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1,574 results for “atmospheres”
The role of atmospheric drivers in a sudden transition of California precipitation in the 2012/13 winter
<p>This contains the data, presented in a publication entitled "The role of atmospheric drivers in a sudden transition of California precipitation in the 2012/13 winter" (JGR: Atmospheres). See the paper for details. See 'Readme.pdf' for the file descriptions.</p>
META-DATA for IEA Wind Task 46 report: Atmospheric drivers of wind turbine blade leading edge erosion: Hydrometeors
<p>The objectives of the work summarized in the report that accompanies this dataset are to:</p> <ul> <li>Describe crucial meteorological parameters for wind turbine blade leading edge erosion</li> <li>Describe technologies appropriate to measurement of hydroclimates and specifically hydrometeor size distributions and phase</li> <li>Identify available data sets that are available to describe hydrometeor size distributions and phase and generate meta-data for data sets available for use in mapping wind turbine blade leading edge erosion potential. This dataset summarizes those meta-data. </li> <li>Identify priority geographic areas for geospatial mapping of wind turbine blade leading edge erosion potential <p> </p> </li> </ul>
Tropical Cyclone Characteristics Represented by the Ocean Wave Coupled Atmospheric Global Climate Model Incorporating Wave-Dependent Momentum Flux
<p>This is dataset of global climate model simulation used in the paper "Tropical Cyclone Characteristics Represented by the Ocean Wave Coupled Atmospheric Global Climate Model Incorporating Wave-Dependent Momentum Flux" by Shimura et al. (2021)</p> <p>Followings are the explanation of data file.</p> <p>*** File naming rule ***<br> {data_group_name}_Exp{experiment_name}_TCnumber{tropical_cyclone_case_number}.nc</p> <p> data_group_name<br> - atm<br> - track</p> <p> experiment_name<br> - Wind<br> - Wave<br> - SlabO</p> <p> tropical_cyclone_case_number<br> - 001<br> - 002<br> ...<br> - 099<br> - 100</p> <p>*** Description on each data group ***<br> <br> atm: three dimentional atmospheric velocity data<br> - level: pressure levels for vertical atmospheric data<br> - longitude: Longitude<br> - latitude: Latitude<br> - velocity_u_component: averaged atmospheric eastward velocity</p> <p> track: data around tropical cyclone track<br> - time: UTC time (YYYYMMDDHH)<br> - longitude_center: Longitude of typhoon center<br> - latitude_center: Latitude of typhoon center<br> - central_pressure: typhoon central pressure<br> - maximum_surface_wind: typhoon maximum surface wind speed<br> - longitude_sfc: Longitude for surface data around typhoon center<br> - latitude_sfc: Latitude for surface data around typhoon center<br> - surface_wind_u_component: surface eastward wind around typhoon<br> - surface_wind_v_component: surface northward wind around typhoon<br> - sea_level_pressure: sea level pressure around typhoon<br> - latent_heat_flux: surface upward latent heat flux<br> - sensible_heat_flux: surface upward sensible heat flux<br> - time_atm: UTC time (YYYYMMDDHH) for atmospheric data<br> - level: pressure levels for atmospheric data<br> - longitude_atm: Longitude for atmospheric data around typhoon center<br> - latitude_atm: Latitude for atmospheric data around typhoon center<br> - velocity_u_component: 3d eastward velocity around typhoon<br> - velocity_v_component: 3d northward velocity around typhoon</p> <p> </p>
Elevated atmospheric CO2 changes defence allocation in wheat but herbivore resistance persists
<p>Predicting how plants allocate to different anti-herbivore defences in response to elevated carbon dioxide (CO<sub>2</sub>) concentrations is important for understanding future patterns of crop susceptibility to herbivory. Theories of defence allocation, especially in the context of environmental change, largely overlook the role of silicon (Si), despite it being the major anti-herbivore defence in the Poaceae. We demonstrated that elevated levels of atmospheric CO<sub>2</sub> (e[CO<sub>2</sub>]) promoted plant growth by 33% and caused wheat (<i>Triticum aestivum</i>) to switch from Si (–19%) to phenolic (+44%) defences. Despite the lower levels of Si under e[CO<sub>2</sub>], resistance to the global pest <i>Helicoverpa armigera</i> persisted; relative growth rates (RGR) were reduced by at least 33% on Si supplied plants, irrespective of CO<sub>2</sub> levels. RGR was negatively correlated with leaf Si concentrations. Mandible wear was c. 30% higher when feeding on Si supplemented plants compared to those feeding on plants with no Si supply. We conclude that higher carbon availability under e[CO<sub>2</sub>] reduces silicification and causes wheat to increase concentrations of phenolics. However, Si supply, at all levels, suppressed the growth of <i>H. armigera</i> under both CO<sub>2</sub> regimes, suggesting that shifts in defence allocation under future climate change may not compromise herbivore resistance in wheat.</p>
Data supporting the study "The evolution of surface structure during atmospheric ageing of nano-scale coatings of an organic surfactant aerosol proxy" by Milsom et al.
<p>Reduced neutron reflectometry (NR) data associated with the study "The evolution of surface structure during atmospheric ageing of nano-scale coatings of an organic surfactant aerosol proxy" by Milsom et al.. One folder contains the raw data for fitted parameters obtained from NR curves and supporting figure 3 in the study. The other contains a set of sub-folders which have reduced NR data along with python scripts which were used to create and fit the interfacial model to the data. Fitting bounds for each parameter are found in these scripts. </p>
Local to regional methane emissions from the Upper Silesia Coal Basin (USCB) quantified using UAV-based atmospheric measurements
<p>Raw data for Andersen et al., 2021 (Local to regional methane emissions from the Upper Silesia Coal Basin (USCB) quantified using UAV-based atmospheric measurements)</p>
Data set used for the article "Atmospheric triggers of the Brunt Ice Shelf calving in February 2021"
<p>Data set used for the manuscript "Atmospheric triggers of the Brunt Ice Shelf calving in February 2021". Which is under review in "Geophysical Research Letters" Journal. </p>
Data Regarding Classification of Infrasonic Atmospheric Events Using Electromagnetic Pulse Analysis
<p> </p> <div>The following data files were used for the analysis presented in the paper </div> <div>"Classification of Infrasonic Atmospheric Events Using Electromagnetic Pulse Analysis"</div> <div> </div> <div>The files include details of the infrasonicly detected evnents, and features extracted from electromagnetic signals, as explined in the README file.</div>
Representing surface heterogeneity in land-atmosphere coupling in E3SMv1 single-column model over ARM SGP during summertime - E3SM SCM data and code
<p>This dataset contains post-processed E3SM single-column model output and code used to produce the figures in the manuscript that we are targeting Geoscientific Model Development to submit. </p>
High-spatiotemporal resolution mapping of spatiotemporally continuous atmospheric CO2 concentrations over the global continent
<p>This dataset contains global continental-scale carbon dioxide inversion results for four periods in 2015 with a spatial resolution of 0.01°.</p>
Unified Model Atmospheric Forecast Model Data for Machine Learning Cloud-Base Height
<p>Unified Model data, in pp format, for machine learning of cloud-base height based on profiles of temperature, humidity, pressure and cloud fraction. The model configuration is Global Atmosphere 6, running with a resolution of N320 (which is coarser than what was running operationally at the time). Each simulation is run for 24 hours, re-initialising every 24 hours. A separate data file is provided every 6 hours. Data points are on a latitude-longitude grid in the horizontal and on a stretched grid in the vertical. See https://gmd.copernicus.org/articles/10/1487/2017/ for details of the model configuration.</p> <p>Data from January 2016 is for training.</p> <p>Data from July 2017 is for development/validation</p> <p>Data from October 2017 is for final testing.</p> <p> </p>
Accurate observation of black and brown carbon in atmospheric fine particles via a versatile aerosol concentration enrichment system (VACES)
<p>The processed data used to prepare the paper.</p>
Evaluating the Arabian Sea as a regional source of atmospheric CO2: seasonal variability and drivers
<p>The netCDF file included here corresponds to datasets used in the Biogeosciences paper entitled "Evaluating the Arabian Sea as a regional source of atmospheric CO2: seasonal variability and drivers" by Alain de Verneil, Zouhair Lachkar, Shafer Smith, and Marina Levy</p> <p>The data included here comprises of model output used in the paper to generate figures in the main manuscript. Many of the figures also contain data from publicly available sources, which is detailed in the "Data availability" section at the end of the paper.</p> <p>The data are in standard netCDF file format, readily readable using netCDF tools (i.e. netCDF4 package in Python, ncread function in Matlab, etc.).</p> <p>Variables names, dimensions, and units are described in the metadata within the netCDF file.</p> <p>Questions regarding this dataset and how it can be used to reproduce the results in the article can be forwarded to Alain de Verneil through email at ajd11@nyu.edu</p>
Aircraft profiles of stable isotope ratios in atmospheric total and condensed water from the NASA ORACLES mission.
<p>Aircraft in-situ measurements of water concentration and heavy water isotope ratios D/H and 18O/16O of cloud water and total water (water vapor plus condensed water) were collected during the NASA ObseRvations of Aerosols above CLouds and their intEractionS (ORACLES) project. Aircraft sampling took place in the southeast Atlantic marine boundary layer and lower troposphere (equator to 22 degrees south) over the months of Sept. 2016, Aug. 2017, and Oct. 2018. Isotope measurements were made using cavity ring-down spectroscopic analyzers integrated into the Water Isotope System for Precipitation and Entrainment Research (WISPER). The WISPER data are processed into mean latitude-altitude curtains and individual vertical profiles for each sampling period.</p> <p> </p> <p>The WISPER data accompanied a suite of other variables including standard meteorological quantities (wind, temperature, moisture), trace gas and aerosol concentrations, radar, and lidar remote sensing, which can be accessed through the DOIs listed further down. The ORACLES campaigns are described by Redemann et al., (2021). The water isotope measurements are further described in Henze et al., (2021). The absolute error with respect to the SMOW-SLAP scale is explained in detail by Henze et al., (2021).</p> <p> </p> <p>Total water concentration and isotope ratios were binned and averaged onto latitude-altitude grids using a kernel estimation approach, with weighting designed to estimate the mean during the approximate month-long duration of each sampling period. Standard deviations for each bin are also computed using kernel density estimation.</p> <p> </p> <p>Time intervals during aircraft vertical profiling are isolated and averaged onto 50-meter vertical levels. The files include water concentration and isotope ratios for both total water and cloud water in addition to temperature, pressure, latitude, and longitude.</p> <p> </p> <p>See included file README.txt for additional details.</p> <p> </p> <p>References</p> <p>---------------</p> <p>Henze, D., Noone, D., and Toohey, D.: Aircraft measurements of water vapor heavy isotope ratios in the marine boundary layer and lower troposphere during ORACLES, Earth Syst. Sci. Data Discuss. [preprint], https://doi.org/10.5194/essd-2021-238, in review, 2021.</p> <p> </p> <p>Redemann, J., Wood, R., Zuidema, P., Doherty, S. J., Luna, B., LeBlanc, S. E., Diamond, M. S., Shinozuka, Y., Chang, I. Y., Ueyama, R., Pfister, L., Ryoo, J.-M., Dobracki, A. N., da Silva, A. M., Longo, K. M., Kacenelenbogen, M. S., Flynn, C. J., Pistone, K., Knox, N. M., Piketh, S. J., Haywood, J. M., Formenti, P., Mallet, M., Stier, P., Ackerman, A. S., Bauer, S. E., Fridlind, A. M., Carmichael, G. R., Saide, P. E., Ferrada, G. A., Howell, S. G., Freitag, S., Cairns, B., Holben, B. N., Knobelspiesse, K. D., Tanelli, S., L'Ecuyer, T. S., Dzambo, A. M., Sy, O. O., McFarquhar, G. M., Poellot, M. R., Gupta, S., O'Brien, J. R., Nenes, A., Kacarab, M., Wong, J. P. S., Small-Griswold, J. D., Thornhill, K. L., Noone, D., Podolske, J. R., Schmidt, K. S., Pilewskie, P., Chen, H., Cochrane, S. P., Sedlacek, A. J., Lang, T. J., Stith, E., Segal-Rozenhaimer, M., Ferrare, R. A., Burton, S. P., Hostetler, C. A., Diner, D. J., Seidel, F. C., Platnick, S. E., Myers, J. S., Meyer, K. G., Spangenberg, D. A., Maring, H., and Gao, L.: An overview of the ORACLES (ObseRvations of Aerosols above CLouds and their intEractionS) project: aerosol–cloud–radiation interactions in the southeast Atlantic basin, Atmos. Chem. Phys., 21, 1507–1563, https://doi.org/10.5194/acp-21-1507-2021, 2021.</p> <p> </p> <p>The complete archive of ORACLES data are accessible via the digital object identifiers (DOIs) provided under ORACLES Science Team references as follows:</p> <p> </p> <p>ORACLES Science Team: Suite of Aerosol, Cloud, and Related Data Acquired Aboard P3 During ORACLES 2018, Version 3, NASA Ames Earth Science Project Office, https://doi.org/10.5067/Suborbital/ORACLES/P3/2018_V3, 2020a. </p> <p> </p> <p>ORACLES Science Team: Suite of Aerosol, Cloud, and Related Data Acquired Aboard P3 During ORACLES 2017, Version 3, NASA Ames Earth Science Project Office, https://doi.org/10.5067/Suborbital/ORACLES/P3/2017_V3, 2020b. </p> <p> </p> <p>ORACLES Science Team: Suite of Aerosol, Cloud, and Related Data Acquired Aboard P3 During ORACLES 2016, Version 3, NASA Ames Earth Science Project Office, https://doi.org/10.5067/Suborbital/ORACLES/P3/2016_V3, 2020c. </p> <p> </p> <p>ORACLES Science Team: Suite of Aerosol, Cloud, and Related Data Acquired Aboard ER2 During ORACLES 2016, Version 3, NASA Ames Earth Science Project Office, https://doi.org/10.5067/Suborbital/ORACLES/ER2/2016_V3, 2020d.</p>
Global atmospheric soluble iron deposition
<p>This dataset contains the modeled atmospheric total/soluble iron deposition on a global scale for the present-day and future conditions. The relative contributions from anthropogenic, biomass burning, and dust sources have been resolved in the data. It can be used for the modeling of ocean biogeochemistry. </p>
The aeroecology of atmospheric convergence zones: the case of Pallid swifts
<p>Trans-Saharan migratory bird species encounter large scale seasonal atmospheric convergence zones, where opposing monsoon and continental air masses meet. These macro-scale atmospheric conditions determine local weather, influence migratory and foraging behaviour and seasonal bird survival rates. Here we investigate the flight behaviour of Pallid swifts (<em>Apus pallidus</em>), a small aerial insectivore, in relation to non-breeding season atmospheric conditions using state-of-the-art GPS logged data. Our analysis suggests that pallid swift prey on insects by catching them where they are most densely concentrated within the atmosphere. Residence of birds in West Africa well past the vegetation minimum suggests that the state of the vegetation and associated local insect populations are not necessarily limiting. Migration events within, to and from, the non-breeding season foraging locations might therefore not only be guided by a decline in vegetation as common metric for prey availability, but also by shifting wind directions and their concentrating effects.</p> <p><strong>Supporting materials</strong></p> <p>This repository includes all data to reproduce the statistics in the described study, above. Certain omissions were made due to the data volumes involved. The latter mostly pertain to the visualization of the processes involved using transects through the atmosphere.</p> <p><strong>Data structure</strong></p> <p>Key data is saved in compressed R serial files (.rds) in the <code>data</code> folder. The <code>position_data.rds</code> file contains bird positions, headings and ancillary data to support most of the analysis in the study. Additional rds files are included which cover spatial analysis in support of the analysis (in the analysis folder).</p> <p><strong>Code execution</strong></p> <p>It is best to execute code in the numeric sequence as provided in the filename. Although it should not matter for the statistical analysis.</p> <p><strong>Licensing</strong></p> <p>For the data include be mindful of the Open Database License (ODbL) which is a copyleft license. Reuse is permitted on the condition that any database (including aggregated working data for analysis) in which our database is used remains open as well. The authors will enforce this policy. All other material such as figures and draft manuscripts are distributed under a CC-BY-SA-4.0 license.</p> <p><strong>Referencing</strong></p> <p>When referencing the data cite both the data repository as: Kearsley, L. et al. 2022. Data from: The aeroecology of atmospheric convergence zones: the case of pallid swifts. – Zenodo Repository, <https://doi.org/10.5281/zenodo.6320888>) and the original paper (Kearsley et al. 2022, <a href="https://doi.org/10.1111/oik.08594">doi.org/10.1111/oik.08594</a>).</p>
Dataset for "Size-dependence of Aerosol Iron Solubility in an Urban Atmosphere"
<p>Online PM concentration/weather condition and offline aerosol composition data for the study of "size-dependence of aerosol iron solubility in an urban atmosphere".</p>
Atmospheric visibility inferred from continuous-wave Doppler wind lidar, data set
<p>Visibility data from Pershore, UK, between 2018 and 2020</p>
Historical Data for paper "When will humanity notice its impacts on atmospheric rivers?"
<p>The repository contains the historical scenario simulation (one ensemble member) of GFDL SPEAR large ensemble data. The data is used to support the finding in the paper entitled "When will humanity notice its impacts on atmospheric rivers?" by Tseng et al. </p>
SSP2-4.5 Data for paper "When will humanity notice its impacts on atmospheric rivers?"
<p>The repository contains the SSP2-4.5 scenario simulation (one ensemble member) of GFDL SPEAR large ensemble data. The data is used to support the finding in the paper entitled "When will humanity notice its impacts on atmospheric rivers?" by Tseng et al.</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.