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510 results for “storms”
Multi-process driven unusually large equatorial perturbation electric fields during the April 2023 geomagnetic storm
<p>Dataset of the article entitled "Multi-process driven unusually large equatorial perturbation electric fields during the April 2023 geomagnetic storm" submitted to <a href="https://www.frontiersin.org/journals/astronomy-and-space-sciences">Frontiers in Astronomy and Space Sciences</a>. The data include the outputs of simulations from the four empirical vertical drift models used in the article: Fejer and Scherliess (1997), Scherliess and Fejer (1999), Kelley and Retterer (2008), and Manoj and Maus (2012).</p>
Large-Scale Traveling Ionospheric Disturbances over the European sector during the geomagnetic storm on March 23-24, 2023: energy deposition in the source regions and the propagation characteristics
<p>IMAGE 2D Ionospheric Equivalent Currents for 23 and 24 March 2023 (https://space.fmi.fi/image/). </p> <p><em>We thank the institutes who maintain the IMAGE Magnetometer Array (<a href="https://space.fmi.fi/image/">https://space.fmi.fi/image/</a>): Tromsø Geophysical Observatory of UiT the Arctic University of Norway (Norway), Finnish Meteorological Institute (Finland), Institute of Geophysics Polish Academy of Sciences (Poland), GFZ German Research Centre for Geosciences (Germany), Geological Survey of Sweden (Sweden), Swedish Institute of Space Physics (Sweden), Sodankylä Geophysical Observatory of the University of Oulu (Finland), DTU Technical University of Denmark (Denmark), and Science Institute of the University of Iceland (Iceland). The provisioning of data from AAL, GOT, HAS, NRA, VXJ, FKP, ROE, BFE, BOR, HOV, SCO, KUL, and NAQ is supported by the ESA contracts number 4000128139/19/D/CT as well as 4000138064/22/D/KS. The authors would like to thank Dr. Liisa Juusola for providing the IMAGE 2D Ionospheric Equivalent Currents data.</em></p>
BAS-PRO Model Solution for "Modeling Field Line Curvature Scattering Loss of 1 to 10MeV Protons during Geomagnetic Storms"
<p>The file <em>ModelingFLC_BASPRO_solution.zip</em> is a BAS-PRO model solution output archived as a zip file. After extracting the zip file, the solution will be spread across multiple plaintext files. The solution is a grid of proton phase space density multiplied by proton rest mass cubed, f, with units km-6 s3. f is specified in terms of the first, second and third adiabatic invariants μ, K and L as well as time.</p> <p>The solution files can be loaded using the BAS-PRO plotting library, available at <a href="https://github.com/atmosalex/BAS-PRO_plotting" target="_blank" rel="noopener">https://github.com/atmosalex/BAS-PRO_plotting</a>. A copy of the BAS-PRO plotting library has also been bundled with this dataset (<em>BAS-PRO_plotting-main.zip</em>) to prevent potential compatibility issues arising from future updates to the online repository. It is recommend to follow the steps in the "Getting started" section of the plotting library README.md file, as this will result in plots of the solution, and will also convert the plaintext solution files into a single file in binary .cdf format which allows for faster loading.</p> <p>The plaintext solution included in this dataset is made up of two sets of files which correspond to different grid resolutions:</p> <ul> <li>Files ending in 'dyn.txt' are 'dynamic output' files, containing the sampled time evolution of f throughout the simulation period. The dynamic output grid is lower resolution than the original BAS-PRO simulation grid in order to save disk space. These files are useful for producing plots.</li> <li>Files <strong>not</strong> ending in 'dyn.txt' are 'simulation grid' files, containing f at the final simulation epoch only, at the original simulation grid resolution. These files are useful for loading into BAS-PRO as an initial condition, or for plotting the final epoch at higher resolution.</li> </ul> <p>The coordinate range of the 'simulation grid' ('dynamic output') files is as follows:</p> <ul> <li>log10(μ/ (1MeV/G)) ranges from: 0.029384425 to 4.2519649 (0.17108176 to 4.1952860)</li> <li>K ranges from: 0 to 5.729029 (0 to 5.729029) in units G0.5 RE</li> <li>L ranges from: 1.13 to 4.0 (1.13 to 4.0)</li> <li>time ranges from 1388534400 to 1517443200, given in terms of seconds passed since January 1, 1970 UTC, and this time range is from January 1, 2014 to February 1, 2018.</li> </ul> <p>The following table gives a description of each file included:</p> <table> <tbody> <tr> <td><em>axis_mu.txt</em></td> <td>first dimension axis: a list of log10(μ/ (1MeV/G)) for the μ of each simulation grid point</td> </tr> <tr> <td><em>axis_K.txt</em></td> <td>second dimension axis: a list of K for each simulation grid point, with units G0.5 RE</td> </tr> <tr> <td><em>axis_L.txt</em></td> <td>third dimension axis: a list of L at each simulation grid point</td> </tr> <tr> <td><em>axis_t.txt</em></td> <td>time axis: a list of each simulation epoch, showing the history of timestepping</td> </tr> <tr> <td><em>map_iK-aeq.txt</em></td> <td>a 2D grid of equatorial pitch angle (degrees) corresponding to each L (rows) and K (columns) listed in the corresponding simulation axis files. A fill value of -1 is used to signify coordinates outside the trapping region.</td> </tr> <tr> <td><em>iK-0001_2D_en.txt</em></td> <td>a 2D grid of energy, with units of megaelectron volt, at each μ (rows) and L (columns) coordinate defined in the simulation axis files, at the K corresponding to the K index listed in the file name. For example, iK-0001... means the first K on the 3D model grid, corresponding to the first value of K listed in the <em>axis_K.txt</em> file. </td> </tr> <tr> <td><em>iK-0001_2D_f.txt</em></td> <td>a 2D grid of f, with units km-6 s3, at each μ (rows) and L (columns) coordinate defined in the simulation axis files, at the K corresponding to the K index listed in the file name. For example, iK-0001... means the first K on the 3D model grid, corresponding to the first value of K listed in the <em>axis_K.txt</em> file.</td> </tr> <tr> <td><em>iK-0001_axis_aeq.txt</em></td> <td>a list of equatorial pitch angle (degrees) at each L in the <em>axis_L.txt</em> file, at the K index listed in the file name. For example, iK-0001... means the first K on the 3D model grid, corresponding to the first value of K listed in the <em>axis_K.txt</em> file. A fill value of -1 is used to signify coordinates outside the trapping region.</td> </tr> <tr> <td><em>...<br></em></td> <td>...</td> </tr> <tr> <td><em>axis_mu_dyn.txt</em></td> <td>first dimension axis: a list of log10(μ/ (1MeV/G)) for the μ of each grid point in the dynamic output of the model</td> </tr> <tr> <td><em>axis_K_dyn.txt</em></td> <td>second dimension axis: a list of K for each grid point in the dynamic output of the model, with units G0.5 RE</td> </tr> <tr> <td><em>axis_L_dyn.txt</em></td> <td>third dimension axis: a list of L at each grid point in the dynamic output of the model</td> </tr> <tr> <td><em>axis_t_dyn.txt</em></td> <td>time axis: a list of each dynamic output epoch</td> </tr> <tr> <td><em>map_iK-aeq_dyn.txt</em></td> <td>a 2D grid of equatorial pitch angle (degrees) corresponding to each L (rows) and K (columns) listed in the corresponding axis files for the dynamic output. A fill value of -1 is used to signify coordinates outside the trapping region.</td> </tr> <tr> <td><em>iK-0001_2D_en_dyn.txt</em></td> <td>a 2D grid of energy, with units of megaelectron volt, at each μ (rows) and L (columns) coordinate defined in the dynamic output axis files, at the K corresponding to the K index listed in the file name. For example, iK-0001... means the first K on the 3D model grid, corresponding to the first value of K listed in the <em>axis_K_dyn.txt</em> file. </td> </tr> <tr> <td><em>iK-0001_2D_f_dyn.txt</em></td> <td>a 2D grid of f, with units km-6 s3, at each μ (rows) and L (columns) coordinate defined in the dynamic output axis files, at the K corresponding to the K index listed in the file name. For example, iK-0001... means the first K on the 3D model grid, corresponding to the first value of K listed in the <em>axis_K_dyn.txt</em> file. The 2D grid is output for every timestep and appended to the file, so subsequent 2D grids correspond to subsequent timesteps at the same K.</td> </tr> <tr> <td><em>iK-0001_axis_aeq_dyn.txt</em></td> <td>a list of equatorial pitch angle (degrees) at each L in the <em>axis_L_dyn.txt</em> file, at the K index listed in the file name. For example, iK-0001... means the first K on the 3D model grid, corresponding to the first value of K listed in the <em>axis_K_dyn.txt</em> file. A fill value of -1 is used to signify coordinates outside the trapping region.</td> </tr> <tr> <td><em>...</em></td> <td>...</td> </tr> <tr> <td><em>progress.txt</em></td> <td>a file used by the BAS-PRO model to continue from partially complete simulations. It contains three values (one per line): epoch of the simulation start time; total simulation time elapsed (seconds); and a mode select value (1 for dynamic, 0 for steady state)</td> </tr> <tr> <td><em>resume.config</em></td> <td>a backup of the original configuration options used to execute the BAS-PRO simulation, used only by the model</td> </tr> </tbody> </table>
STORM Data: Transcriptionally active chromatin loops contain both 'active' and 'inactive' histone modifications that exhibit exclusivity at the level of nucleosome clusters
<p>The dataset underlying the SMLM STORM super-resolution images of 'Transcriptionally active chromatin loops contain both ‘active’ and ‘inactive’ histone modifications that exhibit exclusivity at the level of nucleosome clusters'. See Biorxiv paper for details on sample preparation: <a href="https://www.biorxiv.org/content/10.1101/2023.09.03.555774v1.full.pdf">https://www.biorxiv.org/content/10.1101/2023.09.03.555774v1.full.pdf</a>, Pyranose Oxidase STORM buffer on Elyra 7 Zeiss Microscope, processed with Zen Black. Samples are named according to which figures they occur in the above paper.</p>
Observational dataset for "Using Satellite and ARM Observations to Evaluate Cold Air Outbreak Cloud Transitions in E3SM Global Storm-Resolving Simulations"
<p>This observational dataset include the DOE ARM ground based observations and satellite for the paper titled “Using Satellite and ARM Observations to Evaluate Cold Air Outbreak Cloud Transitions in E3SM Global Storm-Resolving Simulations” on GRL.</p> <p>For the original data source, all ARM observational data sets used in this study are publicly available from the ARM data archive site (https://adc.arm.gov/discovery/#/results/iopShortName::amf2019comble/datastream::anxarmbeatmM1.c1/datastream::anxarmbecldradM1.c1/datastream::anxarsclkazr1kolliasM1.c0). MODIS MOD06 L2 cloud product are publicly available from (https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MOD06 L2, DOI:10.5067/MODIS/MOD06 L2.061).</p> <p>CloudSat products can be ordered from the CloudSat Data Processing center (https://www.cloudsat.cira.colostate.edu/order/). To download CloudSat data, a new user must first create an account by filling out the signup form (https://www.cloudsat.cira.colostate.edu/accounts/signup/).</p>
Supporting Data for "Climate Sensitivity and Relative Humidity Changes in Global Storm-Resolving Model Simulations of Climate Change"
<p>Code and netcdf files of processed X-SHiELD and CMIP6 simulations to reproduce the figures of Timothy M. Merlis, Kai-Yuan Cheng, Ilai Guendelman, Lucas Harris, Christopher S. Bretherton, Maximilien Bolot, Linjiong Zhou, Alex Kaltenbaugh, Spencer K. Clark, Gabriel A. Vecchi, and Stephan Fueglistaler (2024): "Climate Sensitivity and Relative Humidity Changes in Global Storm-Resolving Model Simulations of Climate Change".</p>
Source data for: Electrochemically controlled blinking of fluorophores for quantitative STORM imaging
<p>Stochastic optical reconstruction microscopy (STORM) allows widefield imaging with single-molecule resolution by calculating the coordinates of individual fluorophores from the separation of the fluorophore emission in both time and space. Such separation is achieved by photoswitching the fluorophores between a long-lived OFF state and an emissive ON state. While STORM can image single molecules, molecular counting remains challenging due to undercounting errors from photobleached or overlapping dyes and overcounting artifacts from the repetitive random blinking of the dyes. Here, we show that fluorophores can be switched electrochemically for STORM imaging (EC-STORM), with excellent control over the switching kinetics, duty cycle, and recovery yield. Using EC-STORM, we demonstrate molecular counting by using electrochemical potential to control the photophysics of dyes. The random blinking of dyes is suppressed by a negative potential but the switching ON event can be activated by a short pulsed positive potential, such that the frequency of ON events scales linearly with the number of underlying dyes. We also demonstrate the EC-STORM of tubulins in fixed cells with a spatial resolution as low as ~28 nm and counting of single Alexa 647 fluorophores on various DNA nanoruler structures. This control over fluorophore switching will enable EC-STORM to be broadly applicable in super-resolution imaging and molecular counting.</p>
Relative Contributions of Field-Aligned Currents and Particle Precipitation to the Inter-Hemispheric Asymmetry at High Latitudes During 2015 St. Patrick Day Storm
<p>Data for Space Weather paper "Relative Contributions of Field-Aligned Currents and Particle Precipitation to the Inter-Hemispheric Asymmetry at High Latitudes During 2015 St. Patrick Day Storm". </p>
Supporting Materials for: The calm before the (next) storm: no third outburst in 2019--2020, and ongoing monitoring of the transient AGN IC 3599
<p>This deposit contains supporting materials for the article "The calm before the (next) storm: no third outburst in 2019--2020, and ongoing monitoring of the transient AGN IC 3599" by Grupe, Komossa, and Wolsing (2024), ApJ. </p> <p>The deposit has the following machine-readable tables:</p> <p><a href="../api/records/10899673/draft/files/MRT_longterm_xray_fig1.txt/content" target="_blank" rel="noopener noreferrer">MRT_longterm_xray_fig1.txt</a>: contains the data used to create Figure 1. These are 0.2-2.0 keV fluxes of IC 3599 fromm ROSAT, Chandra, and Swift observations. </p> <div> </div> <div><a href="../api/records/10899673/draft/files/MRT_swift_observations.txt/content" target="_blank" rel="noopener noreferrer">MRT_swift_observations.txt </a>: contains the information about the Swift XRT and UVOT observations, including the Target ID, segement, start and end times in UT, the MJD of the middle of the exposure times in the XRET and each of the UVOT filters. This table is Table 1 in the Appendix of the paper</div> <div> </div> <div> </div> <div><a href="../api/records/10899673/draft/files/MRT_Swift_results.txt/content" target="_blank" rel="noopener noreferrer">MRT_Swift_results.txt</a> : contains the fluxes measured in the XRT and UVOT filters. These results were used to create Figures 2 - 7. This table is Table 3 in the Appendix of the paper. </div> <div> </div> <p>The deposit conatins the UV W2 image that was use to create Figure 10. This is <a href="../api/records/10899673/draft/files/uvw2_sum_low_2013_2023.fits/content" target="_blank" rel="noopener noreferrer">uvw2_sum_low_2013_2023.fits</a></p> <p> </p> <p>The files <a href="../api/records/10899673/draft/files/arf_pc_2013_2023.fits/content" target="_blank" rel="noopener noreferrer">arf_pc_2013_2023.fits</a> , <a href="../api/records/10899673/draft/files/backgr_spec_pc_2013_2023.fits/content" target="_blank" rel="noopener noreferrer">backgr_spec_pc_2013_2023.fits</a> , <a href="../api/records/10899673/draft/files/source_spec_pc_2013_2023.fits/content" target="_blank" rel="noopener noreferrer">source_spec_pc_2013_2023.fits </a>were used for the X-ray spectral analysis of the low state data between 2013-2023. This low state spectrum is displayed in Figures 8 and 9, and was used for the</p> <p>spectral energy distribution in Fugure 11. </p> <p> </p> <p>The files <span><a href="../api/records/10899673/draft/files/arf_pc_high_2010.fits/content" target="_blank" rel="noopener noreferrer">arf_pc_high_2010.fits</a></span> , <span><a href="../api/records/10899673/draft/files/backgr_spec_high_2010.fits/content" target="_blank" rel="noopener noreferrer">backgr_spec_high_2010.fits</a></span> , <span><a href="../api/records/10899673/draft/files/source_spec_high_2010.fits/content" target="_blank" rel="noopener noreferrer">source_spec_high_2010.fits</a></span> were used to do the X-ray spectral</p> <p>analysis of the 2010 high state data. This spectrum is displayed in Figure 9 and was used in Figure 11 to show the spectral energy distribution. </p> <p> </p>
Simulation Data from STORMI and SWMF Models for the May 2024 Geomagnetic Storm Analysis
<p>Simulation Data from STORMI and SWMF Models for the May 2024 Geomagnetic Storm Analysis.</p>
Data for the figures in "In-cloud characteristics observed in US Northeast and Midwest non-orographic winter storms with implications for ice particle mass growth and residence time"
<div> <div>This repository contains data shown in the figures in Allen, L. R., Yuter, S. E., Crowe, D. M., Miller, M. A., and Thornhill, K. L.: "In-cloud characteristics observed in US Northeast and Midwest non-orographic winter storms with implications for ice particle mass growth and residence time," to be submitted to Atmospheric Chemistry and Physics.</div> <div> </div> <div> <div> <div>Description of the original data sources: All of the NASA IMPACTS data are archived by GHRC at https://ghrc.nsstc.nasa.gov/uso/ds_details/collections/impactsC.html (McMurdie et al., 2019). The NSF PLOWS 1-second flight-level data are archived by the UCAR Earth Observing Laboratory at https://data.eol.ucar.edu/dataset/113.063 (UCAR/NCAR - Earth Observing Laboratory, 2011). ERA5 hourly data on pressure levels are available from the Copernicus Climate Data Store at https://cds.climate.copernicus.eu/datasets/reanalysis-era5-pressure-levels?tab=overview (Hersbach et al., 2023b). ERA5 hourly single-level data are available from the Copernicus Climate Data Store at https://cds.climate.copernicus.eu/datasets/reanalysis-era5-single-levels?tab=overview (Hersbach et al., 2023a).</div> </div> </div> </div>
Data used in "Storms drive outgassing of CO2 in the Subpolar Southern Ocean"
<p><strong>Description: </strong></p> <p>The data included in this repository were used to generate the analysis and resulting figures for the paper "Storms drive outgassing of CO<sub>2</sub> in the subpolar Southern Ocean" in Nature Communications.</p> <p>Abstract:</p> <p>"The subpolar Southern Ocean is a critical region where CO<sub>2</sub> outgassing influences the global mean air-sea CO<sub>2</sub> flux (F<sub>CO2</sub>). However, the processes controlling the outgassing remain elusive. We show, using an unprecedented multi-glider dataset combining F<sub>CO2</sub> and ocean turbulence, that the air-sea gradient of CO2 (∆pCO<sub>2</sub>) is modulated by synoptic storm-driven ocean variability (20 µatm, 1-10 days) through two processes. Ekman transport explains 60% of the variability, and entrainment drives strong episodic CO<sub>2</sub> outgassing events of 2-4 mol m<sup>-2</sup> yr<sup>-1</sup>. Extrapolation across the subpolar Southern Ocean using a process model shows how ocean fronts spatially modulate synoptic variability in ∆pCO<sub>2</sub> (6 µatm<sup>2</sup> average) and how spatial variations in stratification influence synoptic entrainment of deeper carbon into the mixed layer (3.5 mol m<sup>-2</sup> yr<sup>-1</sup> average). These results not only constrain aliased-driven uncertainties in F<sub>CO2</sub> but also the effects of synoptic variability on slower seasonal or longer ocean physics-carbon dynamics."</p> <p>In this study, we first use a two-month dataset from the Southern Ocean Seasonal Cycle Experiment (SOSCEx) which utilised multiple autonomous platforms to simultaneously observe the coupled atmosphere - ocean system, in high-resolution, in the Atlantic sector of the subpolar Southern Ocean. Corresponding processed data for this experiment used by this study is provided in the folder /Data/SOSCEx_STORM2_Glider_Data.</p> <p>Using these data we explain how storms influence, through ocean mixed layer physics (advection and mixing), the direction and magnitude of the air-sea CO<sub>2 </sub>gradient (∆pCO<sub>2</sub>) and flux (F<sub>CO2</sub>) over the duration of the experiment. We construct a conceptual ocean mixed layer model that captures the observed synoptic variability of ∆pCO<sub>2</sub> in the observations, we estimate the synoptic variability around the entire subpolar Southern Ocean. The relating data for this second step can be found under /Data/Generalisation</p> <p><strong>Related code:</strong></p> <p>The data files provided are those that are required to create the figures for this study and/or perform key analyses. Each figure or analysis has an associated python script. Auxiliary data that are not provided in this repository are available in other public repositories and have been referred to in the main study manuscript and in each of the python scripts where they are used. The python scripts for this study are found at the corresponding authors GitHub at <a href="https://github.com/sarahnicholson/SouthernOceanStormsCO2">https://github.com/sarahnicholson/SouthernOceanStormsCO2</a>.</p>
Data in support of manuscript "Impacts of storm surge barriers on drag, mixing, and exchange flow in a partially mixed estuary" submitted to JGR-Oceans
<p>Data set in support of manuscript "Impacts of storm surge barriers on drag, mixing, and exchange flow in a partially mixed estuary" submitted to JGR-Oceans in November 2021. Matlab script (makeFigs_barDragMix_upload.m) is used to generate the figures from the manuscript. Data files (*.mat) correspond with each figure (*.png). For questions or additional information please contact D. Ralston.</p>
Cross-shore distribution of the wave-induced circulation over a dissipative beach under storm wave conditions: the dataset
<p>###</p> <p>Author: Marc Pezerat (marc.pezerat@univ-lr.fr or pezeratm@gmail.com)</p> <p>Date: 19/01/2022</p> <p>Purpose: This repository provides the field observations presented in the paper referred below</p> <p>Reference: Pezerat, M., Bertin, X., Martins, K. and Lavaud, L. (2022) Cross-shore distribution of the wave-induced circulation over a dissipative beach under storm wave conditions. Submitted to Journal of Geophysical Research-Ocean</p> <p>###</p> <p>* The directory Obs includes :</p> <p>(1) Wave bulk parameters, computed as described in the paper for the 7 sensors used in this study (.dat files) : the offshore AWAC and ADCP 600 kHz, the intertidal ADCP2MHz, PT2, PT3, ADV and PT5. For the three ADCP and the ADV, the average velocity measurements within the fixed wave cell of both horizontal components of the current in the ENU frame are provided ("uwcell" and "vwcell"). The field "wdepth" corresponds to the water depth above the seabed (i.e. corrected from sensor's elevation). The fields "fmin" and "fc" are the boundaries for the wave energy frequency spectra integration performed to compute the wave bulk parameters.</p> <p>(2) The vertical velocity profiles of the three components of the current in the ENU frame (u,v,w and hv, which corresponds to the norm of the horizonal current) for the three ADCP, provided in netCDF files in which the coordinate "z" corresponds to the height above the seabed.</p> <p>* The file sensors_loc.dat gathers the coordinates and the elevation above the seabed of each sensor.</p> <p>* The file "SaintTrojan_bathy_topo_ST2021.gr3" corresponds to the grid with the bathymetry used for this study in a format compliant with SCHISM-WWM.</p> <p>* The file "ww3.bnds_SaintTrojan_0.2d_ZWND14m_20210110_20210301_spec.nc" contains wave energy spectra issued from a North Atlantic application of Wavewatch III model that are used to force WWM at the offshore boundary.</p>
Dataset for XBeach model calibration and simulation of sample storms in an intermediate-to-dissipative, microtidal coast (Sabaudia, Italy)
<p>The archive contains XBeach input data and datasets used for the calibration of the hydrodynamic model XBeach and subsequent simulation of six sample storms at different tidal levels at the intermediate-to-dissipative, microtidal coast of Sabaudia (Tyrrhenian Sea, Italy).</p> <p>VERSION v2 (January 24, 2022): uploaded a new version of the dataset to support the revised version of the manuscript.</p>
Wave dissipation and mean circulation on a shore platform under storm wave conditions: the dataset
<p>###</p> <p>Author: Laura Lavaud (laura.lavaud@univ-lr.fr or llavaud@orange.fr)</p> <p>Date: 11/02/2022</p> <p>Purpose: This repository provides the field observations presented in the paper referred below</p> <p>Reference: Lavaud, L., Bertin, X., Martins, K., Pezerat, M., Coulombier, T., Dausse, D. (2022). Wave dissipation and mean circulation on a shore platform under storm wave conditions. Submitted to Journal of Geophysical Research-Earth Surface</p> <p>###</p> <p>* The directory storm_conditions is relative to the field campaign conducted in storm conditions, it includes:</p> <p>(1) Wave bulk parameters, computed as described in the paper for the 8 sensors used in this study: (SENSOR_Date_WDepth_Hm0_Tm02_Tpc_Fmin_Fmax.dat files) : the offshore ADCP 600 kHz and PT2, the intertidal PT4, 5, 6, 8, 9 and 10. The field "WDepth" in the title of each file corresponds to the mean water depth above the seabed (i.e. corrected from sensor's elevation above the bed), Hm0 is the significant wave height, Tm02 the mean wave period and Tpc the continuous peak period. The fields "Fmin" and "Fmax" are the boundaries for the integration of the wave energy frequency spectra performed to compute the wave bulk parameters.</p> <p>(2) The file ADV7_Date_WDepth_horizontal_current_ENU_20minburst.dat contains the WDepth and the horizontal components of the velocity at the ADV in the ENU frame, averaged over bursts of 20 minutes. </p> <p>(3) The file sensors_coordinates_storm_cond.dat gathers the coordinates and the NGF (IGN69) elevation of the seabed at the location of each sensor.</p> <p>* The directory fair_weather_conditions is relative to the field campaign conducted in fair weather conditions, it includes:</p> <p>(1) Wave bulk parameters, computed for the 6 sensors used in this study (SENSOR_Date_WDepth_Hm0_Tm02_Tp_Fmin0pt04_Fmax0pt25.dat files) : the intertidal ADCP 2MHz, PT0, PT1, ADV, PT2 and PT3.</p> <p>(2) The file sensors_coordinates_fairweather_cond.dat gathers the coordinates of each sensor.</p> <p>* The directory model_input_files includes the files used to run the simulations presented in this study:</p> <p>(1) The SCHISM and WWM input files (param.nml and wwminput.nml) and the vertical grid (vgrid.in)</p> <p>(2) The file "ww3.bnd_spec_20200120_20200229.nc" which corresponds to directional wave energy spectra used to force WWM at the open boundary of the computational grid. These spectra were computed from a North Atlantic application of Wavewatch III model forced by CFSR wind fields. </p>
Event list for intense geoelectric fields identified from EarthScope sites during geomagnetic storms
<p>This data set contains properties of intense geoelectric field events identified from 24 EarthScope sites during geomagnetic storms with Dst minima of less than - 100 nT from 2006 to 2019. Columns are the name of EarthScope site that detected the event (station), geoelectric field component in which the event was detected (Ecomp), the component value of geoelectric fields (Edata), geoelectric field peak prominence, geoelectric field peak width, dBx/dt at the time of the geoelectric field peak, dBy/dt at the time of geoelectric field peak, date and time when the intense geoelectric field peak was observed, geographical latitude of the EarthScope site (glat), geographical longitude of the EarthScope site (glon), geomagnetic latitude of the EarthScope site (mlat), geomagnetic longitude of the EarthScope site (mlon), and magnetic local time of the EarthScope site when the event was detected (mlt).</p>
Innovatory rainfall simulator design – A concept of moving storm automation DATA
<p>Rainfall simulator discharge data for two slope conditions, two storm movement directions, and three different velocities.</p>
Comparing Storm Resolving Models and Climates via Unsupervised Machine Learning
<p>Storm-resolving climate models (SRMs) have gained international interest for their unprecedented detail with which they globally resolve convection. However, this high resolution also makes it difficult to quantify the emergent differences or similarities among complex atmospheric formations induced by different parameterizations of sub-grid information. This paper uses modern unsupervised machine learning methods to analyze and intercompare SRMs based on their high-dimensional simulation data, learning low-dimensional latent representations and assessing model similarities based on these representations. To quantify such inter-SRM ``distribution shifts'', we use variational autoencoders in conjunction with vector quantization. Our analysis involving nine different global SRMs reveals that only six of them are aligned in their representation of atmospheric dynamics. Our analysis furthermore reveals regional and planetary signatures of the convective response to global warming in a fully unsupervised, data-driven way. In particular, this approach can help elucidate the effects of climate change on rare convection types, such as ``Green Cumuli''. Our study provides a path toward evaluating future high-resolution global climate simulation data more objectively and with less human intervention than has historically been needed.</p>
Evaluation of Slowfade Diamond as a Buffer for STORM Microscopy: raw data
<p>raw microscope data associated with the article "Evaluation of Slowfade Diamond as a Buffer for STORM Microscopy"</p> <p>See article for details about the imaging conditions.</p> <p> </p>
ScienceDex guides
Understand access before you commit
These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
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.