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1,574 results for “Atmosphere”
Long-term Atmospheric, Soil and Water Sensor Data from the GCE-LTER Eddy Covariance Flux Tower on Sapelo Island, Georgia
Long-term measurements of various atmospheric, soil and water properties were made using electronic sensors attached to the GCE-LTER eddy covariance flux tower deployed in a Spartina alterniflora salt marsh on Sapelo Island, Georgia. Variables measured include air and water temperature, relative humidity, precipitation, wind speed and direction, soil temperature, water pressure and solar radiation components (i.e. incident and reflected photosynthetically available, total, long-wave and shortwave radiation). Measurements were logged at 5 minute intervals using multiple Campbell Scientific Instruments CR3000 data loggers, and then combined into a single monotonic time series data set. Quality control analyses were performed to remove values deemed invalid due to sensor failure or miscalibration and to assign Q/C qualifiers to values outside expected ranges or failing various sanity and quality checks of the data. Note that some measurements were spatially replicated with multiple sensors deployed in different micro-habitats (e.g. at the tower and in a nearby marsh platform or creek). Sensors were also added to the tower at various times after the initial installation, therefore some variables do not span the entire period of record. Measurements at this site are ongoing, and the data set will be updated annually to include additional observations.
A Global database of methane concentrations and atmospheric fluxes for streams and rivers
This dataset, referred to as MethDB, is a collation of publicly available values of methane (CH4) concentrations and atmospheric fluxes for world streams and rivers, along with supporting information on location, geographic, physical, and chemical conditions of the study sites. The data set is composed of four linked tables, corresponding to the data sources (Papers_MethDB), the study sites (Sites_MethDB), concentrations (Concentrations_MethDB), and influx/efflux rates (Fluxes_MethDB). Information was extracted from journal articles, government reports, book chapters, and similar sources that were acquired before 15 September 2015. Concentrations and fluxes were converted to a standard unit (micromoles per liter for concentration and millimoles per square meter per day for flux) and both the author-reported and converted data are included in the database. MethDB was assembled as part of a larger synthesis effort on stream and river CH4 dynamics, and assembled data were used to identify large-scale patterns and potential drivers of fluvial CH4 and to generate an updated global-scale estimate of CH4 emissions from world rivers.
Atmospheric ozone data from Soddie, 2005 - 2017.
This is a summary of hourly ozone concentrations measured at a height of ~2.5 on a tower in the Soddie meadow. The focus was on the winter, but both the sampling frequency and the length of sampling during a year varied.
Atmospheric pressure on Hog Island, Phillips Creek Marsh and Oyster, VA 2012-2025
Barometric pressure is measured hourly at meteorological stations on the Atlantic Coast of the Delmarva Peninsula. Data is available as a long table that gives the average, minimum and maximum air pressure for each station at each time, or as a table that gives the average values for each of the stations, along with the median value across stations on each line.
Atmospheric deposition fields of nutrients (N, Fe, and P)
<p>Past (1850), present (2010), and future projected (2010) atmospheric nutrient deposition data used in PISCES simulations for the publication: Myriokefalitakis, S., Gröger, M., Hieronymus, J., and Döscher, R.: An explicit estimate of the atmospheric nutrient impact on global oceanic productivity, Ocean Sci. Discuss., https://doi.org/10.5194/os-2020-27, in review, 2020.</p>
Influence of Atmospheric Air Plasma Pre-Treatment of Veneers on the Mechanical Properties and Stability of Beech Plywood
<p>Wood-based sheet materials such as plywood, fiberboard, particleboard, and oriented strain board find applications in civil engineering, building technology, furniture manufacturing and many more. All these materials rely strongly on an effective bond formation between the resin and the wood base material, which gives rise to their mechanical performance and stability, as well as their resistance to moisture and liquids. In our study, we present the use of a commercial atmospheric air plasma system, which we used for the pretreatment of veneers of common beech (<em>Fagus sylvatica</em> L.) wood before formation of plywood boards. Plasma treatment parameters were optimized following the change in water contact angle. Two different stacking patterns were used for plasma-treated veneers. The time stability of the plasma modification was investigated by forming a second set of plywood boards 70 hours after plasma treatment of the respective veneers. The influence of the plasma treatment on mechanical properties was studied via bending and shear strength of the four sets of plasma-treated boards in comparison to a plywood out of the same veneer without plasma treatment. Water and moisture resistance were tested through water immersion and surface water resistance tests. Further, confocal laser scanning microscopy was used to determine changes of the surfaces’ morphologies.</p>
Model simulation data used in "The global impact of the transport sectors on atmospheric aerosol in 2030 – Part 2: Aviation" (Righi et al., Atmos. Chem. Phys., 2016)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Atmos. Chem. Phys.</i>, 2016). For details see the README.md file.</p>
Model simulation data used in "The global impact of the transport sectors on atmospheric aerosol in 2030 – Part 1: Land transport and shipping" (Righi et al., Atmos. Chem. Phys., 2015)
<p>This dataset contains the output of the EMAC global model simulations analysed and discussed in Righi et al. (<i>Atmos. Chem. Phys.</i>, 2015). For details see the README.md file.</p>
Data for: Ring Current Electron Precipitation During the 17 March 2013 Geomagnetic Storm: Underlying Mechanisms and Their Effect on the Atmosphere
<p>All data are included as MATLAB figure files, png files and MATLAB MAT files.</p><p>File precipitated_flux.mat contains a 4-D array of values of precipitated electron flux in [1/(s cm^2 keV)] for 289 time points from 16 March 2013 to 19 March 2013, with a 15 min time step; 100 values of energy in a range from 10 keV to 1 MeV, with a 10 keV step; on a spatial grid of 28 by 49 (P, R).</p><p>netCDF data can be opened with a variety of software tools, including Matlab, Origin or Python.</p>
Simulated severe convective wind events and environments from the Bureau of Meteorology Atmospheric Regional Projections for Australia (BARPA)
<p>Contacts for further details:</p> <ul> <li>This data record and associated research: Andrew Brown (andrewb1@student.unimelb.edu.au)</li> <li>BARPA data: Chun Hsu Su (chunhsu.su@bom.gov.au), Christian Stassen (Christian.Stassen@bom.gov.au), Harvey Ye (harvey.ye@bom.gov.au)</li> </ul> <h1>Introduction</h1> <p>This record contains data in support of Brown et al. (2024), including post-processed regional climate model data, automatic weather station observations, and post-processed reanalysis data over southeastern Australia for various time periods <strong>over December-Febrary months only</strong> (see descriptions below). This data relates to analysis of severe convective wind gusts in historical and future climate, with analysis scripts in <a href="https://github.com/andrewbrown31/BARPA/tree/main/wind_gust_analysis">this repository</a>. The data are described here according to the directory structure of this record (noting the files and directories have been compressed into <code>barpa_data.tgz</code>), as well as the relevant data sources. </p> <h1>Data sources</h1> <ul> <li><strong>The Bureau of Meteorology Atmospheric Regional Projections for Australia (BARPA)</strong>. A regional climate model containing a regional (BARPA-R) and convection-permitting (BARPAC-M) configuration, with large-scale forcing from ERA-Interim (1990-2015) and ACCESS1-0 using a historical (1985-2005) and RCP8.5 (2039-2059) forcing. See Brown et al. (2024) and Su et al. (2021) for more details. Note that any reuse of this data should properly acknowledge the Australian Bureau of Meteorology. Note also that the BARPA data used here was produced as part of the <a href="https://www.climatechangeinaustralia.gov.au/en/projects/esci/">Electricity Sector Climate Information project</a> (with licence and disclaimers <a href="https://www.climatechangeinaustralia.gov.au/en/projects/esci/risk-assessment/#Disclaimer">here</a>)<em>,</em> with more current BARPA versions (not used here) available at <a href="https://dx.doi.org/10.25914/z1x6-dq28" target="_blank" rel="noopener">https://dx.doi.org/10.25914/z1x6-dq28</a>. </li> <li><strong>Measured wind gusts from automatic weather stations (AWS)</strong>. Gust data is provided by the <a href="http://www.bom.gov.au/climate/data/stations/">Australian Bureau of Meteorology</a>, from 272 AWS locations over 2005-2015. Note that any reuse of this data should properly acknowledge the Australian Bureau of Meteorology.</li> <li><strong>The ERA5 reanalysis </strong>from the European Center for Medium Range Weather Forecasting (Hersbach 2020).</li> <li><strong>The ERA-Interim reanalysis</strong> from the European Center for Medium Range Weather Forecasting (Dee 2011).</li> </ul> <h1>/10min_points</h1> <p>This directory contains .csv files, with wind gust and related environmental data at 10-minute intervals at point locations, corresponding to automatic weather station locations. Data is available over 2005-2015. Files are named in the form <code>barpac_m_aws_<state>.csv</code> and <code>barpac_m_aws_<state>_barpa_r_interp.csv</code>. Here, <state> represents different administrative regions in southeast Australia, including New South Wales (nsw), Victoria (vic), South Australia (sa) and Tasmania (tas). See Figure 1 in Brown et al. (2024) for a map of station locations, that is also included in the /meta directory. The <code>barpa_r_interp</code> suffix indicates that the BARPAC-M wind gusts have been interpolated to the BARPA-R grid for comparison.</p> <p>This data is used in Brown et al. (2024) for evaluation and analysis of BARPA wind gusts in the historical climate (forced by ERA-Interim). The user is directed to that paper for more information on data processing. For the .csv files here, column descriptions are provided in Table 1, below.</p> <h1>/daily_points</h1> <p>This directory contains .csv files, with data associated with daily maximum wind gusts at point locations. This data is derived from the 10min_points data described above, with the same column descriptions in Table 1, below. The different files in this directory are as follows:</p> <ul> <li> <p><code>barpac_m_aws_dmax_obs.csv</code><br>Daily maximum observed wind gust from AWS measurements, with associated wind gust ratio and environmental conditions from ERA5.</p> </li> <li> <p><code>barpac_m_aws_dmax_2p2km.csv</code><br>Daily maximum simulated wind gust from BARPAC-M at closest grid point to AWS location, with associated wind gust ratio, lighting flash count, and environmental conditions from BARPA-R.</p> </li> <li> <p><code>barpac_m_aws_dmax_12km.csv</code><br>Daily maximum simulated wind gust from BARPA-R at closest grid point to AWS location, with associated wind gust ratio, lightning flash count, and environmental conditions.</p> </li> <li> <p><code>barpac_m_aws_dmax_erai.csv</code><br>Daily maximum simulated wind gust from ERA-Interim at closest grid point to AWS location, with associated wind gust ratio and environmental conditions from ERA5.</p> </li> <li> <p><code>barpac_m_aws_dmax_2p2km_barpa_r_interp.csv</code><br>As in <code>barpac_m_aws_dmax_2p2km.csv</code>, but wind gusts are interpolated to the BARPA-R grid prior to calculating the daily maximum.</p> </li> </ul> <h1>/monthly_nc</h1> <p>This directory contains monthly netcdf files at 12 km horizontal grid spacing, with post-processed BARPA data, relating to simulated severe convective wind gusts (from BARPAC-M), and their associated large-scale environments (from BARPA-R). This includes BARPAC-M and BARPA-R data that has been forced by the ACCESS1-0 global climate model, that is intended for analysis of future changes in severe convective wind events and environments. For further information, the user can refer to the internal file metadata, as well as Brown et al. (2024). The files in this directory are as follows (where <experiment> is either <code>hist</code> for historical climate forcing (1985-2005) or <code>rcp</code> for RCP8.5 climate forcing (2039-2059)):</p> <ul> <li> <p><code>barpac_scws_<experiment>_monthly.nc</code><br>Monthly counts of simulated severe convective wind events from BARPAC-M, for each type of environment (see <code>cluster</code> in Table 1).</p> </li> <li> <p><code>barpar_<experiment>_monthly.nc</code><br>Monthly counts of favouable severe convective wind environments from BARPA-R (using <code>bdsd</code>, see Table 1), for each type of environment (see <code>cluster</code> in Table 1).</p> </li> <li> <p><code>barpac_scws_bdsd_<experiment>.nc</code><br>Monthly counts of simulated severe convective wind events from BARPAC-M, that occur under favourable environmental conditions from BARPA-R. For each type of environment (see <code>cluster</code> in Table 1).</p> </li> <li> <p><code>barpac_max_<experiment>_monthly.nc</code><br>Monthly maximum simulated severe convective wind gust, from BARPAC-M, for each type of environment (see <code>cluster</code> in Table 1).</p> </li> </ul> <h1>/daily_nc</h1> <p>This directory contains monthly netcdf files at 12 km horizontal grid spacing, with daily maximum wind gusts from BARPAC-M, as well as the wind gust ratio (see <code>wgr_4</code> in Table 1) and the type of convective environment (from BARPA-R, see <code>cluster</code> in Table 1). This includes BARPA data that has been forced by ERA-Interim and by ACCESS1-0. For further information, the user can refer to the file metadata, as well as Brown et al. (2024). The files in this directory are as follows (where <code><experiment></code> is either <code>historical</code> for historical climate forcing or <code>rcp85</code> for RCP8.5 climate forcing, <code><forcing_model></code> is either <code>erai</code> for ERA-Interim or <code>ACCESS1-0</code>, <<code>date1></code> is the file start date and <code><date2></code> is the file end date):</p> <ul> <li><code>barpa_scw_<forcing_model>_<experiment>_0_<date1>_<date2>.nc</code></li> </ul> <h1>/meta</h1> <p>Lists of AWS stations, for each administrative state, with a file containing column descriptions. Note that not all of the stations listed in these files are used for analysis. Fig1.jpeg is from Brown et al. (2024), showing the BARPAC-M domain (also defines the netcdf file spatial extents), and the location of AWS.</p> <h3>Table 1</h3> <table> <tbody> <tr> <td><strong>Column</strong></td> <td><strong>Name</strong></td> <td><strong>Notes</strong></td> </tr> <tr> <td>stn_id</td> <td>Automatic weather station (AWS) identifier</td> <td> </td> </tr> <tr> <td>time</td> <td>Wind gust time (UTC)</td> <td> </td> </tr> <tr> <td>gust</td> <td>Observed wind gust speed from AWS (m/s)</td> <td>Observed wind gusts are measured at a height of 10 m, and represent a 3-second average. Data is provided as a one-minute maximum, and is resampled to a 10-minute maximum here for comparison with BARPA</td> </tr> <tr> <td>wgr_4</td> <td>Wind gust ratio</td> <td>The observed wind gust ratio, defined as the ratio between <code>gust</code>, and the 4-hour mean from the 10-minute data here.</td> </tr> <tr> <td>time_6hr</td> <td>6-hourly time (UTC)</td> <td>The most recent 6-hourly time step prior to <code>time</code>, associated with environmental diagnostics.</td> </tr> <tr> <td>mu_cape</td> <td>Most unstable convective available potential energy (J/kg)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>s06</td> <td>Bulk vertical wind shear from the surface to 6 km above ground level (m/s)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>dcape</td> <td>Downdraft convective available potential energy (J/kg)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>bdsd</td> <td>Brown and Dowdy (2021) Statistical Diagnostic (BDSD) for identifying favourable severe convective wind environments</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>qmean01</td> <td>Mass-weighted mean mixing ratio from the surface to 1 km (g/kg)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>Umean06</td> <td>Mass-weighted mean wind speed from the surface to 6 km above ground level (m/s)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>lr13</td> <td>Temperature lapse rate from 1 km above ground level to 3 km above ground level (◦C/km)</td> <td>Environmental diagnostic derived from BARPA-R. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>cluster</td> <td>Environment type</td> <td> <p>Based on statistical clustering of severe convective wind environments by Brown et al. 2023<br>0: Strong background wind cluster<br>1: Steep lapse rate cluster<br>2: High moisture cluster</p> <p>Derived from BARPA-R</p> </td> </tr> <tr> <td>s06_era5</td> <td>See s06</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>qmean01_era5</td> <td>See qmean01</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>Umean06_era5</td> <td>See Umea06</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>lr13_era5</td> <td>See lr13</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>bdsd_era5</td> <td>See bdsd</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>dcape_era5</td> <td>See dcape</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>mu_cape_era5</td> <td>See mu_cape</td> <td>Environmental diagnostic derived from ERA5. See Brown et al. (2024) for more information.</td> </tr> <tr> <td>cluster_era5</td> <td>See cluster</td> <td> <p>Based on statistical clustering of severe convective wind environments by Brown et al. 2023<br>0: Strong background wind cluster<br>1: Steep lapse rate cluster<br>2: High moisture cluster</p> <p>Derived from ERA5</p> </td> </tr> <tr> <td>wg10_12km_point</td> <td>Simulated wind gust from BARPA-R (m/s)</td> <td>Intended to represent a 10 meter wind gust. See Ma et al. (2018) for gust parameterisation details.</td> </tr> <tr> <td>wgr_12km_point</td> <td>Wind gust ratio from BARPA-R </td> <td>See wgr_4</td> </tr> <tr> <td>wg10_2p2km_point</td> <td>Simulated wind gust from BARPC-M (m/s)</td> <td>Intended to represent a 10 meter wind gust. See Ma et al. (2018) for gust parameterisation details.</td> </tr> <tr> <td>wgr_2p2km_point</td> <td>Wind gust ratio from BARPAC-M (see wgr_4)</td> <td>See wgr_4</td> </tr> <tr> <td>n_lightning_fl</td> <td>Number of daily lightning flashes from BARPAC-M</td> <td>See Brown et al. (2024) for more information.</td> </tr> <tr> <td>erai_wg10</td> <td>Simulated wind gust from ERA-Interim (m/s).</td> <td>Intended to represent a 10 meter wind gust. Note that ERA-Interim is provided in 3-hourly intervals, rather than 10-minute intervals for BARPA.</td> </tr> </tbody> </table> <p> </p>
Data Product for "Toward a Cenozoic history of atmospheric CO2"
<p>These data were vetted and revised from published paleo-CO2 data (original estimates archived at https://zenodo.org/uploads/8052599) by an international group of proxy experts supported through an NSF-funded Research Coordination Network. It brings together paleo-CO2 reconstruction data from terrestrial and marine archives, and the compilation includes estimates derived from multiple proxies including Phytoplankton, Boron, Stomatal Frequencies, Leaf Gas Exchange, Liverworts, Land Plant d13C, Paleosols, and Nahcolite. Data are visualized in an interactive product plot made available on the Paleo-CO2 project web page at (https://www.paleo-co2.org) and are also archived in the NCDC database (<a href="https://www.ncei.noaa.gov/pub/data/paleo/climate_forcing/trace_gases/Paleo-pCO2/product_files/">https://www.ncei.noaa.gov/pub/data/paleo/climate_forcing/trace_gases/Paleo-pCO2/product_files/</a>). </p>
CAMELS-LUX: Highly Resolved Hydro-Meteorological and Atmospheric Data for Physiographically Characterized Catchments around Luxembourg
<p>The CAMELS-LUX dataset encompasses hydro-meteorological time series and catchment attributes for 56 partly nested stream gauges feeding into the Luxembourgish stream network. The data is available at three temporal resolutions: daily, hourly and at a 15-minute resolution and spans the hydrological years from 2004-11-01 to 2021-10-31. The static catchment attributes cover parameters classifying the topography, geology and land use as well as climatic and hydrologic annual statistics of the 17-year time period.</p> <p>While an in depth description of the dataset as well as background information on catchments, the environment and exact calculation methods is provided in the accompanying publication in ESSD, the dataset description below isolates information on the available parameters and data structure contained in the provided files.</p> <p>Please note that the dataset might not include data corrections or validations that are subject to a date later than the date of the retrieval of the data for the processing of this dataset. This dates back to 2022 for most hydrologic time series, and to 2023 for the reanalysis data or the precipitation data. We are aware of duplicate rows in the time series file with a resolution of 15 minutes for catchment 16 as well as time stamp shifts in the precipitation data. We are working on correcting these data to update this dataset.</p> <p><strong>Data structure</strong></p> <p><strong>Time series data</strong></p> <ol> <li>Hydrologic parameters</li> <li>Precipitation parameters</li> <li>Air temperature and potential evapotranspiration parameters</li> <li>Thunderstorm relevant atmospheric parameters</li> <li> Soil Moisture parameters</li> </ol> <p><strong>Static catchment attributes</strong></p> <ol> <li>Basin IDs</li> <li>Meta catchment attributes</li> <li>Climatic catchment attributes</li> <li>Geologic catchment attributes</li> <li>Land use catchment attributes</li> <li>Topographic catchment attributes</li> </ol> <p><strong>Spatial data - shapefiles</strong></p>
Coefficients for the SMACPy atmospheric correction algorithm
<p>This dataset stores coefficients used by the updated Simplified Method for Atmospheric Correction - Python (SMACPy). The coefficients are used, together with meteorological data, to remove the effects of atmospheric gases and aerosols from a satellite image.</p> <p>Currently, this is a beta version and more updates to these coefficients may be expected in the future.</p> <p> </p> <p>Coefficients exist for various aerosol types, all defined from the equivalent 6S (<a href="https://doi.org/10.1109/36.581987">10.1109/36.581987</a>) aerosol types:</p> <p>BIOMA: Biomass burning plumes.</p> <p>CONTI: Continental aerosol.</p> <p>DESER: Desert dust aerosol.</p> <p>MARIT: Maritime aerosol, primarily sea salt.</p> <p>STRATO: Stratospheric aerosol.</p> <p>URBAN: Urban pollution aerosol.</p> <p>NOAER: A profile with no aerosol effects, just atmospheric gases and Rayleigh scattering.</p> <p> </p> <p> </p> <p>In this version the following satellites / sensors are supported:</p> <p>NASA Aqua / MODIS</p> <p>NASA Terra / MODIS</p> <p>Fengyun-4A / AGRI</p> <p>GEO-KOMPSAT-2A / AMI</p> <p>GOES-16 / ABI</p> <p>GOES-17 / ABI</p> <p>Himawari-8 / AHI</p> <p>Landsat-8 / OLI</p> <p>Meteosat-8 / SEVIRI (no HRV channel)</p> <p>Meteosat-9 / SEVIRI (no HRV channel)</p> <p>Meteosat-10 / SEVIRI (no HRV channel)</p> <p>Meteosat-11 / SEVIRI (no HRV channel)</p> <p>NOAA-20 / VIIRS (M and I bands)</p> <p>Suomi-NPP / VIIRS (M and I bands)</p> <p>Sentinel-2A / MSI</p> <p>Sentinel-2B / MSI</p> <p>Sentinel-3A / OLCI</p> <p>Sentinel-3B / OLCI</p> <p>Sentinel-3A / SLSTR</p> <p>Sentinel-3B / SLSTR</p> <p> </p>
Datasets for "A unified framework to estimate the origins of atmospheric moisture and heat using Lagrangian models"
<p>This repository contains the post-processed model outputs from HAMSTER v1.2.0 as used in the following paper: </p> <p>Keune, J., Schumacher, D. L., and Miralles, D. G.: A unified framework to estimate the origins of atmospheric moisture and heat using Lagrangian models, Geosci. Model Dev., 15, 1875–1898, https://doi.org/10.5194/gmd-15-1875-2022, 2022.<br> <br> The data set contains (1) global validation statistics for the three fluxes (evaporation, precipitation, sensible heat), and (2) the climatological source regions of precipitation and heat for Denver, Beijing and Windhoek. The former are found in the directory 'validation/global', and the latter are found in the directories '1001' (Denver), '3001' (Beijing) and '5002' (Windhoek). Multiple experiments were performed to assess the uncertainty of the source regions. Thus, multiple files exist, that show the same variables but for multiple experiments (indicated by the names "ALLPBL", "RH-10-20", "SOD08-SCH19", "SCH20", "FAS19" in the file name). For the moisture source regions, the uncertainty of the attribution methodology was assessed; these are indicated by the different folders, i.e. 'linear_upscaled' and 'random2_upscaled'. For each city and each experiment, the climatologically averaged source regions ('_mean.nc') and the climatologically averaged individual backward day contributions ('_bwmean.nc') are provided. Data sets are in the netCDF format and contain metadata following the CF convention.</p>
Natural Laboratories Atmosphere Dataset
<p>This spreadsheet contains a list of peer-reviewed published estimates of cloud radiative property changes associated with the perturbations of aerosols from a variety of natural laboratories discussed in Christensen et al. (2022).</p> <p>Christensen, M., Gettelman, A., Cermak, J., Dagan, G., Diamond, M., Douglas, A., Feingold, G., Glassmeier, F., Goren, T., Grosvenor, D., Gryspeerdt, E., Kahn, R., Li, Z., Ma, P.-L., Malavelle, F., McCoy, I., McCoy, D., McFarquhar, G., Mülmenstädt, J., Pal, S., Possner, A., Povey, A., Quaas, J., Rosenfeld, D., Schmidt, A., Schrödner, R., Sorooshian, A., Stier, P., Toll, V., Watson-Parris, D., Wood, R., Yang, M., and Yuan, T.: Opportunistic Experiments to Constrain Aerosol Effective Radiative Forcing, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2021-559, in review, 2021.</p> <p> </p>
Atmospheric Halocarbon Observations at Beromünster, Switzerland, and Bayesian Inverse Modeling to assess Emissions
<p>Atmospheric halocarbon (CFCs, halons, HCFCs, HFCs, PFCs, SF<sub>6</sub>, NF<sub>3</sub>, HFOs) and carbon monoxide (CO) observations (mole fractions) from the tall tower site at Beromünster, Switzerland (47.2 °N, 8.2 °E, 797 m a.s.l., 212 m a.g.l.), covering the period September 2019 to September 2020. The halocarbon measurements were conducted using a Medusa pre-concentration unit, coupled to gas chromatography (Agilent 6890N) and mass spectrometry (Agilent 5975, GC-MS).</p> <p>For further details see: Miller, B. R., Weiss, R. F., Salameh, P. K., Tanhua, T., Greally, B. R., Mühle, J., and Simmonds, P. G.: Medusa: A Sample Preconcentration and GC/MS Detector System for in Situ Measurements of Atmospheric Trace Halocarbons, Hydrocarbons, and Sulfur Compounds, Anal. Chem., 80, 1536–1545, https://doi.org/10.1021/ac702084k, 2008).</p> <p>The data format follows that used within the AGAGE network (see AGAGE data archive: <a href="http://agage.mit.edu/data/agage-data">http://agage.mit.edu/data/agage-data</a>).</p> <p>Data results for the Bayesian inversion conducted based on the measurement data from Beromünster to assess Swiss halocarbon emissions. Files are provided in netCDF format for the 28 individual substances discussed in (Rust, D. et al., 2022, <em>Swiss halocarbon emissions for 2019 to 2020 assessed from regional atmospheric observations</em>, Atmospheric Chemistry and Physics). Each file contains the a priori and a posteriori emissions as used or calculated in the Bayesian inversion. Data are provided on the grid used in the inversion (irregular longitude/latitude). Metadata are included as netCDF attributes. The netCDF files follow the CF conventions and are readable with any netcdf interface/tool.</p>
Coupled atmosphere-wave-ocean simulation of Hurricane Dorian (2019)
<p><strong>Description</strong></p> <p>This dataset provides the output of the coupled atmosphere-wave-ocean simulation of Hurricane Dorian from August 29 to September 7, 2019. The simulation is a composite of two separate simulations:</p> <ol> <li>From 00 UTC August 29 to 00 UTC September 1, 2019</li> <li>From 00 UTC September 1 to 00 UTC September 7, 2019</li> </ol> <p>The first simulation serves as "spin-up" for the hurricane and its environment prior to landfall. The second simulation is initialized from the output of the first simulation, while relocating the Dorian vortex to its correct position on September 1. Due to the size of the dataset only the surface fields are made available.</p> <p><strong>Model configuration</strong></p> <ul> <li><strong>Atmosphere</strong>: Weather Research and Forecasting (WRF, https://github.com/wrf-model/WRF) model v4.2.2, with the Advanced Research WRF (ARW) dynamical core. The model has a 3-km resolution grid over the parent domain and a 1-km resolution nest over the Bahamas region (September 1-7 only), both with 45 vertical layers. Initial and boundary conditions are based on 6-hourly ERA-5 dataset.</li> <li><strong>Ocean Waves</strong>: University of Miami Wave Model (UMWM, https://umwm.org). The model is configured at the same 3-km as the atmosphere model, and has 36 directional bins and 37 frequency bins that are logarithmically spaced from 0.0313 to 2 Hz.</li> <li><strong>Ocean Circulation</strong>: HYbrid Coordinate Ocean Model (HYCOM, https://github.com/HYCOM) v2.3.01, configured at 0.01 degree resolution and 41 vertical layers. Initial and boundary conditions are based on daily GOFS 3.1 41-layer HYCOM + NCODA Global 1/12° Analysis, daily. K-Profile Parameterization for vertical mixing.</li> <li><strong>Coupling</strong>: Earth System Modeling Framework (ESMF, https://github.com/esmf-org/esmf) v8.0.1</li> </ul> <p><strong>File Description</strong></p> <ul> <li>blkdat.input - HYCOM (ocean circulation) configuration file</li> <li>dorian2019_atmosphere_1km_2019090100.nc - Atmosphere at 1-km resolution dataset</li> <li>dorian2019_atmosphere_waves_3km_2019082900.nc - Atmosphere and waves at 3-km resolution dataset, Aug 29 - Sep 1.</li> <li>dorian2019_atmosphere_waves_3km_2019090100.nc - Atmosphere and waves at 3-km resolution dataset, Sep 1-7</li> <li>dorian2019_ocean_1km_2019082900.nc - Ocean circulation at 1-km resolution dataset</li> <li>main.nml - UMWM (waves) configuration file</li> <li>namelist.input - WRF (atmosphere) configuration file</li> <li>regional.depth.[ab] - HYCOM (ocean circulation) bathymetry files</li> <li>regional.grid.[ab] - HYCOM (ocean circulation) grid files</li> <li>umwm.gridtopo - UMWM (waves) grid and bathymetry file</li> <li>wrfbdy_d01 - WRF (atmosphere) boundary conditions file</li> <li>wrfinput_d01.2019082900 - WRF (atmosphere) initial conditions file for parent domain on Aug 29</li> <li>wrfinput_d01.2019090100 - WRF (atmosphere) initial conditions file for parent domain on Sep 1</li> <li>wrfinput_d02.2019090100 - WRF (atmosphere) initial conditions file for inner nest on Sep 1</li> </ul> <p><strong>Coupled model source code</strong></p> <p>The model source code has not yet been released. We plan to open source it upon publication of the paper describing the simulation. When the source code is released, we will add the link to this repository.</p>
Sub-10 nm size-distribution data for "What controls the observed size-dependency of the growth rates of sub-10 nm atmospheric particles?"
<pre>Size-Distribution data from the CERN CLOUD experiment (Kirkby et al., 2011) measured with a DMA-train (Stolzenburg et al., 2017) Data acquired during the CLOUD10 (Fall 2015) and CLOUD12 (Fall 2017) campaigns. Data associated with the publication Kontkane et al. (2022). File name indicates the Experiment number as specified in Table 3, Kontkanen et al. (2022) and the internal CLOUD run numbers as given in Table S1, Kontaknen et al. (2022). Concentration of precursor gases are also given in these two Tables. Exp. 8 only used data from NAIS and is not included in this repository. Header indicates the diameter at which the size-distribution is measured. First column is time column with areadable timestamp in the format %Y-%m-%d %H:%M:%S. Data is dN/dlog_10 dp in unit cm^(-3). Full size-distribution (up to 400 nm) can be obtained from the author upon request. References: Kontkanen et al. (2022), What controls the observed size-dependency of the growth rates of sub-10 nm atmospheric particles?, Environ. Sci.: Atmos., accepted. Kirkby et al. (2011), Role of sulphuric acid, ammonia and galactic cosmic rays in atmospheric aerosol nucleation, Nature, 476, 429-433, http://dx.doi.org/10.1038/nature10343 Stolzenburg et al. (2017), A DMA-train for precision measurement of sub-10nm aerosol dynamics, Atmos. Meas. Tech., 10, 1639-1651, http://www.atmos-meas-tech.net/10/1639/2017/ </pre>
Atmospheric angular momentum predictions
<p>Monthly mean predictions of atmospheric angular momentum for each year from 1960.</p> <p>All predictions start on 1 November.</p> <p>There is one value per month per latitude per year.</p> <p>Summing the values for each latitude gives the global mean.</p>
Architectural Atmospheres — Literature Record
<p>This dataset is an output of the RESONANCES project. <br>It collects bibliographic entries about four topics:</p> <ul> <li>architectural atmospheres</li> <li>phenomenology in architecture</li> <li>emotions, embodiment, and empathy in architecture</li> <li>the biological basis of atmospheric perception.</li> </ul>
ScienceDex guides
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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)
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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.