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25 results for “dust storm”
MarsWRF imposed dust simulations for investigation of dust storm trajectories
<p>This dataset contains representative MarsWRF simulation outputs used to conduct the analysis discussed in Wang et al. [2023]. The model uses a staggered "C" computational grid, with 52 vertical eta layers and a 2º longitude × 2° latitude horizontal resolution. The model grid structure is summarized in a data object structure that is saved in the IDL software (NV5 Geospatial, Broomfield, CO) SAV file format as “wrfgrid.sav”. It can be read using the IDL software command “restore,'wrfgrid.sav'” or using the Python “scipy” library module that can interpret the IDL SAV file format, scipy.io.readsav.</p> <p>Files for each simulation are collected using the “tar” archive tool and compressed using the “gzip” tool to minimize storage requirements, and can be extracted similarly, (e.g., tar -xvzf *.tar.gz). Each file is written in NetCDF format and contains 30 sols of 2-hourly output (i.e., 360 output timesteps per file) for U (zonal wind), V (meridional wind), T (perturbation potential temperature with respect to 300 K, i.e., potential temperature – 300., which is a native WRF output field), PSFC (surface pressure), L_S (solar longitude), and UST (surface friction velocity). </p> <p>The no-storm control run simulation employs the dust optical depth scenario saved in dustscenario_nostorm.nc. This optical depth scenario is derived from the observationally-derived multiannual dust climatology [Montabone et al., 2015] by reducing the climatology to a single year and removing the influence of large dust storm episodes in the contributing years. The other MarsWRF simulations included in this archive impose additional dust optical depth over the base no-storm dust scenario in different latitudinal bands (i.e., spanning all longitudes) and for different L<sub>S</sub> time periods, as indicated by the archive file names. For example, ls200n230_45N75N_tau1.732.tar is the archive file for the simulation with additional imposed dust between 45ºN and 75ºN from Ls = 200º to Ls = 230º with the imposed optical depth amplitude of 1.732. Due to the large data volume, the archived files for each simulation can only cover the corresponding Ls period of interest for a representative Mars year. For details of the simulations, please refer to Wang et al. [2023 submitted] listed in the References section.</p>
A subset of the EMARS dataset in MY24 and MY26 converted from the sigma-p hybrid coordinate to the pressure coordinate and a list of local dust storms detected during the MYs in western Arcadia Planitia
<p>This dataset includes a subset of EMARS' background mean data (Greybush et al., 2019) converted from the sigma-p hybrid coordinate to the pressure coordinate. Only MY24 and MY26 were used to generate the figures shown in Ogohara (submitted to JGR Planets). <br>Updates from the original EMARS are:</p> <ul> <li>The vertical coordinate has been converted from the sigma-p hybrid coordinate to the pressure coordinate.</li> <li>The variables expressing the Earth date (e.g., year, month, day, etc.) have been combined into one variable, earth_date.</li> <li>A new variable, emars_date, has been created from emars_sol and mars_hour.</li> </ul> <p>In addition, this dataset provides two lists of local dust storms events during MY24 and MY26 which were detected in western Arcadia Planitia using a deep learning-based method proposed by Ogohara and Gichu (2022). The lists are:</p> <ul> <li>[Data Set S1] List of global image swath files examined. Only file names of MGS/MOC red band images are listed. The list consists of 5 columns indicating image ID, observation date, orbit number, solar longitude, and filter name (RED).</li> <li>[Data Set S2] List of global image swath files containing identified dust storms, as well as some attributes of the detected dust storms. Only file names of red band images are listed. The list consists of 7 columns indicating image ID, observation date, orbit number, solar longitude, center longitude and latitude, and area (km2.)</li> </ul>
Dataset of "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" (2/2)
<p>This dataset contains the GrADS data of high-resolution Mars GCM results used for figures in the paper "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" by T. Kuroda, A.S. Medvedev and E. Yiğit.</p> <p>Each file contains two-dimensional (X: longitude, Y: latitude) data of surface pressure (Ps) and dust opacity in infrared wavelength (tau), and three-dimensional (X: longitude, Y: latitude, Z:sigma-level) data of temperature (T), zonal wind velocity (u), meridional wind velocity (v) and vertical wind velocity (w). Each tar.xz file contains snapshots of those data in every 1/6 Sol for Ls of 30 degrees. The dust scenario implemented for producing this dataset is taken from Montabone et al. (2020).</p> <p>data270rdc-my34.tar.xz: for Ls=270-300 (48 Sols)</p> <p>data300rdc-my34.tar.xz: for Ls=300-330 (51 Sols)</p> <p>data330rdc-my34.tar.xz: for Ls=330-360 (56 Sols)</p>
Dataset of "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" (1/2)
<p>This dataset contains the GrADS data of high-resolution Mars GCM results used for figures in the paper "Gravity Wave Activity in the Atmosphere of Mars During the 2018 Global Dust Storm: Simulations With a High-Resolution Model" by T. Kuroda, A.S. Medvedev and E. Yiğit.</p> <p>Each file with the name starting 'data' contains two-dimensional (X: longitude, Y: latitude) data of surface pressure (Ps) (unit: hPa) and dust opacity in infrared wavelength (tau), and three-dimensional (X: longitude, Y: latitude, Z:sigma-level) data of temperature (T) (unit: K), zonal wind velocity (u) (unit: m/s), meridional wind velocity (v) (unit: m/s) and vertical wind velocity (w) (unit: m/s), in snapshots of every 1/6 Sol for the periods of 30 degrees in Ls per a file as described below. The dust scenario implemented for producing this dataset is taken from Montabone et al. (2020), which is based on the observed dust opacity in Mars Year 24 (MY34).</p> <p>data180rdc-my34.tar.xz: for Ls=180-210 (49 Sols)</p> <p>data210rdc-my34.tar.xz: for Ls=210-240 (47 Sols)</p> <p>data240rdc-my34.tar.xz: for Ls=240-270 (46 Sols)</p> <p>The .tar.xz files can be extracted in Linux with 'tar Jxvf' command, and .grd and .ctl files with the same stem are generated.</p> <p>The file 'flux61ls5-my34.tar.xz' contains the three-dimensional fluxes and physical parameters calculated from the model output with the MY34 dust scenario. The contents are (T')^2, (u')^2, (v')^2, u'v', u'w', v'w' T(bar), u(bar), v(bar), squared Brunt-Vaisala frequency, and geopotential height. (bar) denotes the sum of the total wavenumber s=0-60 components, and the dash denotes the deviation from (bar), i.e. sum of the total wavenumber s=61-106 components. There are 36 time grids between Ls=182.5 and Ls=357.5 with the step of Ls=5 degrees. Kinetic and potential energies can be derived from these values using the formulae in the paper.</p> <p>The file 'flux61ls5-lowdust.tar.xz' is the same as 'flux61ls5-my34.tar.xz', except the model output with the 'low-dust' scenario (Kuroda et al., 2019; Kuroda, 2019a, 2019b).</p> <p>The file 'scripts.zip' contains the FORTRAN scripts to derive the fluxes and physical parameters equivalent to the file 'flux61ls5-my34.tar.xz' from the model outputs in this dataset and Kuroda (2020), i.e. data180rdc-my34.tar.xz, data210rdc-my34.tar.xz, data240rdc-my34.tar.xz, data270rdc-my34.tar.xz, data300rdc-my34.tar.xz and data330rdc-my34.tar.xz. Also, the fluxes and physical parameters equivalent to the file 'flux61ls5-lowdust.tar.xz' can be derived with those scripts from the model outputs data180rdc.tar.xz, data210rdc.tar.xz, data240rdc.tar.xz, data270rdc.tar.xz, data300rdc.tar.xz and data330rdc.tar.xz which are available in Kuroda (2019a, 2019b).</p>
Impact of a Bimodal Dust Distribution on the 2018 Martian Global Dust Storm with the NASA/Ames Mars Global Climate Model
<p>This dataset is simulation data from the NASA Ames Mars GCM, produced for the research article titled "Impact of a Bimodal Dust Distribution on the 2018 Martian Global Dust Storm with the NASA/Ames Mars Global Climate Model." It is presented in NetCDF format.</p>
Data in support of manuscript submitted to JGR-Planets "Strong variability of Martian water ice clouds during dust storms revealed from ExoMars Trace Gas Orbiter/NOMAD"
<p>These files contain data underlying figures for the submission version of:</p> <p>Strong variability of Martian water ice clouds during dust storms revealed from ExoMars Trace Gas Orbiter/NOMAD</p> <p>which was submitted to JGR: Planets for review. </p> <p>The access to the dataset will be closed until the final version of the paper is accepted.</p>
Study of fluid and dust field features during the sand and dust storms in the Qingtu Lake Observatory
<p>The dataset concludes the fluid and dust filed information used in the work.</p>
Data set for the manuscript "The Atmospheric Drivers of the Major Saharan Dust Storm in June 2020"
<p>This publication is under consideration at geophysical research letters.</p>
Data for "Martian oxygen and hydrogen upper atmospheres responding to solar and dust storm drivers: Hisaki space telescope observations"
<p>Data files (.npy) and python codes (.ipynb) to produce the figures in the paper.</p> <p>Download the zip file (dataforfigures_v2.zip), open plot_figX.ipynb with Jupyter notebook, and run it.</p> <p> </p>
In situ characterization of dust storms and their snow darkening effect over Himalayas
<p>The dataset provided here pertains to the Mukteshwar region. Users are requested to acknowledge the data curators before utilizing it.</p>
Effect of the 2018 Martian global dust storm on the main species in the upper ionosphere: observations and simulations
<p>This dataset contains the MCD outputs and photochemical calculation results for the manuscript "Effect of the 2018 Martian global dust storm on the main species in the upper ionosphere: observations and simulations"</p>
ground-based air quality measurements during the 2021 spring super dust storms
<p>The attachment stores the hourly ground-based PM10 concentration measurements from the China air quality monitoring network during the 2021 spring super dust storms. </p>
Quantifying the large-scale electrification equilibrium effects in dust storms using field observations at Qingtu Lake Observatory
<p>The measured divergence of the electric field, PM<sub>10</sub> concentration, ambient temperature and relative humidity time series during dust storms (at Qingtu Lake observatory) are given in these datasets.</p>
Mitigating the Health Effects of Desert Dust Storms Using Exposure-Reduction Approaches
ClinicalTrials.gov study NCT03503812. IPD Sharing: UNDECIDED. Countries: 3. Publications: 11.
Dust-storm forcing of Rossby waves on Mars
<p>These data files were used in the following journal article: </p> <p>D. Hinson and J. Wilson (2024). Dust-storm forcing of Rossby waves on Mars. Icarus 412, 115998.<em> </em><em>https://doi.org/10.1016/j.icarus.2024.115998</em></p>
Source contributions to two super dust storms over Northern China in March 2021 and the impact of soil moisture
Open the record for dataset details and reuse information.
Martian Dust Storms and Gravity Waves: Disentangling Water Transport to the Upper Atmosphere
<p>The data for JGR:Planets article figures.</p>
Climatological Monthly Frequency of Dust Storm over Land for Varying Intensities, Based on MODIS Terra Deep Blue Level 2 Aerosol Products MOD04_L2 Collection 6.1, on a Global 0.1 by 0.1 Degree Grid, Level 3 Version 1 (MODFDS_CLM_GLB_L3) at GES DISC
Version 1 is the current version of the dataset.This collection MODFDS_CLM_GLB_L3 provides level 3 climatological monthly frequency of dust storms (FDS) over land from 175°W to 175°E and 80°S to 80°N at a spatial resolution of 0.1˚ x 0.1˚. It is derived from Level 2, the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue aerosol products Collection 6.1 from Terra (MOD04_L2). The dataset is the climatological monthly mean for each month averaged over 2000 to 2022.The FDS is calculated as the number of days per month when the daily dust optical depth is greater than a threshold optical depth (e.g., 0.025) with two quality flags: the lowest (1) and highest (3). It is advised to use flag 1, which is of lower quality, over dust source regions, and flag 3 over remote areas or polluted regions. Eight thresholds (0.025, 0.05, 0.1, 0.25, 0.5, 0.75, 1, 2) are saved separately in eight files.If you have any questions, please read the README document first and post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).
Standard Deviation of Monthly Frequency of Dust Storm over Land for Varying Intensities, Based on MODIS Terra Deep Blue Level 2 Aerosol Products MOD04_L2 Collection 6.1, on a Global 0.1 by 0.1 Degree Grid, Level 3 Version 1 (MODFDS_SDV_GLB_L3) at GES DISC
Version 1 is the current version of the dataset.This collection MODFDS_SDV_GLB_L3 provides level 3 standard deviation of climatological monthly frequency of dust storms (FDS) over land from 175°W to 175°E and 80°S to 80°N at a spatial resolution of 0.1˚ x 0.1˚. It is derived from Level 2, the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue aerosol products Collection 6.1 from Terra (MOD04_L2). The dataset is the standard deviation of climatological monthly mean for each month over 2000 to 2022.The FDS is calculated as the number of days per month when the daily dust optical depth is greater than a threshold optical depth (e.g., 0.025) with two quality flags: the lowest (1) and highest (3). It is advised to use flag 1, which is of lower quality, over dust source regions, and flag 3 over remote areas or polluted regions. Eight thresholds (0.025, 0.05, 0.1, 0.25, 0.5, 0.75, 1, 2) are saved separately in eight files.If you have any questions, please read the README document first and post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).
Monthly Frequency of Dust Storm over Land for Varying Intensities, Based on MODIS Aqua Deep Blue Level 2 Aerosol Products MYD04_L2 Collection 6.1, on a Global 0.1 by 0.1 Degree Grid, Level 3 Version 1 (MYDFDS_MON_GLB_L3) at GES DISC
Version 1 is the current version of the dataset.This collection MYDFDS_MON_GLB_L3 provides level 3 monthly frequency of dust storms (FDS) over land from 175°W to 175°E and 80°S to 80°N at a spatial resolution of 0.1˚ x 0.1˚. It is derived from Level 2, the Moderate Resolution Imaging Spectroradiometer (MODIS) Deep Blue aerosol products Collection 6.1 from Aqua (MYD04_L2). The dataset covers the monthly mean from 2003 to 2022.The FDS is calculated as the number of days per month when the daily dust optical depth is greater than a threshold optical depth (e.g., 0.025) with two quality flags: the lowest (1) and highest (3). It is advised to use flag 1, which is of lower quality, over dust source regions, and flag 3 over remote areas or polluted regions. Eight thresholds (0.025, 0.05, 0.1, 0.25, 0.5, 0.75, 1, 2) are saved separately in eight files.If you have any questions, please read the README document first and post your question to the NASA Earthdata Forum (forum.earthdata.nasa.gov) or email the GES DISC Help Desk (gsfc-dl-help-disc@mail.nasa.gov).
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
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.