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1,574 results for “atmospheres”
Data for the publication "Impact of Isolated Atmospheric Aging processes on the Cloud Condensation Nucleiactivation of Soot Particles"
<p>The repository contains the data for the paper:</p> <p>Friebel, F., Lobo, P., Neubauer, D., Lohmann, U., Drossaart van Dusseldorp, S., Mühlhofer, E., and Mensah, A. A.: Impact of isolated atmospheric aging processes on the cloud condensation nuclei activation of soot particles, Atmos. Chem. Phys., 19, 15545–15567, https://doi.org/10.5194/acp-19-15545-2019, 2019.</p> <p>Note that the scripts to plot this data are to be found in the accompanying package (http://dx.doi.org/10.5281/zenodo.3452036)</p>
Data for the publication "Ozone concentration versus Temperature: Atmospheric aging of soot particles"
<p>The repository contains the data for the paper:</p> <p>Friebel, F and Mensah, A. A. Ozone concentration versus Temperature: Atmospheric aging of soot particles, <em>Langmuir </em> <strong>2019</strong>, 35, 45, 14437–14450 <a href="https://doi.org/10.1021/acs.langmuir.9b02372">https://doi.org/10.1021/acs.langmuir.9b02372</a></p>
Freshwater transport from the Atlantic to the Pacific Ocean through the atmosphere
<p>emp_eul_lagrang.mat: This mat file contains global evaporation - precipitation (E-P) data obtained from ERA-Interim, Eulerian and Lagrangian method. The validation of the atmospheric water-mass version of TRACMASS had been done using this data.</p> <p>psixz_ep_eastward.zip: This zip file contains 12 netcdf files corresponding to each month. These files were used to compute E - P, atmospheric freshwater transport as a function of month and atmospheric zonal water-mass stream function for the water-mass traveling from the Atlantic to Pacific Ocean over Afro-Eurasia. </p> <p>psixz_ep_westward.zip: Same as above but for the water-mass traveling from the Atlantic to Pacific Ocean over America. </p> <p>resdt_eastward.nc: This netcdf file consists of atmospheric water residence time extracted from water-mass trajectories traveling from the Atlantic to Pacific Ocean over Afro-Eurasia.</p> <p>resdt_westward.nc: Same as above but from water-mass trajectories traveling from the Atlantic to Pacific Ocean over America.</p> <p>resdt_all.nc: This netcdf file consists of atmospheric water residence time computed from water-mass trajectories traveling from the Atlantic to Pacific Ocean regardless of its pathway (Eastward over Afro-Eurasia + Westward over America).</p> <p>resdt_all_global.nc: This netcdf file consists of global atmospheric water residence time calculated from the global TRACMASS run.</p> <p> </p>
The dataset for the paper titled "Convergence of convective updraft ensembles with respect to the grid spacing of atmospheric models" by Sueki et al.
<p>This repository contains data, analysis codes, and model configuration files for NICAM and SCALE-RM, which is used for the paper titled "Convergence of convective updraft ensembles with respect to the grid spacing of atmospheric models" by Sueki et al.</p> <p>There are 6 fortran codes at the top directory.<br> 1. deep_convective_area.f90<br> 2. algorithm01.f90<br> 3. algorithm02.f90<br> 4. statistics_algorithm01.f90<br> 5. statistics_algorithm02.f90<br> 6. spectrum_calculation.f90<br> You can find description for each code at the top it.</p> <p>All data are archived in subdirectories. Directory tree is following:</p> <p>Sueki-et-al.2020/<br> |-- exp-a<br> | |-- dx0200<br> | | |-- algorithm01<br> | | `-- algorithm02<br> | | |-- r00000-00500<br> | | |-- r00500-01000<br> | | |-- r01000-02000<br> | | |-- r02000-04000<br> | | |-- r04000-08000<br> | | `-- r08000-99999<br> | |-- dx0400<br> | | |-- algorithm01<br> | | `-- algorithm02<br> | | |-- r00000-00500<br> | | |-- r00500-01000<br> | | |-- r01000-02000<br> | | |-- r02000-04000<br> | | |-- r04000-08000<br> | | `-- r08000-99999<br> | |-- dx0800<br> | | |-- algorithm01<br> | | `-- algorithm02<br> | | |-- r00000-00500<br> | | |-- r00500-01000<br> | | |-- r01000-02000<br> | | |-- r02000-04000<br> | | |-- r04000-08000<br> | | `-- r08000-99999<br> | |-- dx1600<br> | | |-- algorithm01<br> | | `-- algorithm02<br> | | |-- r00000-00500<br> | | |-- r00500-01000<br> | | |-- r01000-02000<br> | | |-- r02000-04000<br> | | |-- r04000-08000<br> | | `-- r08000-99999<br> | `-- dx3200<br> | |-- algorithm01<br> | `-- algorithm02<br> | |-- r00000-00500<br> | |-- r00500-01000<br> | |-- r01000-02000<br> | |-- r02000-04000<br> | |-- r04000-08000<br> | `-- r08000-99999<br> |-- exp-b<br> | |-- dx0050<br> | | |-- algorithm01<br> | | `-- algorithm02<br> | | |-- r00000-00500<br> | | |-- r00500-01000<br> | | |-- r01000-02000<br> | | |-- r02000-04000<br> | | |-- r04000-08000<br> | | `-- r08000-99999<br> | |-- dx0100<br> | | |-- algorithm01<br> | | `-- algorithm02<br> | | |-- r00000-00500<br> | | |-- r00500-01000<br> | | |-- r01000-02000<br> | | |-- r02000-04000<br> | | |-- r04000-08000<br> | | `-- r08000-99999<br> | |-- dx0200<br> | | |-- algorithm01<br> | | `-- algorithm02<br> | | |-- r00000-00500<br> | | |-- r00500-01000<br> | | |-- r01000-02000<br> | | |-- r02000-04000<br> | | |-- r04000-08000<br> | | `-- r08000-99999<br> | |-- dx0400<br> | | |-- algorithm01<br> | | `-- algorithm02<br> | | |-- r00000-00500<br> | | |-- r00500-01000<br> | | |-- r01000-02000<br> | | |-- r02000-04000<br> | | |-- r04000-08000<br> | | `-- r08000-99999<br> | `-- dx0800<br> | |-- algorithm01<br> | `-- algorithm02<br> | |-- r00000-00500<br> | |-- r00500-01000<br> | |-- r01000-02000<br> | |-- r02000-04000<br> | |-- r04000-08000<br> | `-- r08000-99999<br> |-- model-configuration-file<br> | |-- nicam<br> | `-- scale-rm<br> | |-- exp-a<br> | | |-- init<br> | | |-- pp<br> | | `-- run<br> | `-- exp-b<br> | |-- init<br> | |-- pp<br> | |-- run01<br> | |-- run02<br> | `-- run03<br> |-- spectrum<br> `-- statistics</p>
Accelerometer-Derived Atmospheric Density from the CHAMP and GRACE Satellites
<p>Archive of the thermospheric density dataset (including estimated uncertainties) for CHAMP and GRACE satellite accelerometer missions, first released in February of 2011, with an extension of GRACE density data in 2012. </p>
manuscript (atmosphere-3145955) titled: The Black Sea Upwelling System: Analysis on the Western Shallow Waters Authored by: Maria Emanuela Mihailov was accepted in Atmosphere (ISSN 2073-4433) on 15 August 2024
<p>Datasets represents the modelling results for:</p> <p>- Coastal Upwelling Transport Index (CUTI) of the National Oceanic and Administrative Administration’s Environmental Research Division (NOAA-ERD) was used to derive the time series of the coastal upwelling index in four locations on the north-western Black Sea coast. The coastal upwelling index time series was calculated using monthly average wind fields from the European Centre for Medium-Range Weather Forecasts (ECMWFs) reanalysis and MATLAB software to compute the CUTI </p> <p>- The upwelling index (UI) is computed using the CUTI Formula (<span>Bakun Index </span>), defined as CUTI (m3·s−1·100 m−1), representing the volume transport per distance unit of an alongshore section. The sign of Ekman transport is changed to define positive or negative values of UI as a response to upwelling or downwelling favourable winds.<br>To compute the BEUTI, Copernicus Marine Service [1] data are used for dedicated locations.</p> <p>[1] <span>Grégoire,<em> </em>M.;<em> </em>Vandenbulcke,<em> </em>L.;<em> </em>Capet,<em> </em>A.<em> </em>Black<em> </em>Sea<em> </em>Biogeochemical<em> </em>Reanalysis<em> </em>(CMEMS<em> </em>BS-Biogeochemistry)<em> </em>(Version<em> </em>1)<em> </em>set.<em> </em>Copernicus<em> </em>Monitoring<em> </em>Environment<em> </em>Marine<em> </em>Service<em> </em>(CMEMS).<em> </em>2020.<em> </em>Available<em> </em>online:<em> </em>https://marine.copernicus.eu/<em> </em>(accessed<em> </em>on<em> </em>10<em> </em>November<em> </em>2023).</span></p>
Figure 2 in Atmospheric Disturbances in the Airflow around Mountains and the Problem of Flight Safety in the Mountains of the Republic of Adygeya
Figure 2. Pattern of the Mount Fisht airflow for the model (a) – scenario I (U=15 m/s), (b) – scenario II (U=19 m/s) and (c) – scenario III (U=22 m/s).
Figure 1 in Atmospheric Disturbances in the Airflow around Mountains and the Problem of Flight Safety in the Mountains of the Republic of Adygeya
Figure 1. Lago-Naki Plateau. The dashed line shows the cross-section of the terrain through Mount Fisht for model calculations.
Observational atmospheric angular momentum.
<p>Reanalyses of Earth's angular momentum from ERA data. Data are divided by 1.0e+24.<br>Each row consists of 12 monthly means.<br>Data are given for each of 324 latitudes, starting near the S pole.<br>Data are given for each year from 1960, starting from November 1960 to match model predictions.</p>
Data and code for the publication: "Unexpected anthropogenic emission decreases explain recent atmospheric mercury concentration declines"
<p>A. Feinberg, Aug 2024</p> <p>arifeinberg@gmail.com</p> <p> </p> <p>Essential data and code for the publication: Feinberg et al. : Unexpected anthropogenic emission decreases are required to explain recent atmospheric mercury concentration declines</p> <p> </p> <p>The directories include:</p> <p>1) analysis<strong>_</strong>plotting<strong>_</strong>scripts/ - all analysis scripts used to analyze observations, produce input data, and plot figures for paper</p> <p>2) GC<strong>_</strong>code/ - Archived GEOS-Chem code used to simulate the runs in this paper</p> <p>3) GC<strong>_</strong>data/ - GEOS-Chem simulation data and run scripts can be found here for the following runs:</p> <p>BASE - run2021</p> <p>BASE+LEG - run2022</p> <p>DEC<strong>_</strong>LEG<strong>_</strong>ONLY - run2024</p> <p>ZHANG23 - run2025</p> <p>DEC<strong>_</strong>ANT<strong>_</strong>NH - run2026</p> <p> </p> <p>4) input<strong>_</strong>data/ - input data used to run GEOS-Chem</p> <p> </p> <p>Please refer to other README.md files within sub-directories and contact me for any questions.</p>
Rock thermal conductivity and thermal inertia measurements under martian atmospheric pressures - Data
<p>Dataset accompanying the paper "Rock thermal conductivity and thermal inertia measurements under martian atmospheric pressures" published in Icarus in September 2024. </p> <p>Files included: </p> <p>Individual photos of the rocks in this study's sample suite; photos of laboratory equipment and setup; raw emissivity spectral data, emissivity specta plotted; thermal inertia data collected with the MTPS sensor; thermal conductivity data collected with the TPS sensor; itemized calculations of thermophysical data error; sample mineral abundance data; sample major/trace element measurement data; and detailed sample porosity data. </p> <p> </p>
Global FLEXPART-ERA5 simulations using 30 million atmospheric parcels since 1980
<h2><strong>Abstract</strong></h2> <p>This database compiles the outputs of the global experiment performed with the Lagrangian particle dispersion model FLEXPART since 1980. The experiment was conducted using the ERA5 reanalysis data provided by the European Centre for Medium-Range Weather Forecasts (ECMWF) and homogeneously dividing the atmosphere into 30 million particles. The database can be used to investigate global moisture and heat transport and to establish sink-source relationships.</p> <h2><strong>Input data</strong></h2> <p>The data employed for FLEXPART running was the ERA5 reanalysis dataset from the ECMWF (Hersbach et al., 2020). To feed the model, the input data was downloaded and pre-processed by using the software Flex_extract v7.1 (Tipka et al., 2020).</p> <p>The original available ERA5 resolution is 0.1-degree and 1-hour. For this experiment, ERA5 input data was retrieved for the global area (90ᵒS to 90ᵒN and 180ᵒW to 180ᵒE) at a 0.5-degree horizontal resolution for 137 level from the surface to 1 hPa and a 3-hour temporal resolution (00, 03, 06, 09, 12, 15,18 and 21 UTC).</p> <p>The data is stored in individual GRIB files for each time step, following the name criteria "EAYYMMDDHH". The size of each file is approximately 530 MB. The variables included in each file are: temperature, specific humidity, u- and v-wind components, Eta-coordinate vertical velocity, divergence, specific cloud liquid water content, specific cloud ice water content, and the logarithm of surface pressure on model levels; and 2m temperature an dew-point temperature, 10m u and v wind component, geopotential, land-sea mask, mean sea level pressure, snow depth, the standard deviation of orography, surface pressure, total cloud cover, convective precipitation, large-scale precipitation, surface sensitive heat flux, eastward and northward turbulent surface stress and surface net solar radiation at the surface level.</p> <h2><strong>Software and running</strong></h2> <p>The software used for the simulations is the Lagrangrian particle dispersion model FLEXPART on version 10.4 (Pisso et al., 2019). The software is configured for a global experiment, and the simulations were obtained from 1980 to the present with a temporal resolution of 3-h. For the experiment, 30 million particles were homogeneously distributed on the global area, and their trajectories were followed according to the model configuration specified in the COMMAND and RELEASES files. The complete period is distributed in individual annual experiments, with each annual experiment obtained continuously running the model from October of the previous year to December of that year.</p> <h2><strong>Outputs characteristics</strong></h2> <p>The outputs were stored in individual GRIB files for each time step, with the file name following the naming convention "partposit_YYYYMMDDHH". Each file has a size of 1,76 GB, and the total size of the annual experiment is 6 TB. Each file contains information about each particle of the experiment: the particle identification number (particle ID), the particle's position (latitude, longitude, and altitude), topographic height, potential vorticity, specific humidity, air density, atmospheric boundary layer height, and temperature. The file corresponding to the 1st January 2023 at 00UTC is provided in this repository as an example. Due to the size of each file, the complete dataset is accessible by personal contact (see <em>Data Access</em> section). </p> <h2><strong>Post-process and applications</strong></h2> <p>The dataset presented here allows for the analysis of moisture and heat transport in the atmosphere for any region of the world up to 3-h temporal resolution and different horizontal resolutions. The transport may be established between sources and sinks, both in a forward or backward tracking in time. Currently, two open-source post-processing options developed within the EPhyslab-UVigo group are available for the analysis of these data: TROVA (Fernadez-Alvarez et al., 2022) and LATTIN (Perez-Alarcón et al., 2024) with different moisture tracking calculation options, and the latter including tools for heat transport analysis. Both options allow different methodologies (those most widely used) for the moisture transport analysis. The studies can be configured for any region of the planet, specifying it by a NetCDF 2-D mask, and the moisture transport can be set for different time periods (from 1 to 15 days, being from 8 to 10 days the periods most commonly applied according to the mean residence time of water vapor in the atmosphere). For further discussion on the residence time of water vapor in the atmosphere and its application for Lagrangian studies see Gimeno et al. (2021) and Nieto and Gimeno (2019).</p> <h2><strong>Example of application</strong></h2> <p>J. C. Fernández-Álvarez, M. Vázquez, A. Pérez-Alarcón, R. Nieto, L. Gimeno (2023) Comparison of moisture sources and sinks estimated with different versions of FLEXPART and FLEXPART-WRF models forced with ECMWF reanalysis data, Journal of Hydrometeorology, doi: 10.1175/JHM-D-22-0018.1.</p> <p>A. Pérez-Alarcón, R. Sorí, M. Stojanovic, M. Vázquez, R.M. Trigo, R. Nieto, L. Gimeno (2024) Assessing the Increasing Frequency of Heat Waves in Cuba and Contributing Mechanisms, Earth Systems and Environment, DOI: 10.1007/s41748-024-00443-8</p> <h2><strong>Validation</strong></h2> <p>The moisture transport analysis provided by this dataset was validated by Fernández-Alvarez et al. (2023) through an in-depth comparison with different versions of the model, horizontal resolutions and input data, including the ERA-Interim reanalysis from the ECMWF, which has been widely used for this purpose over the past decades.</p> <h2><strong>Data Access</strong></h2> <p>Data access is available by contacting the EPhysLab group via: rnieto[at]uvigo.gal or l.gimeno[at]uvigo.gal</p>
Dataset used in JGR-Atmospheres manuscript 2024JD041159
<p>Final processed data used to generate all figures in the accepted manuscript (2024JD041159) to Journal of Geophysical Research - Atmospheres.</p> <p>This repository contains a 21-year database of mesoscale convective systems tracked over northwestern South America using the new ATRACKCS algorithm.</p> <h2>General description of MCS database</h2> <p>The MCS_Climatology_NwSA file contains all convective systems tracked from 2001 to 2021. Below is a brief description of its columns:</p> <p><strong>- track_id:</strong> Track identifier. Each track contains a list of polygons representing the convective system at different time steps.</p> <p><strong>- polygon_id:</strong> Identifier of the polygon that represents a convective system at a given time and location.</p> <p><strong>- time:</strong> Date and time of detection of the convective system.</p> <p><strong>- geometry:</strong> Geometry (and location) of the polygon representing the convective system at a given time step.</p> <p><strong>- area_tb:</strong> Area in km2.</p> <p><strong>- centroid:</strong> Location of the centroid of the convective system.</p> <p><strong>- mean_tb:</strong> Mean brightness temperature of the convective system.</p> <p><strong>- mean_p:</strong> Mean precipitation rates of the convective system.</p> <p><strong>- max_p:</strong> Max value of precipitation of the convecive system.</p> <p><strong>- intersection_percentage:</strong> Percentage of interception between a convective system at time i and the same convective system at time i+1.</p> <p><strong>- distance:</strong> Distance between consecutive convective systems.</p> <p><strong>- direction:</strong> Direction of propagation of the convective system.</p> <p><strong>- total_duration:</strong> Duration of the entire track in hours.</p> <p><strong>- total_distance:</strong> Total distance of the entire track in km.</p> <p><strong>- mean_velocity:</strong> Mean velocity of the entire track in km/h.</p>
ERA5 atmospheric stability and Geostrophic wind shear for usage in WAsP
<p>The dataset "ERA5-meso.nc" is obtained by loading the variables needed for calculating the temperature scale from hourly ERA5 files. These files are available in grib format that have been obtained from the Copernicus Data Store (CDS). They are opened using xarray and cfgrib and processed using the functions stability_histogram from the python package PyWAsP. Because a conditional mean based on the 50% highest wind speeds must be calculated, all values are binned according to wind speed at 100 m and this histogram is then used to calculate the mean and root-mean-square of the temperature scale. The boundary layer height scale is calculated in a similar fashion. For more documentation see the accompanying paper. A validation of the WAsP model using these data is available in the references.</p> <p>The file "ERA5-baro.nc" contains the geostrophic wind shear. The mean magnitude and direction is obtained sector-wise in similar fashion as described above. The geostrophic wind shear can be calculated from the pressure level geopotential height. The way to do this is described here:</p> <p><a href="https://orbit.dtu.dk/en/publications/implementation-of-large-scale-average-geostrophic-wind-shear-in-w" target="_blank" rel="noopener">https://orbit.dtu.dk/en/publications/implementation-of-large-scale-average-geostrophic-wind-shear-in-w</a></p> <p>These data are for estimating atmospheric stability conditions, if you are looking for data to estimate air density, please refer to the item "ERA5 data for air density calculations in WAsP" (related materials item 5). The methods for this are described in related materials item 7.</p> <p>v1-v2: Version corresponding to paper before review (related materials 3), do not use these.</p> <p>v3: Final version that corresponds to the published version of the paper (related materials 6):<br>https://doi.org/10.1007/s10546-023-00803-3<br>This is slightly different then the first version due to Eq. 9</p> <p>v4: Updates to load the files using PyWAsP versions specifically suited for use in pywasp with the variable names adopted in PyWAsP. For ERA5-baro.nc NaNs are filled with 0.0, i.e. assuming a barotropic atmosphere.</p> <p>Mirror of: https://data.dtu.dk/articles/dataset/ERA5_atmospheric_stability_for_usage_in_WAsP_12_8/19576042</p>
Atmospheric moisture recycling in Mediterranean-type climate regions across the world
<p>Please cite the corresponding manuscript when using this data:</p> <p>... (information will follow as soon as the manuscript is published)</p> <p>This dataset includes the local precipitation recycling ratios and the regional moisture recycling ratios for five major Mediterranean-type climate regions across the globe. Below we list these five regions and explain the concepts of local precipitation recycling and regional moisture recycling. </p> <p> </p> <p><strong>Mediterranean-type climate regions</strong></p> <p>Region 1: South West Australia</p> <p>Region 2: West coast of the US (California)</p> <p>Region 3: Central Chile</p> <p>Region 4: Mediterranean Basin (region around the Mediterranean Sea)</p> <p>Region 5: The Cape region of South Africa</p> <p> </p> <p><strong>Local precipitation recycling ratio</strong></p> <p>The local precipitation recycling ratio is the fraction of precipitation that originated within approximately 50 km from where it rains out, i.e., it evaporated from the grid cell where it rains out and the 8 surrounding grid cells. The grid cells have a resolution of 0.5DEGx0.5DEG. A more detailed explanation is provided in the journal article Theeuwen et al. (2024). </p> <p>The files that include local precipitation recycling ratios are:</p> <table> <tbody> <tr> <td><strong>File name</strong></td> <td><strong>Study region</strong></td> <td><strong>Time dimension (month)</strong></td> <td><strong>Latitude range</strong></td> <td><strong>Longitude range</strong></td> </tr> <tr> <td>PLMR_SWAustralia.nc</td> <td>South West Australia</td> <td>January-December</td> <td>-15:-48 DEGN</td> <td>106:154 DEGE</td> </tr> <tr> <td>PLMR_California.nc</td> <td>West coast of the US (California)</td> <td>January-December</td> <td>52:20 DEGN</td> <td>-131:-105 DEGE</td> </tr> <tr> <td>PLMR_CentralChile.nc</td> <td>Centra Chile</td> <td>January-December</td> <td>-10:-54 DEGN</td> <td>-80:-60 DEGE</td> </tr> <tr> <td>PLMR_Med_Basin.nc</td> <td>Mediterranean Basin</td> <td>January-December</td> <td>48:23 DEGN</td> <td>-20:-45 DEGE</td> </tr> <tr> <td>PLMR_SWCapeSA.nc</td> <td>The Cape region of South Africa</td> <td>January-December</td> <td>-26:-40 DEGN</td> <td>10:38 DEGE</td> </tr> </tbody> </table> <p> </p> <p><strong>Regional moisture recycling ratio</strong></p> <p>The regional moisture recycling ratio data includes both regional evaporation recycling ratios as well as regional precipitation recycling ratios. </p> <p>The regional evaporation recycling ratio is the fraction of evaporated water that rains out within the Mediterranean region it evaporated from. </p> <p>The regional precipitation recycling ratio is the fraction of precipitation that originated from the Mediterranean region it rains out in. </p> <p>This data has a resolution of 0.5DEGx0.5DEG and is a multi-year average (years: 2008-2017). A more detailed description is provided in the journal article Theeuwen et al. (2024). </p> <table> <tbody> <tr> <td><strong>Filename</strong></td> <td><strong>Type of recycling</strong></td> <td><strong>Study region</strong></td> </tr> <tr> <td>ERMR_SWAustralia.nc</td> <td>Regional evaporation recycling</td> <td>South West Australia </td> </tr> <tr> <td>ERMR_California.nc</td> <td>Regional evaporation recycling</td> <td>West coast of the US (California)</td> </tr> <tr> <td>ERMR_CentralChile.nc</td> <td>Regional evaporation recycling</td> <td>Centra Chile</td> </tr> <tr> <td>ERMR_Med-Basin.nc</td> <td>Regional evaporation recycling</td> <td>Mediterranean Basin</td> </tr> <tr> <td>ERMR_CapeSA.nc</td> <td>Regional evaporation recycling</td> <td>The Cape region of South Africa</td> </tr> <tr> <td>PRMR_SWAustralia.nc</td> <td>Regional precipitation recycling</td> <td>South West Australia </td> </tr> <tr> <td>PRMR_California.nc</td> <td>Regional precipitation recycling</td> <td>West coast of the US (California)</td> </tr> <tr> <td>PRMR_CentralChile.nc</td> <td>Regional precipitation recycling</td> <td>Centra Chile</td> </tr> <tr> <td>PRMR_Med-Basin.nc</td> <td>Regional precipitation recycling</td> <td>Mediterranean Basin</td> </tr> <tr> <td>PRMR_CapeSA.nc</td> <td>Regional precipitation recycling</td> <td>The Cape region of South Africa</td> </tr> </tbody> </table>
Processed data used for JGR publication "Role of Midwater Mixed Waves in the Loop Current Separation Events from A Coupled Ocean-Atmosphere Regional Model and In-Situ Observations"
<p>This is the processed dataset used in the JGR publication "Role of Midwater Mixed Waves in the Loop Current Separation Events from A Coupled Ocean-Atmosphere Regional Model and In-Situ Observations" by Xiao Ge.</p> <p>Please contact the author (gexiao@tamu.edu) for all the original/processed outputs of R-CESM, and use the following original papers as citations.</p> <p>The dataset used in this research includes:</p> <p>1. Loop Current Dynamics 2009-2011: LC_*.nc is the processed (reorganized) data for each in-situ station, * represents their station ID</p> <ul> <li>https://digital.library.unt.edu/ark:/67531/metadc955416/</li> <li>https://www.sciencedirect.com/science/article/pii/S0377026516301348?via%3Dihub</li> <li>https://search.dataone.org/view/%7BBD2513E6-3B34-4B7C-BCB9-3C4ED5E8D0FB%7D</li> </ul> <p>2. Regional Community Earth System Model, R-CESM: <a href="https://zenodo.org/api/records/13932074/draft/files/h.nc/content" target="_blank" rel="noopener noreferrer">h.nc</a> is the bathymetry data of R-CESM; cmpr_*.nc files are provided as examples of the original R-CESM outputs; pvsf_prho_*.nc are the processed (subsampled at the target region and interpolated on potential density layers, derived stream function, potential vorticity, and relative vorticity) R-CESM outputs used in this research; and <a href="https://zenodo.org/uploads/13932074" target="_blank" rel="noopener noreferrer">LC_pv_40hlp_2013.nc</a> is the example of organized processed R-CESM (pvsf_prho_*.nc files) containing potential vorticity and relative vorticity for figures plotting</p> <ul> <li>https://journals.ametsoc.org/view/journals/bams/102/9/BAMS-D-20-0024.1.xml?tab_body=fulltext-display</li> </ul> <p> </p> <p> </p> <p> </p> <p> </p>
GNSS deep SNR retrievals of marine atmosphere boundary layer (MABL) specific humidity
<p>This folder contains 5 prediction files ended with *_v2.h5. These files can be loaded using the provided code "prediction_data_loader.py". All variables are in their respective physical units.</p> <p>The *.tgz file contains the training and validation codes as well as sample training and validation datasets from METOP-B satellite. Please refer to the paper for details. All variables had been normalized in the training and validation datasets so no real physical meaning attached.</p> <p>Reference:</p> <p><a href="https://publications.copernicus.org/">Gong, J., Wu, D. L., Badalov, M., Ganeshan, M., and Zheng, M.: A machine-learning-based marine atmosphere boundary layer (MABL) moisture profile retrieval product from GNSS-RO deep refraction signals, Atmos. Meas. Tech., 18, 4025–4043, https://doi.org/10.5194/amt-18-4025-2025, 2025.</a></p> <p> </p> <p>POC: Jie.Gong@nasa.gov</p> <p>10/17/2024</p> <p> </p> <p>Update on 08/27/2025: The final paper has been published on AMT. Please see updated reference information above.</p> <p>--- THE END ---</p>
Global atmospheric particle formation from CERN CLOUD measurements: nucleation rate dataset
<p>Data S1 for paper "Dunne et al, (the CLOUD collaboration), Global atmospheric particle formation from CERN CLOUD measurements, Science 354 6316 (2016). Data contains nucleation rates and chamber conditions (including precursor gas concentrations) for inorganic binary and ternary (H2SO4-H2O) and (H2SO4-NH3-H2O) neutral and ion-induced nucleation measurements presented in the paper. These data were originally attached as supplemental to the paper, but are not currently available on the Science website (as of October 2024). </p> <p>Relative humidity units are percent. Care is needed to interpret all the data, for example not all ammonia values quoted were measured directly, some are inferred. See the supplementary materials of the paper for more discussion, and users of data at relative humidity other than (38+/-5)% are advised to discuss with the contact author Hamish Gordon. Ion production rate and nucleation rate units are per cm3 per second. The sulfuric acid units can be interpreted by noting that the value of 632 in the first data entry is 6.32x10^8 cm-3. </p> <p>Please cite the original Science article if you use these data: https://www.science.org/doi/10.1126/science.aaf2649</p>
Relative Random Errors in the Convective Atmospheric Boundary Layer Estimated by the Relaxed Filtering Method from Large Eddy Simulations
<p>Data supporting the paper "How representative are uncrewed aircraft system measurements of the convective boundary layer?" by Brian R. Greene, Leia M. Otterstatter, and Scott T. Salesky, submitted to Geophysical Research Letters in 2024. Data are postprocessed from large-eddy simulations of the convective atmospheric boundary layer that are used to produce the figures within the paper. Details on the production of these files are included in the supplementary informatin of this paper.</p>
Main output data used in "Exploring the Greenland Ice Sheet's response to future atmospheric warming-threshold scenarios over 200 years" (Delhasse et al., 2025)
<p>Outputs used in:</p> <p>Delhasse, A., Kittel, C. and Beckmann, J.: Exploring the Greenland Ice Sheet's response to future atmospheric warming-threshold scenarios over 200 years, EGUsphere [preprint], https://doi.org/10.5194/egusphere-2025-709, 2025.</p> <p>Each MAR-PISM coupling experiment (1991-2200) is related to the Greenland warming over a 10-year period compared to our reference period (1961-1990) at which climate is stabilized until 2200. The last experiment is the Reverse one, where the climate is year by year reversed after 2100 to go back to 2000-climate as forcing in 2200, the last year of the simulation. Please refer to Delhasse et al. (2024) for the coupling description.</p> <div> <table> <tbody> <tr> <th> <p>Experiment </p> </th> <th> <p>Exact Greenland warming at 600hPa (°C)</p> </th> <th> <p>10-years period</p> </th> </tr> </tbody> <tbody> <tr> <td> <p>CTRL</p> </td> <td> <p>+0.00</p> </td> <td> <p>1961-1990</p> </td> </tr> <tr> <td> <p>+1</p> </td> <td> <p>+1.04</p> </td> <td> <p>1995-2004</p> </td> </tr> <tr> <td> <p>+1.5</p> </td> <td> <p>+1.51</p> </td> <td> <p>2010-2019</p> </td> </tr> <tr> <td> <p>+2</p> </td> <td> <p>+2.04</p> </td> <td> <p>2021-2030</p> </td> </tr> <tr> <td> <p>+3</p> </td> <td> <p>+2.98</p> </td> <td> <p>2040-2049</p> </td> </tr> <tr> <td> <p>+4</p> </td> <td> <p>+4.04</p> </td> <td> <p>2058-2067</p> </td> </tr> <tr> <td> <p>+5</p> </td> <td> <p>+5.00</p> </td> <td> <p>2074-2083</p> </td> </tr> <tr> <td> <p>+6</p> </td> <td> <p>+5.96</p> </td> <td> <p>2083-2092</p> </td> </tr> <tr> <td> <p>+7</p> </td> <td> <p>+6.85</p> </td> <td> <p>2091-2100</p> </td> </tr> </tbody> </table> </div> <p><strong>Table 1. Greenland warmings at 600hPa since 1961-1990 used to define our experiments and the corresponding 10-years periods over which warmings are determined. </strong></p> <p>For each experiment, 3 types of output are available (where <em>EXP</em> corresponds to the name of the experiment as referenced in Table 1) : </p> <ul> <li> <p>EXP-PISM-thk-msk-1991-2200.nc: contain yearly ice thickness (THK) and ice mask (MASK) as simulated by PISM (PISM grid, 4.5 km);</p> </li> <li> <p>EXP-SMB-ME-RU-MAPI-CESM2-1991-2200.nc: contain yearly SMB (surface mass balance), ME (melt), and RU (runoff) on the MAR grid (25 km);</p> </li> <li> <p>EXP-ts-MB-D-SMB-1991-2200.nc: contain time series of the total MB (mass balance), D (discharge), and SMB (surface mass balance) integrated over the all ice sheet mask from PISM.</p> </li> </ul> <p>The MAR code used in this dataset is tagged as v3.11.3 on https://gitlab.com/Mar-Group/MARv3/-/tree/v3.11.3 (last access: 24 October 2024) (MARTeam, 2024). The PISM code used is tagged as PISMv1.2.2 on <a href="https://github.com/pism/pism/releases/tag/v1.2.2">https://github.com/pism/pism/releases/tag/v1.2.2</a> (last access: 24 October 2024).</p> <p>If you need other variables from MAR or PISM, send us an email (alison.delhasse@uliege.be) and we will be glad to help you. We will also be happy to share the scripts we have developed to analyze the outputs and make the figures in this paper if needed. Please cite the paper if you use these MAR-PISM outputs.<br><br><strong><em>Data usage notice:</em></strong></p> <p>If you use any of these results, please acknowledge the work of the people involved in producing them. Acknowledgments should be similar to the one below that contains information related to MAR and PISM. To document MAR scientific impact and enable ongoing support of the model, users are likely encouraged to contact me to add their works to the list of MAR-related publications. </p> <p>"We thank A. Delhasse, C. Kittel, and J. Beckmann, as well as the MAR and PISM teams which make available the model outputs. We also thank agencies (F.R.S - FNRS, CÉCI, and the Walloon Region) that provided computational resources for MAR-PISM simulations. "</p> <p>You should also refer to and cite the following paper in its latest version:</p> <p>Delhasse, A., Kittel, C. and Beckmann, J.: Exploring the Greenland Ice Sheet’s response to future warming-threshold scenarios over 200 years, [JOURNAL UNDER REVIEW], 2024.</p> <p><strong><em>References</em></strong></p> <p>Delhasse, A., Beckmann, J., Kittel, C., and Fettweis, X.: Coupling MAR (Modèle Atmosphérique Régional) with PISM (Parallel Ice Sheet Model) mitigates the positive melt–elevation feedback, The Cryosphere, 18, 633–651, https://doi.org/10.5194/tc-18-633-2024, 2024.</p> <p>MARTeam: MARv3.11, GitLab [data set], <a href="https://gitlab.com/Mar-Group/MARv3">https://gitlab.com/Mar-Group/MARv3#</a> (last access: 24 October 2024), 2024.</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.