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57 results for “waves and winds”
Roles of Wind-Driven Currents and Surface Waves in Sediment Resuspension and Transport During a Tropical Storm
<p>Roles of Wind-Driven Currents and Surface Waves in Sediment Resuspension and Transport During a Tropical Storm</p>
Effects of Tropospheric Vertical Wind Shear on Gravity Waves Generated by Tropical Cyclones
<p>The configuration for running WRF, and the data and codes for ploting the figures in our manuscript are provided here.</p>
Large eddy simulation input data for publication "Pressure fields in the airflow over wind-generated surface waves" by Funke et al.
<p>Input files allow for the simulation of a turbulent flow over a sinusoidal surface using the PALM LES model, version 6.0, revision 4901.</p>
Database for "A New Four-Component L*-dependent Model for Radial Diffusion based on Solar Wind and Magnetospheric Drivers of ULF Waves"
<p>Database for <strong>"A New Four-Component L*-dependent Model for Radial Diffusion based on Solar Wind and Magnetospheric Drivers of ULF Waves" </strong>submitted to Space Weather by Murphy et al. </p> <p>The repository contains 2 datasets:</p> <ul> <li>The power spectral density of the compressional magnetic field from THEMIS, Van Allen Probes, and GOES along with accompanying position (MLT, L, L* TS05), solar wind, and geomagnetic data</li> <li>The power spectral density of the azimuthal electric field from THEMIS and Van Allen Probes along with accompanying position (MLT, L, L* TS05), solar wind, and geomagnetic data</li> </ul> <p>Both datasets are provided as an IDL save file and as an HDF5 file.</p> <p>The IDL data can be loaded using: </p> <pre><code>filename='electric_field_psd.sav' filename='magnetic_field_psd.sav' restore, filename, /verbose</code></pre> <p>The HDF5 file can be opened and investigate using (small changes will be required to store each variable):</p> <pre><code class="language-python">import h5py filename = 'magnetic_field_psd.h5' # magnetic field data filename = 'magnetic_field_psd.h5' # electric field data with h5py.File(filename, "r") as f: # loop through all keys (data) #print key and key attribute and size for i in f.keys(): print(f"{i} - {f[i].attrs['attributes']}, shape - {f[i].shape}") ds_obj = f[i] # returns as a h5py dataset object ds_arr = f[i][()] # returns as a numpy array</code></pre> <p>Below is a description of unique and common variables in each file. The psd variables have a shape [f,t], indicating the first dimension is frequency and the second is time, the f_mhz variable has shape [f], and all time series have shape [t]; here [f] and [t] denotes the number of elements in frequency and time arrays. </p> <p>------------------------------------</p> <p><strong>Magnetic field data set:</strong></p> <p><strong><em>Files</em></strong></p> <ul> <li>magnetic_field_psd.h5</li> <li>magnetic_field_psd.sav</li> </ul> <p><strong><em>Unique Data (variable in file)</em></strong></p> <ul> <li>psd <ul> <li>Power spectral density of the compressional magnetic field from THEMIS, Van Allen Probes, and GOES</li> <li>Units - nT<sup>2</sup>/mHz</li> <li>Shape - [f, t]</li> </ul> </li> </ul> <p>------------------------------------</p> <p><strong>Electric field data set:</strong></p> <p><strong><em>Files</em></strong></p> <ul> <li>electric_field_psd.h5</li> <li>electric_field_psd.sav</li> </ul> <p><strong><em>Unique Data (variable in file)</em></strong></p> <ul> <li>psd <ul> <li>Power spectral density of the azimuthal electric field from THEMIS, Van Allen Probes, and GOES</li> <li>Units - (mV/m)<sup>2</sup>/mHz</li> <li>Shape - [f, t]</li> </ul> </li> </ul> <p>------------------------------------</p> <p><strong>Common Data in the Magnetic and Electric Field Datasets (variable in file):</strong></p> <ul> <li>probe <ul> <li>Corresponding satellite of each time stamp</li> <li>Shape [t]</li> </ul> </li> <li>t <ul> <li>Time stamp of each time series; number of seconds since 1970 (UNIX time), [t]</li> <li>Units - s</li> <li>Shape [t]</li> </ul> </li> <li>f_mhz <ul> <li>Frequency of psd data, [f]</li> <li>Units - mHz</li> <li>Shape [f] - (19)</li> </ul> </li> <li>ae <ul> <li>OMNI AE index of each time stamp</li> <li>Units - nT'</li> <li>Shape [t]</li> </ul> </li> <li>al <ul> <li>OMNI AL index of each time stamp, units - nT</li> <li>Shape [t]</li> </ul> </li> <li>au <ul> <li>OMNI AU index of each time stamp </li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>b_t <ul> <li>OMNI IMF B of each time stamp</li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>b_x <ul> <li>OMNI IMF Bx (GSM) of each time stamp</li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>b_y <ul> <li>OMNI IMF By (GSM) of each time stamp</li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>b_z <ul> <li>OMNI IMF Bz (GSM) of each time stamp</li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>dst <ul> <li>OMNI Dst of each time stamp</li> <li>Units - nT</li> <li>Shape [t]</li> </ul> </li> <li>kp - <ul> <li>OMNI Kp (Kp*10) of each time stamp</li> <li>units - NA</li> <li>Shape [t]</li> </ul> </li> <li>l_sh <ul> <li>L-shell of each time stamp</li> <li>Shape [t]</li> </ul> </li> <li>ls_t05 <ul> <li>L* from TS05 of each time stamp</li> <li>Shape [t]</li> </ul> </li> <li>mlt <ul> <li>Magnetic Local Time of each time stamp</li> <li>Unit - hour</li> <li>Shape [t]</li> </ul> </li> <li>n <ul> <li>OMNI Solar Wind Proton Density of each time stamp</li> <li>Units - n/cc</li> <li>Shape [t]</li> </ul> </li> <li>pdyn <ul> <li>OMNI Solar Wind Dynamic Pressure (flow pressure) of each time stamp</li> <li>Units - nPa</li> <li>Shape [t]</li> </ul> </li> <li>symh - b'OMNI Sym-H of each time stamp, units - nT', shape - (477205,)</li> <li>v_t <ul> <li>OMNI Solar wind V of each time stamp</li> <li>Units - km/s</li> <li>Shape [t]</li> </ul> </li> <li>v_x <ul> <li>OMNI Solar wind Vx (GSE) of each time stamp</li> <li>Units - km/s</li> <li>Shape [t]</li> </ul> </li> <li>v_y <ul> <li>OMNI Solar wind Vy (GSE) of each time stamp</li> <li>Units - km/s</li> <li>Shape [t]</li> </ul> </li> <li>v_z <ul> <li>OMNI Solar wind Vz (GSE) of each time stamp</li> <li>Units - km/s</li> <li>Shape [t]</li> </ul> </li> </ul> <p> </p> <p> </p> <p> </p>
Wind profile in the wave boundary layer and its application in a coupled atmosphere-wave model
<p>The simulation data for the study</p>
figure data for Statistical Properties and Distributions of Dayside Whistler-mode Waves Under Various Solar Wind Conditions
<p>Figure dataset saved in csv files with the same binned format as the paper described. </p>
Spectral Flux Decomposition in a Wind-Driven Channel Flow with Near-Inertial Waves
Open the record for dataset details and reuse information.
Data for "The Nature of Right-handed Polarized Ion-scale Waves In the Near-Sun Solar Wind and Extended Solar Corona: the Antisunward Fast-Magnetosonic Whistler Wave or the Sunward Ion Cyclotron Wave?" by Shi et al.
<h2>The database includes all theoretical analysis results based on the linear model.</h2> <h2>Captions:</h2> <p><strong>data_fig1.mat</strong> file is used to plot Figure 1, which is the data of the instabilities driven by the interplay of the temperature anisotropy and relative streaming speed of the proton beam component.</p> <div>x_axis: X axis data, relative streaming speed</div> <div>y_axis: Y axis data, temperature anisotropy</div> <div>gamma_max: normalized growth rate \gamma, used in figure 1a</div> <div>frequency_max: normalized frequency f in the plasma frame, used in figure 1b</div> <div>theta_max: wave propagating angle \theta in the plasma frame , used in figure 1c</div> <div>ellip_max: ellipticity \epsilon in the plasma frame, used in figure 1d</div> <div>theta_max_sc: wave propagating angle \theta in the spacecraft frame , used in figure 1e</div> <div>ellip_max_sc: ellipticity \epsilon in the spacecraft frame, used in figure 1f</div> <div> </div> <div> </div> <p><strong>data_fig2.mat </strong>file is used to plot Figure 2, which is the data of the dependence of the instability in regime I on the relative steaming speed of the proton beam component.</p> <div> sICW_x_axis1: X axis data of figure 2a, normalized k </div> <div> sICW_y_axis1: Y axis data of figure 2a, relative streaming speed</div> <div> sICW_gamma_all: normalized growth rate \gamma of sunward ICW in k and relative steaming speed space, used in figure 2a</div> <div> sICW_x_axis2: X axis data of figure 2b-2e, normalized k</div> <div> sICW_frequency: normalized frequency f of sunward ICW, used in figure 2b</div> <div> sICW_gamma: normalized growth rate \gamma of sunward ICW, used in figure 2c</div> <div> sICW_etr_b: the energy transfer rate of proton beam, used in figure 2d</div> <div> sICW_etr_c: the energy transfer rate of proton core, used in figure 2e</div> <div> </div> <div> asICW_x_axis1: X axis data of figure 2f, normalized k </div> <div> asICW_y_axis1: Y axis data of figure 2f, relative streaming speed</div> <div> asICW_gamma_all: normalized growth rate \gamma of antisunward ICW in k and relative steaming speed space, used in figure 2f</div> <div> asICW_x_axis2: X axis data of figure 2g-2j, normalized k</div> <div> asICW_frequency: normalized frequency f of antisunward ICW, used in figure 2g</div> <div> asICW_gamma: normalized growth rate \gamma of antisunward ICW, used in figure 2h</div> <div> asICW_etr_b: the energy transfer rate of proton beam, used in figure 2i</div> <div> asICW_etr_c: the energy transfer rate of proton core, used in figure 2j</div> <div> </div> <div> </div> <p><strong>data_fig3.mat</strong> file is used to plot Figure 3, which is the data of the dependence of the instability in regime I on the temperature anisotropy of the proton core component.</p> <div> sICW_x_axis1: X axis data of figure 3a, normalized k </div> <div> sICW_y_axis1: Y axis data of figure 3a, temperature anisotropy of the proton core</div> <div> sICW_gamma_all: normalized growth rate \gamma of sunward ICW in k and temperature anisotropy of the proton core space, used in figure 3a</div> <div> sICW_x_axis2: X axis data of figure 3b-3e, normalized k</div> <div> sICW_frequency: normalized frequency f of sunward ICW, used in figure 3b</div> <div> sICW_gamma: normalized growth rate \gamma of sunward ICW, used in figure 3c</div> <div> sICW_etr_b: the energy transfer rate of proton beam, used in figure 3d</div> <div> sICW_etr_c: the energy transfer rate of proton core, used in figure 3e</div> <div> </div> <div> asICW_x_axis1: X axis data of figure 3f, normalized k </div> <div> asICW_y_axis1: Y axis data of figure 3f, temperature anisotropy of the proton core</div> <div> asICW_gamma_all: normalized growth rate \gamma of antisunward ICW in k and temperature anisotropy of the proton core space, used in figure 3f</div> <div> asICW_x_axis2: X axis data of figure 3g-3j, normalized k</div> <div> asICW_frequency: normalized frequency f of antisunward ICW, used in figure 3g</div> <div> asICW_gamma: normalized growth rate \gamma of antisunward ICW, used in figure 3h</div> <div> asICW_etr_b: the energy transfer rate of proton beam, used in figure 3i</div> <div> asICW_etr_c: the energy transfer rate of proton core, used in figure 3j</div> <div> </div> <div> </div> <p><strong>data_fig4</strong>.mat file is used to plot Figure 4, which is the data of the dependence of the wave frequency on the bulk flow speed.</p> <div> x_axis: X axis of figure 4, bulk flow speed.</div> <div> y_axis1: Y axis of figure 4a, normalized relative streaming speed of regime III and V</div> <div> y_axis2: Y axis of figure 4b, normalized relative streaming speed of regime I</div> <div> fVsw_fmw: the normalized frequency distributions in spacecraft frame of antisunward fast-magnetosonic whistler waves in regimes III and V, used in figure 4a</div> <div> fVsw_sic: the normalized frequency distributions in spacecraft frame of sunward ion cyclotron waves in regimes I, used in figure 4b</div> <div> fVsw_fmw_n1: spacecraft frequency of antisunward fast-magnetosonic whistler waves on the 1.5VA bulk flow speed, used in figure 4c</div> <div> fVsw_fmw_n2: spacecraft frequency of antisunward fast-magnetosonic whistler waves on the 2.5VA bulk flow speed, used in figure 4c</div> <div> fVsw_sic_n1: spacecraft frequency of sunward ion cyclotron waves on the 0.25VA bulk flow speed, used in figure 4c</div> <div> fVsw_sic_n2: spacecraft frequency of sunward ion cyclotron waves on the 0.5VA bulk flow speed, used in figure 4c</div> <div> fVsw_sic_n3: spacecraft frequency of sunward ion cyclotron waves on the 0.75VA bulk flow speed, used in figure 4c</div>
Wind Plasma and Radio Waves (WAVES) Electron Plasma Densities, Level H0 (H0), 17.664 s Data
Wind Waves high time resolution plasma densities estimated by using the electron density inferred from the thermal noise peak as recognized by a neural network.
Wind Plasma and Radio Waves (WAVES) Electric Field Intensities and Electron Plasma Densities, Key Parameter (K0), 3 min Data
Wind Waves instrument, Plasma and Radio Wave Key Parameter data: electric field average intensity in dB above background at 76 log-spaced frequencies from 250 Hz to 9.4 MHz that is determined from neural network analysis of the in situ electron plasma frequency, Fpe, line.
Wind Radio/Plasma Wave, (WAVES) Hi-Res Parameters CDF
Wind Waves RAD2, RAD1, and TNR data in CDF format. RAD1 RAD1 is the low frequency radio astronomy receiver. It sweeps over the range of 20 to 1040 kHz with as many as 256 channels. However, some of the time the number of channels is restricted to 16 or 32 so that direction of arrival and polarization information can be obtained. RAD2 RAD2 is the high frequency radio astronomy receiver. It sweeps over the range of 1.075 to 13.825 MHz with as many as 256 channels. However, some of the time the number of channels is restricted to 16 or 32 so that direction of arrival and polarization information can be obtained. TNR The thermal noise receiver (TNR) is designed to actively track the solar wind plasma frequency. TNR consists of 5 overlapping bands. Each band covers 2 octaves, with the next band beginning at the mid point of the lower band. The overall frequency range is 4 - 256 kHz. Usually the tnr is operated in a mode where the first, third and fifth bands are sampled, but occassionally the instrument is driven by neural network software which tries to pick the one band containing the plasma frequency. For more information: The Radio and Plasma Wave Investigation on the Wind Spacecraft, Sp.Sci.Rev.,Vol 71, pg, 231-263,1995
Wave Height and Wind Speed
<p>This data is a product of the South China Sea multi-nested wave model. The South China Sea multi-nested wave model is based on the third-generation wave model WW3, which uses the latest wind stress calculation scheme to improve the model's forecasting effect on wind and waves generated by different wind speeds and changing wind fields. The model can provide hourly forecasts, including wind speed and wind waves, with a spatial resolution of up to 1/40°*1/40°.</p>
The correspondence of Pc5 waves at high latitude and in the solar wind during the solar cycle 23: Data of figures
<p>This deposit contains the data used in the publication in Journal of Geophysical Research: Space Physics, currently titled "The correspondence of Pc5 waves at high latitude and in the solar wind during the solar cycle 23". The paper associated with the data has been submitted on 19.12.2018, and this upload has been done to correspond with the data policy of the journal. The data will be made available through Zenodo after the paper has been accepted for publication or as required by the journal.</p> <p>The steps required to produce this derived data set have been described in detail in the publication above.</p>
High frequency wind recored from sonic anemometers: Impact of swell waves on atmospheric surface turbulence
<p>Wind-wave interaction affects the fluxes of exchange processes across the air-sea wavy interface. For example, several measurements and modelling experiments have suggested that during the swell waves the wind shear and veer may significantly vary (depending on thermal stratification among other environmental factors). This dataset shows interesting wave energy penetration observed from sonic anemometer mounted at an offshore met-mast at 15m height above the mean sea level. More codes will be provided in Github</p> <p>https://github.com/MostafaBakhoda/JGRL\_2022\_WindWaveDecomposition.git</p> <p>The data are used in following submitted research:</p> <p>"Impact of swell waves on atmospheric surface turbulence: A wave-turbulence decomposition method"</p> <p> </p> <p> </p> <p> </p>
Wind Plasma and Radio Waves (WAVES) Time Domain Sampler (TDS) Dust Impact, Level 3, 1 s Data
Wind WAVES Time Domain Sampler, TDS, Dust Data File References: 1) Bougeret, J.-L., et al., WAVES: The Radio and Plasma Wave Investigation on the Wind Spacecraft, Space Sci. Rev., 71, 231-263, 1995, doi:10.1007/BF00751331. 2) Malaspina, D.M., M. Horanyi, A. Zaslavsky, K. Goetz, L.B. Wilson III, and K. Kersten, Interplanetary and Interstellar Dust observed by the Wind/WAVES Electric Field Instrument, Geophys. Res. Lett., 41, 266-272, 2014, doi:10.1002/2013GL058786. 3) Malaspina, D.M., and L.B. Wilson III, A Database of Interplanetary and Interstellar Dust Detected by the Wind Spacecraft, J. Geophys. Res., 121, 9369-9377, 2016, doi:10.1002/2016JA023209.
Wind-generated Gravity Waves Retrieval from Sea Surface Elevation by Airborne Ka-band Interferometric Altimeter
<p>This dataset contains WSSE simulation data , AirKaIA data and sea state data used in the paper "Wind-generated Gravity Waves Retrieval from Sea Surface Elevation by Airborne Ka-band Interferometric Altimeter".</p>
Wind-generated Gravity Waves Retrieval from Sea Surface Elevation by Airborne Ka-band Interferometric Altimeter
<p>This dataset contains WSSE simulation data , AirKaIA data and sea state data used in the paper "Wind-generated Gravity Waves Retrieval from Sea Surface Elevation by Airborne Ka-band Interferometric Altimeter".</p>
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Allen Brain Atlas
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DANDI Archive for NWB datasets
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International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.