and flow discharge.</p>
Data and Codes of the Research 《An Analytical Spectral Model for Infragravity Waves over Topography in Intermediate and Shallow Water》
<p>This is the dataset and codes prepared for the submission of the research in the title.</p> <p>Please run the script Main.m directly after unzipping the package. For a detailed guidance of how to use it, please see the Guide.html in the UserGuide folder.</p>
Waves in the deep water regime
Open the record for dataset details and reuse information.
Data Supporting Dissipation Scaled Internal Wave Drag in a Global Heterogeneously Coupled Internal/External Mode Total Water Level Model
Open the record for dataset details and reuse information.
Positioning GNSS data of "Optimizing simultaneous water level and wave measurements from multi-GNSS interferometric reflectometry over one year at an exposed coastal site"
<p>These files show the position of the GNSS antenna used in the experiment. Files are obtained for DOY 53 and 242 in 2020.</p> <p> </p>
Shear wave velocity inversion based on dispersion characteristics of seabed Scholte wave in deep water
<p>Simulated displacement records at the seabed with three different water depths using spectral element method. </p>
In situ observational data for "Typhoon-associated short-term marine heat waves in the northern South China Sea coastal waters"
<p>The in situ observational data for "Typhoon-associated short-term marine heat waves in the northern South China Sea coastal waters", which will be submitted to the Journal of GRL</p>
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International Brain Laboratory public data
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OpenNeuro
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