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121 results for “Surface Wave”
Processed data and code for manuscript "Non-negligible impact of Stokes drift and wave-driven Eulerian currents on simulated surface particle dispersal in the Mediterranean Sea"
<p>This repository contains the python code and processed data to reproduce analysis and figures from Rühs et al. (2024, Ocean Science): "Non-negligible impact of Stokes drift and wave-driven Eulerian currents on simulated surface particle dispersal in the Mediterranean Sea".</p> <p>To reproduce the whole analysis, including the calculations of the trajectories, the following needs to be downloaded/included into a local working directory:</p> <ul> <li>the content of this repository in respective sub-directories, i.e. code (created and maintained at <a href="https://github.com/sruehs/RuehsEtAl2024_ImpactWavesSurfaceDispersal">https://github.com/sruehs/RuehsEtAl2024_ImpactWavesSurfaceDispersal</a>), data-proc, figs</li> <li>the original surface velocity data, to be downloaded here: <a href="https://zenodo.org/records/10879702">https://zenodo.org/records/10879702</a>, in an additional sub-directory named data-orig</li> </ul> <p>Additionally, the OceanParcels package, available via <a href="https://github.com/OceanParcels/parcels">https://github.com/OceanParcels/parcels</a> or <a href="https://anaconda.org/conda-forge/parcels">https://anaconda.org/conda-forge/parcels</a> needs to be installed in the python working environment. Then, the scripts in the code directory can be executed to re-run the trajectory simulations and analysis. Alternatively, the output in forms of figures and processed data can be accesed directly in the respective sub-directories.</p>
Upper lithospheric structure of northeastern Venezuela from joint inversion of surface wave dispersion and receiver functions
<p>Dataset from the publication: <strong>Upper lithospheric structure of northeastern Venezuela from joint inversion of surface wave dispersion and receiver functions</strong>. DOI: <a href="https://doi.org/10.5194/egusphere-2022-230">10.5194/egusphere-2022-230</a></p> <p> </p> <p>Includes: <em><strong>EGFs, Dispersion Curves measurements, RFs, Vs3dmodel and Moho depths</strong></em></p> <p> </p>
Dataset for Surface waves prediction based on acoustic backscattering
<p>Underwater acoustic measurements dataset is represented by three types of files “.raw", “.mat", ".dat" as follows:<br> * “.raw" format also represented by three types of data.<br> - “...search.raw" files contain complex envelop from all hydrophones calculated at four emitted frequencies<br> - “...chan.raw" files is a signal in a wide band from one of the hydrophones - for control and noise analysis.<br> - the largest files are the raw wideband signal from all hydrophones. One such file was saved per eight-hour sound emission cycle.<br> * Spectrogram files are saved in MATLAB format “.mat” v7 . Phasing of the antenna array (all-round view) and calculation of window spectra near each emitted pulse was carried out.<br> * ".dat" files contain features of the average spectrum of the backscattered signal.<br> * Direct measurements of surface wave characteristics, made by a Datawell DWR-G4 wave-rider buoy, accompanied the acoustic measurements. This data is included too.</p> <p>In this archive, we upload all available files of the "dat" and "mat" type and a limited number of "raw" files. You may unpack all ".tar.gz" files into one folder, preserving the directory tree, existing in the archives.</p> <p>Users should refer to the included “.pdf” file for the data format description and to a published preprint for a description of the experimental conditions and instrumentation characteristics. See [arXiv:arXiv:2204.10153] via <a href="https://arxiv.org/abs/2204.10153">https://arxiv.org/abs/2204.10153</a> (Also check when the link is updated to the journal paper) </p> <p>The authors are grateful to their colleges, who helped during the expedition. Data acquisition would be impossible without their contribution. This research was supported by the Russian Science Foundation, grant number 20-77-10081 (the expedition and motivation for study) and the State Contract with the Ministry of Education and Science of the Russian Federation, grant number 0030-2021-0017 (the instruments for underwater acoustic measurements).</p>
WUSA surface wave amplification measurements (Schardong et al. 2019)
<p>#Western U.S. surface wave amplification measurements</p> <p>If using this data please cite: <br> Schardong, L., Ferreira, A.M., Berbellini, A. and Sturgeon, W., 2019. The anatomy of uppermost mantle shear-wave speed anomalies in the western US from surface-wave amplification. Earth and Planetary Science Letters, 528, p.115822.</p> <p>Directory format:<br> USA.RAV... = vertical-component Rayleigh wave amplification measurements<br> USA.RAH... = horizontal-component Rayleigh wave amplification measurements<br> USA.TOR... = Love wave amplification measurements</p> <p>File format:<br> motion type index -- mode order (n) -- angular order (l) -- period -- relative local amplification w.r.t. PREM -- estimated error on local amplification</p> <p>The number of data points varies depending on the available data in Hendrik van Heijst's database, and the various selection steps. </p>
A dataset for comparing filtering methods used to wave and non-wave flow at the surface of the Agulhas region
<p>This dataset comprises sea surface height (SSH) and velocity data at the ocean surface in two small regions near the Agulhas retroflection. The unfiltered SSH and a horizontal velocity field are provided, along with the same fields after various kinds of filtering, as described in the accompanying manuscript, <em>Using Lagrangian filtering to remove waves from the ocean surface velocity field</em><em> (</em><a href="https://doi.org/10.31223/X5D352">https://doi.org/10.31223/X5D352</a>)<em>. </em>The code repository for this work is <a href="https://github.com/cspencerjones/separating-balanced">https://github.com/cspencerjones/separating-balanced</a> . </p> <p>Two time-resolutions are provided: two weeks of hourly data and 70 days of daily data.</p> <p>Seventy_daysA.nc contains daily data for region A and Seventy_daysB.nc contains daily data for region B, including unfiltered, lagrangian filtered and omega-filtered velocity and sea-surface height. </p> <p>two_weeksA.nc contains hourly data for region A and two_weeksB.nc contains hourly data for region B, including unfiltered and lagrangian filtered velocity and sea-surface height. </p> <p>Note that region A has been moved in version 2 of this dataset. </p> <p>See the manuscript and code repository for more information. </p> <p>This work was supported by NASA award 80NSSC20K1142.</p>
IMMERSE Horizon 2020 Project Downstream User Toolbox – data for tutorial on impact of wave coupling on surface particle dispersion simulations
<p>Exemplary data for tutorial on impact of wave coupling on surface particle dispersal simulations<br> <a href="https://github.com/immerse-project/Downstream-Users-Toolbox/tree/main/T8.3_WaveCoupling_ParticleTransport_UniU">https://github.com/immerse-project/Downstream-Users-Toolbox/tree/main/T8.3_WaveCoupling_ParticleTransport_UniU</a><br> created as part of the downstream user toolbox of the IMMERSE Horizon 2020 project (<a href="https://immerse-ocean.eu/">https://immerse-ocean.eu/</a>).</p> <p>In the tutorial the impact of new options for the representation of wave-current interactions in the NEMO ocean model (<a href="https://www.nemo-ocean.eu/">https://www.nemo-ocean.eu/</a>) on surface particle simulations are tested in a case study for the Mediterranean Sea. The tutorial consists of two jupyter notebooks: Parcels_CalcTraj.ipynb and CompTraj_uncoupledVScoupled.ipynb. Parcels_CalcTraj.ipynb calculates Lagrangian particle trajectories based on velocity output from ocean only as well as coupled ocean-wave model simulation by making use of the OceanParcels software (<a href="https://oceanparcels.org/">https://oceanparcels.org/</a>). CompTraj_uncoupledVScoupled.ipynb compares dispersal statistics of Lagrangian particle trajectories calculated from ocean-only vs coupled ocean-wave model simulations.</p> <p>This repository contains the surface velocity and ocean model grid data needed to run Parcels_CalcTraj.ipynb, as well as the trajectory data produced by Parcels_CalcTraj.ipynb, which is needed to run CompTraj_uncoupledVScoupled.ipynb. The surface velocity data stems from two simulations with a regional high-resolution (1/24° horizontal resolution) model configuration for the Mediterranean Sea: a coupled ocean-wave model simulation and a complimentary ocean-only simulation. These model simulations make use of the NEMO v4.2-RC ocean model, the Wave Watch 3 v.6.07 wave model, the OASIS3-MCT coupler, and ECMWF atmospheric fields; they are described in detail in IMMERSE deliverable D5.7 “Assessment of wave-current effects on the circulation in theMed-MFC system”<strong>.</strong></p>
Multigrid spatially constrained dispersion curve inversion package: towards distributed acoustic sensing surface wave imaging
<p>Surface wave methods, commonly applied in diverse fields, encounter challenges in complex subsurface environments due to limitations inherent in traditional inversion techniques. Conventional one-dimensional inversion (1DI), with its reliance on fixed grids and deterministic linear approaches, often introduces biases, diminishing lateral resolution. Laterally constrained inversion (LCI) improves robustness by addressing lateral coherency but falls short in delineating arbitrary interfaces due to its dependency on fixed grid models. The advent of Distributed Acoustic Sensing (DAS) technology offers extensive seismic data, yet its potential for high-resolution imaging remains underutilized. We introduce a Multigrid Spatially Constrained Dispersion Curve Inversion (MCI) method to overcome these challenges, aiming to harness high-resolution DAS surface wave imaging capabilities. </p> <p>The package includes essential scripts and models required to replicate key figures from the study by Guan et al. (2023, currently under review). These codes are designed to help readers evaluate the effectiveness of the MCI approach using synthetic demonstrations. Additionally, the package includes a refined 2D Vs (shear wave velocity) model derived from a DAS (Distributed Acoustic Sensing) field study conducted in Imperial Valley, California. This model offers new insights into the regional fault system, underscoring the importance of enhanced spatial resolution in large-scale geophysical investigations.</p> <p>It is organized into three directories and contains a total of 14 files. The directory structure is as follows:<br>├── DAS field data<br>│ ├── Pltmodels.m<br>│ ├── README.txt<br>│ ├── field_models.pdf<br>│ ├── model_1DI.mat<br>│ ├── model_LCI.mat<br>│ └── model_MCI.mat<br>├── MCI_Main<br>│ ├── DisForward.p<br>│ ├── InvForward.p<br>│ ├── InvJacobian.p<br>│ ├── MCI.p<br>│ ├── readme.txt<br>│ └── whitejet3.m<br>└── Synthetic demos<br> ├── MCI_Main.m<br> └── syndata.mat</p>
Imaging the sediment cover offshore central Chile with surface-wave dispersion and P-wave conversion using DAS
<p>This repository contains codes and data used to reproduce the figures in the paper <em>Vernet, C. et al, "Imaging the sediment cover offshore central Chile with surface-wave dispersion and P-wave conversion using distributed acoustic sensing", 2025, (<a href="https://doi.org/10.1029/2024JB030507">https://doi.org/10.1029/2024JB030507</a>).</em></p>
Pressure data used in 'Surface-to-space atmospheric waves from Hunga Tonga-Hunga Ha'apai eruption' (Wright et al., 2022)
<p>Pressure data used in 'Surface-to-space atmospheric waves from Hunga Tonga-Hunga Ha’apai eruption' (Wright et al., 2022). </p> <p> </p> <p><strong>Phase speed estimates by station:</strong></p> <p>Author: <em>Fred Prata, AIRES Pty Ltd</em></p> <p>Description:<em> distances, locations, arrival times and phase speed estimates for the Hunga Tonga Lamb wave from pressure stations used in our study.</em></p> <p> </p> <p> </p> <p><strong>Pressure time series data (19 stations):</strong></p> <p><strong>Lauder (1 station):</strong></p> <p>Author: <em>Dan Smale/NIWA, State Highway 85, Omaku, New Zealand</em></p> <p>Description: <em>Data sourced from a CO2 eddy-covariance instrument operated and maintained by NIWA. Values were provided as an image file of pressure anomaly versus time (NZST) which was digitized at approximately 90 s time resolution and 0.1 hPa.</em></p> <p><strong>Mt Eliza / HRO (1 station):</strong></p> <p>Author: <em>Fred Prata/AIRES Pty Ltd, 116 Humphries Road, Mount Eliza, Vic 3930, Australia</em></p> <p>Description: <em>Data derived from an ecowitt weather station (Easyweather-WIFIA 19E) operated and maintained by AIRES Pty Ltd. The measurements are logged every 5 minutes with a pressure resolution of 0.1 hPa.</em></p> <p><strong>Tonga (1 station):</strong></p> <p>Author:<em> Malo e Leilei Taaniela/Fua'amotu Domestic Airport, Tonga and Shane Cronin/University of Auckland, School of Environment, New Zealand.</em></p> <p>Description: <em>Data derived from a barometer operated by the Tongan meteorological office located at Nukualofa port (met.gov.to). Sampling interval is 1 minute and the pressure resolution is 0.1 hPa</em></p> <p><strong>Weatherlink (3 stations):</strong></p> <p>Author: <em>Fred Prata/AIRES Pty Ltd, 116 Humphries Road, Mount Eliza, Vic 3930, Australia</em></p> <p>Description: <em>Data downloaded from http://weatherlink.com The time resolution is 5 minutes for Davis and Boston and 15 minutes for Travis. The pressure resolution is 0.01 in Hg.</em></p> <p><strong>PurpleAir (13 stations): </strong></p> <p>Author: <em>citizen science project - https://map.purpleair.com/ (free for non-commercial use)</em></p> <p>Description: <em>PNG images of pressure traces from each station: American Samoa, Anchorage, Auckland, Brisbane, Colorado Springs, Concepcion, Glenn Dale, Kahuko, Manhattan Beach, Papeete, Solvang, Sydney, Tokyo. See table, described above, for latitude/longitude of each site.</em></p> <p> </p> <p><strong>Other pressure data used in the paper already archived elsewhere, and associated licensing (11 stations):</strong></p> <p><strong>AIMS (10 stations)</strong>: https://apps.aims.gov.au/metadata/search?term=Weather%20Stations (CC BY 3.0 AU)</p> <p><strong>Wegenernet (1 station)</strong>: https://wegenernet.org/portal/v7.1/2021/1 ("openly available to all and free of charge except for commercial usage")</p> <p> </p> <p> </p> <p><strong>Not included (6 stations):</strong></p> <p>Due to licensing terms, we do not include 6 pressure time series obtained from the Australian Bureau of Meteorology in their raw form, specifically those at <em>Mt Isa Aero, Learmonth Airport, Broome Airport, Alice Springs Airport, Adelaide Airport and Perth Airport</em>. Derived products made from these data are permitted to be shared, and accordingly phase speed estimates from these stations are included in the table described above. A graphical representation of the data from <em>Broome</em> is also included in the scientific paper these data support as Extended Data Figure 1e.</p> <p> </p> <p> </p>
Data for Directional Surface Wave Spectra And Sea Ice Structure from ICEsat-2 Altimetry
<p>This is data used for <em>Directional Surface Wave Spectra And Sea Ice Structure from ICEsat-2 Altimetry</em> in the Cryosphere.</p> <p>The code that reproduces this data can be found at</p> <pre>10.5281/zenodo.6908645</pre> <p>See README.md for further instructions.</p> <p> </p>
Response of convectively coupled Kelvin waves to surface temperature forcing in aquaplanet simulations: data and code
<p>This is the data and code used for a journal paper entitled "Response of convectively coupled Kelvin waves to surface temperature forcing in aquaplanet simulations", written by Mu-Ting Chien and Daehyun Kim in 2024. This paper is in minor revision in the Journal of Advances in Modeling Earth System. The submitted paper is here: (https://essopenarchive.org/doi/full/10.22541/essoar.171322728.86206700/v1).</p>
Calculation of RF sheath properties from surface wave-fields: a post-processing method
<p>The accompanying files contain digital data for figures in the article "Calculation of RF sheath properties from surface wave-fields: a post-processing method" by J.R. Myra and H. Kohno, submitted to the journal Plasma Physics and Controlled Fusion.</p> <p><br> Abstract:</p> <p>In ion cyclotron range of frequency (ICRF) experiments in fusion research devices, radio frequency (RF) sheaths form where plasma, strong RF wave fields and material surfaces coexist. These RF sheaths affect plasma material interactions such as sputtering and localized power deposition, as well as the global RF wave fields themselves. RF sheaths may be modeled by employing a sheath boundary condition (BC) in place of the more customary conducting wall BC; however, there are still many ICRF computer codes that do not implement the sheath BC. In this paper we present a method for post-processing results obtained with the conducting wall BC. The post-processing method produces results that are equivalent to those that would have been obtained with the RF sheath BC, under certain assumptions. The post-processing method is also useful for verification of sheath BC implementations and as a guide to interpretation and understanding of the role of RF sheaths and their interactions with the waves that drive them.</p> <p> </p>
Surface Gravity Waves in the Gulf of Mexico
<p>This dataset includes the results of a coupled SWAN-ROMS simulation over the Gulf of Mexico between 2001 and 2010. Published at https://doi.org/10.1029/2018JC014820 (JGR: Oceans)</p>
Surface wave tomography with transfer learning and Moho constraints: Method and application to China mainland
<p>The content of this study is the inversion of surface waves based on deep learning. The dataset includes the trained deep learning model, dispersion data, synthetic data test results, real data application results, error analysis, and more detailes.</p>
Ocean and ice with waves data for role of surface gravity waves in aquaplanet ocean climates
<p>This data corresponds to the runs analysed in the manscript: Role of Surface Gravity Waves in Aquaplanet Ocean Climates (JAMES, 2021).</p> <p>In this work, we present a set of idealised numerical experiments that demonstrate the thermodynamic and dynamic implications of surface gravity waves for the oceanic climate of an aquaplanet. We study the impact of accounting for modulations by such waves upon air-sea momentum fluxes, Langmuir circulation and the Stokes-Coriolis force.</p> <p>This dataset is made up of atmospheric, oceanic and surface gravity wave simulations. When uncompressed the total dataset is 1.6 TB, the ocean and ice with waves component is 564 GB. See below for further details.</p> <p>See the related works section for the corresponding datasets.</p>
Ocean and ice without waves data for role of surface gravity waves in aquaplanet ocean climates
<p>This data corresponds to the runs analysed in the manscript: Role of Surface Gravity Waves in Aquaplanet Ocean Climates (JAMES, 2021).</p> <p>In this work, we present a set of idealised numerical experiments that demonstrate the thermodynamic and dynamic implications of surface gravity waves for the oceanic climate of an aquaplanet. We study the impact of accounting for modulations by such waves upon air-sea momentum fluxes, Langmuir circulation and the Stokes-Coriolis force.</p> <p>This dataset is made up of atmospheric, oceanic and surface gravity wave simulations. When uncompressed the total dataset is 1.6 TB, the ocean and ice without waves component is 484 GB. See below for further details.</p> <p>See the related works section for the corresponding datasets.</p>
Text-fig. 8. a. Shell belonging to one flank of the conch a horizontally bedded individual with sveral large oysters attached to its underside indicating that the shell was either originally vertical or was flipped from one surface to the other by turbulance. Approximately 300 mm across. b. Crushed individual showing oysters encrusting both flanks of the conch. 250 mm in diameter. c. Wave-worn conch showing oysters attached to the umbilicus, the venter and possibly the inside of the body-chamber. Tape measure for scale. d. Flank of conch with crinoid debris and oysters spread around its periphery. Scope of image approximately 350 mm. in 'Cenoceras Islands' In The Blue Lias Formation (Lower Jurassic) Of West Somerset, Uk: Nautilid Dominance And Influence On Benthic Faunas
Text-fig. 8. a. Shell belonging to one flank of the conch a horizontally bedded individual with sveral large oysters attached to its underside indicating that the shell was either originally vertical or was flipped from one surface to the other by turbulance. Approximately 300 mm across. b. Crushed individual showing oysters encrusting both flanks of the conch. 250 mm in diameter. c. Wave-worn conch showing oysters attached to the umbilicus, the venter and possibly the inside of the body-chamber. Tape measure for scale. d. Flank of conch with crinoid debris and oysters spread around its periphery. Scope of image approximately 350 mm.
A niching particle swarm optimization strategy combined with cluster analysis for the multimodal inversion of surface waves
<p>The data include two study cases used for multimodal surface wave inversion.</p> <p>For case 1, the data present a combination of active and passive surface wave methods.</p> <p>For case 3, we use Rayleigh waves to detect a low-velocity soft interlayer underneath the road.</p> <p>Detailed description can be found in the data description document.</p>
Diurnal waves forced by horizontal convergence of near-surface winds on Mars
<p>This site provides public access to data used in the following journal article: </p> <p>D. Hinson and J. Wilson (2023). Diurnal waves forced by horizontal convergence of near-surface winds on Mars, Icarus 394, 115420, doi: 10.1016/j.icarus.2022.115420 </p> <p><a href="https://ntrs.nasa.gov/api/citations/20230001896/downloads/20230001896-Hinson_2022_Diurnal_waves%5B1%5D.pdf">https://ntrs.nasa.gov/api/citations/20230001896/downloads/20230001896-Hinson_2022_Diurnal_waves%5B1%5D.pdf</a></p> <p> </p>
Machine Learning Models for Surface Wave Dispersion Curve Inversion using Mixture Density Networks
<p>Machine learning (ML) approach for dispersion curve inversion using mixture density networks (MDN) based on Keil and Wassermann (2023).</p> <p>The ML approach presented here allows the simultaneous estimation of layer numbers, layer depth and a complete probability distribution of the S-wave velocity structure in the upper 100 m. This is achieved by a two-step ML approach, where 1) a regular NN classifies the number of layers within the upper 100 m of the subsurface and 2) individual trained mixture density networks output the depth estimates together with a fully probabilistic solution of the S-wave velocity structure. We trained the model to distinguish structures with 2 - 7 subsurface layers.</p> <p>The trained classification NN and the individual MDNs are located in the folder ./trained_models.<br> With the jupyter notebook Prediction.ipynb the dispersion curve inversion can be performed using the already trained ML models.<br> With the jupyter notebooks Training-MDN.ipynb and Training-classification.ipynb the models can be trained on new data.<br> The code for the set-up of the MDN is based on Earp et al. (2020).</p> <p> </p> <p>More details and updates on the code can be found on: <a href="https://github.com/SabrinaKeil/MDN_Inversion">https://github.com/SabrinaKeil/MDN_Inversion</a> </p>
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Allen Brain Atlas
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International Brain Laboratory public data
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OpenNeuro
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