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1,574 results for “atmospheres”
Wuhan Radiosonde data for LLR atmospheric delay correction study
<p>Wuhan (57494) radiosonde data for studing atmospheric refraction-induced delay correction in lunar laser ranging</p>
Atmospheric chamber tests
<p> </p> <pre>measured data set, in an optical link with the influence of an atmospheric chamber</pre>
Data set for: "Model atmospheric aerosols convert to vesicles upon entry into aqueous solution"
<p>This document compiles raw data used in the aerosol to vesicle transformation study carried out by <strong>Serge Nader <em>et al.</em></strong><br> For detailed information and context, refer to the main article and its supplementary material published in ACS Earth and Space Chemistry.</p> <p>The Excel file contains data relevant to each figure in the main article and supporting information. The additional compressed file contains raw Transmission Electron Microscopy (TEM) photographs.</p>
Dataset for "Relationships of the Hygroscopicity of HULIS with Their Degrees of Oxygenation and Sources in the Urban Atmosphere, J. Geophys. Res. Atmos."
<p>The hygroscopicity of humic-like substances (HULIS) from Beijing aerosols and its relationship with the degrees of oxygenation and sources were investigated. The dataset in the Excel sheet contains the measured hygroscopicity parameter (<em>κ</em>), elemental ratios, and estimated intrinsic<em> κ</em> for HULIS, and the mass-based source contributions to HULIS, the <em>κ</em> estimated for total organic aerosol and the high-polarity fraction of water-soluble organic matter, and other data for Zhou et al. in Journal of Geophysical Research: Atmospheres. The concentrations and elemental ratios of HULIS and the source contributions to HULIS (both in µg/m<sup>3</sup> and percentage) were taken from Zhou et al. (2021). The dataset is based on the work supported by JSPS KAKENHI JP19H04253 and the National Natural Science Foundation of China (grant no. 41625014).</p> <p>Ruichen Zhou, Yange Deng, Bhagawati Kunwar, Qingcai Chen, Jing Chen, Lujie Ren, Kimitaka Kawamura, Pingqing Fu, and Michihiro Mochida: Relationships of the hygroscopicity of HULIS with their degrees of oxygenation and sources in the urban atmosphere, J. Geophys. Res. Atmos.</p> <p>Ruichen Zhou, Qingcai Chen, Jing Chen, Lujie Ren, Yange Deng, Petr Vodicka, Dhananjay K.Deshmukh, Kimitaka Kawamura, Pingqing Fu, and Michihiro Mochida: Distinctive sources govern organic aerosol fractions with different degrees of oxygenation in the urban atmosphere, Environ. Sci. Technol., 55, 4494–4503, 2021.</p>
Concentration and spatial variation of atmospheric ice nucleating particles over East Antarctica retrieved from the surface snow
<p>This dataset is the INP concentration in water.</p>
Data for: New insights into multi-component atmospheric wet deposition across China: A multidimensional analysis
<p>Atmospheric wet deposition has attracted much attention because of its removal function of aerosol particles and impact on ecosystem. It is a complex multi-component process due to various pollutants emission sources and reaction processes. However, traditional studies have focused on single components, ignoring the internal relationships and the comprehensive wet deposition level; this has hindered the comprehensive understanding and the assessment of ecological effects of deposition. Here, based on the monitoring of wet deposition across 52 stations during 2013–2018 in China, we used two novel multidimensional methods to investigate multi-component atmospheric wet deposition: network analysis and comprehensive index. Network analysis is a new method that can help us better explore the overall relationship between multiple components. The tighter deposition networks, the stronger relationship between different components, meaning that their source are more similar. We find that with the improvement of human development level and the diversification of energy structure, the deposition relationship networks will become looser. Comprehensive index is a method to realize the fusion of multiple parameters, which can help us explore the comprehensive effect of multi-component atmospheric deposition on ecosystem. We calculated the deposition comprehensive index for different ecological regions and found that it was strongly influenced by human activities (energy consumption, fertilization, and vehicle ownership) and precipitation. The findings presented here provide a new perspective for understanding multi-component atmospheric deposition and potential suggestions for evaluating its comprehensive ecological effects. </p>
Data for Field evidence for Asian outflow and fast depletion of total gaseous mercury in the polluted coastal atmosphere
<p>Hourly data of TGM at Tai Mo Shan in Hong Kong</p>
Dataset for publication: The Land-Atmosphere Feedback Observatory: A New Observational Approach for Characterizing Land-Atmosphere Feedback
<p>Here are zip files containing the data sets for the figures of the publication "The Land-Atmosphere Feedback Observatory: A New Observational Approach for Characterizing Land-Atmosphere Feedback" by Späth et al., 2023, GI, doi: 10.5194/gi-12-25-2023.</p><p>Data collected from the Halo Doppler lidar (DL), Atmospheric Raman Temperature and HUmidity Sounder ARTHUS, scanning differential absorption lidar (DIAL), Eddy-Covariance stations (EC) and the Water and Temperature Sensor Network (WaTSeN) of the Land-Atmisphere Feedback Observatory LAFO at University of Hohenheim, Stuttgart, Germany. The data cover the temporal period as presented in the figures in Späth et al., 2023, GI, doi: 10.5194/gi-12-25-2023.</p>
Variability of Atmospheric CO2 Over the Arctic Ocean: Insights From the O-Buoy Chemical Observing Network
<p>This dataset contains relevant files for the GEOS-Chem chemical transport model simulations used in the following manuscript: Graham, K. A., Friedrich, G., Rauschenberg, C. D., Williams, C. R., Bottenheim, J. W., Chavez, F. P., Halfacre, J. W., Holmes, C. D., Perovich, D. K., Shepson, P. B., Simpson, W. R., Tans, P. P., & Matrai, P. A. (2022). Variability of Atmospheric CO<sub>2</sub> Over the Arctic Ocean. <em>Journal of Geophysical Research: Atmospheres</em>. In Review.</p>
Supplementary dataset to the publication by Hieronymi et al.: "Ocean color atmospheric correction methods in view of usability for different optical water types", Frontiers in Marine Science (under review, submitted 22 Dec 2022)
<p>The dataset is an annex to the publication (under review, submitted 22 Dec 2022):</p> <p>Martin Hieronymi, Shun Bi, Dagmar Müller, Eike M Schütt, Daniel Behr, Carsten Brockmann, Carole Lebreton, François Steinmetz, Kerstin Stelzer and Quinten Vanhellemont: "Ocean color atmospheric correction methods in view of usability for different optical water types", Frontiers in Marine Science.</p> <p>The data were created to compare the results of different atmospheric correction methods for ocean (water) color imagery. The dataset includes ten modified ESA/EUMETSAT Copernicus Sentinel-3 OLCI satellite scenes from optically diverse sea areas worldwide. The NetCDF files are optimized for visualization in the ESA Sentinel Application Platform (SNAP) and especially the Spectrum View. The data include original OLCI Level-1B top-of-atmosphere radiances recorded by the sensor and the results from five different atmospheric correction methods, i.e., spectral remote-sensing reflectance at 16 OLCI bands. The atmospheric correction methods compared are</p> <ol> <li> <p>IPF (Collection 3, the standard method),</p> </li> <li> <p>C2RCC (v1.7 including IPF gains; Brockmann et al. [2016]),</p> </li> <li> <p>A4O (v0.23 (2022-01-19); a novel method by Hieronymi et al.),</p> </li> <li> <p>POLYMER (v4.14 (2021-12-17); Steinmetz et al. [2011]), and</p> </li> <li> <p>ACOLITE-DSF (v2022-10-25.0; Vanhellemont and Ruddick [2021]).</p> </li> </ol> <p>The original flags supplied in each case are also provided.</p> <table> <tbody> <tr> <td> <p><strong># </strong></p> </td> <td> <p><strong>Sensor-Date-UTC</strong></p> </td> <td> <p><strong>Region </strong></p> </td> <td> <p><strong>Special features </strong></p> </td> </tr> <tr> <td> <p>1</p> </td> <td> <p>S3A-20160720-092821</p> </td> <td> <p>Barents Sea</p> </td> <td> <p>High latitudes, bloom of coccolithophores</p> </td> </tr> <tr> <td> <p>2</p> </td> <td> <p>S3A-20160720-093421</p> </td> <td> <p>North Sea, Wadden Sea</p> </td> <td> <p>Moderately to extremely scattering waters, tidal areas, in situ data</p> </td> </tr> <tr> <td> <p>3</p> </td> <td> <p>S3A-20170114-130626</p> </td> <td> <p>South Atlantic Ocean, Rio de la Plata estuary</p> </td> <td> <p>Extremely scattering waters, clear oceanic waters, sun glint, South Atlantic Anomaly</p> </td> </tr> <tr> <td> <p>4</p> </td> <td> <p>S3A-20170527-015236</p> </td> <td> <p>Yellow Sea, East China Sea, Yangtze, Lake Taihu</p> </td> <td> <p>Extremely scattering waters, tidal areas, large rivers, absorbing aerosols, sun glint</p> </td> </tr> <tr> <td> <p>5</p> </td> <td> <p>S3A-20170529-092334</p> </td> <td> <p>Mediterranean Sea</p> </td> <td> <p>Large areas with clear waters, sun glint</p> </td> </tr> <tr> <td> <p>6</p> </td> <td> <p>S3A-20170913-080730</p> </td> <td> <p>Black Sea, Aegean Sea</p> </td> <td> <p>Clear and absorbing waters</p> </td> </tr> <tr> <td> <p>7</p> </td> <td> <p>S3A-20180715-093613</p> </td> <td> <p>North Sea, Baltic Sea</p> </td> <td> <p>Intense bloom of cyanobacteria partly with scum</p> </td> </tr> <tr> <td> <p>8 9</p> </td> <td> <p>S3A-20200601-092517 S3B-20200601-084546</p> </td> <td> <p>North Sea, Baltic Sea</p> </td> <td> <p>Inter-comparison of S3A and S3B with different observation angles, absorbing waters</p> </td> </tr> <tr> <td> <p>10</p> </td> <td> <p>S3B-20200406-093801</p> </td> <td> <p>North Sea, Baltic Sea</p> </td> <td> <p>High OWT diversity</p> </td> </tr> </tbody> </table> <p> </p>
Intercomparison of atmospheric Carbonyl Sulfide (TransCom-COS; Parts one & two):
<p>Atmospheric concentrations of COS simulated by the ATMs (TM5, NICAM5, NICAM6, LMDz, TM3, TOMCAT) used in the Transcom-COS experiment.</p> <p>Zhumker: Anthropogenic component</p> <p>BB: Biomass burning component</p> <p>VEG-SIB4: Vegetation component given by the SIB4 terrestrial model</p> <p>SOIL-SIB4: Soil component given by the ORCHIDEE terrestrial model</p> <p>VEG-ORC: Vegetation component given by the ORCHIDEE terrestrial model</p> <p>SOIL-ORC: Soil component given by the SIB4 terrestrial model</p> <p>OCEAN_Lennartz : Oceanic fluxes with the indirect fluxes through DMS from Lennartz et al. (2017)</p> <p>OCEAN_LennartzPICSES: Oceanic fluxes with the indirect fluxes through DMS from the ocean model NEMO PISCES</p> <p>OPT_JM= Optimized COS fluxes described in Ma et al. (2021)</p> <p>OPT_LSCE= Optimized COS fluxes described in Remaud et al. (2022)</p> <p>Month: Monthly resolution</p> <p>Diurnal: 3 hourly resolution</p>
Atmospheric boundary layer height and energy over the Tibetan Plateau
<p>Processed data of atmospheric boundary layer height and energy over the Tibetan Plateau. </p>
Sensitivity of glaciers in the European Alps to anthropogenic atmospheric forcings: case study of the Argentière glacier
<p>This Zenodo repository contains all datasets (IPSL CMIP6 data, glaciological data, SAFRAN data), ElmerIce codes and Python Jupyter Notebook used in the study reported in the article <strong><em>"Sensitivity of glaciers in the European Alps to anthropogenic atmospheric forcings: case study of the Argentière glacier"</em></strong></p>
Model output for analyses in Qian et al. (2023) "Region and cloud regime dependences of parametric sensitivity in E3SM Atmosphere Model".
<p>This archive contains post-processed data for analyses in Qian et al. (2023) "<strong>Region and cloud regime dependences of parametric sensitivity in E3SM Atmosphere Model</strong>". These data are model output from the Perturbed Parameters Ensemble (PPE) simulations conducted with DOE's E3SM Atmosphere Model Version 1 (EAMv1). There are 256x12 PPE simulations with each simulation run for 5 days following the approach of Cloud Associated Parameterization Testbed (CAPT) and transpose Atmosphere Model Intercomparison Project (AMIP) paradigm. The diagnostic variables associated with the parameterizations of turbulence and shallow convection, deep convection, cloud microphysics, and gravity wave drag at day 5 are saved and used for analyses in this paper. </p>
Rainy downdrafts in abyssal atmospheres
<p>Visualizations for a select set of simulations, for intuition to accompany manuscript. Naming conventions follow the paper, except big.gif and small.gif which refer to b_0/H of 1 and 1/10 respectively with medium resolution.</p>
Quantifying the contributions of atmospheric processes and meteorology to severe PM2.5 pollution episodes during the COVID-19 lockdown in the Beijing-Tianjin-Hebei, China
<p>Data</p>
Preliminary DOI/Repository of ALPINE3D and SNOWPACK data of the submitted paper "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model"
<p>There are 2 zip folders in this repository.</p> <p>"a3d_jgr.zip" contains a folder structure that must be kept as it is in order to run the simulation in the current configuration.<br> The setup contains both input and output data as well as the model configuration as used in the submitted manuscript <br> "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model".</p> <p>The zip file contains 3 main folders:</p> <ul> <li>base_setup_files</li> <li>a3d_jgr_alpha1</li> <li> a3d_jgr_alpha3</li> </ul> <p>The "base_setup_files" contains all input files that are necessary to run the reference (R) simulation ("a3d_jgr_alpha1" folder) and the comparison "C" scenario ("a3d_jgr_alpha3") folder. In the a3d_jgr_alpha1 and a3d_jgr_alpha3 folders you find the corresponding outputs as used in the paper, as well as the settings used - which only differ by the changed "SCHMIDT_DRIFT_FUDGE" value that is found in each a3d_jgr_alphax/setup/io.ini file. The input data is already linked accordingly in each io.ini file.</p> <p>a3d_jgr_alpha1 also contains the detailed snow profiles for each point along the transects.</p> <p>To reproduce the results, download and compile the source code for the adjusted ALPINE3D model first, which can be obtained from https://gitlabext.wsl.ch/snow-models/alpine3d.git under the "alpine3d_mosaic" branch. After installing, you can run the provided model setup uploaded here.</p> <p>_________________________________________________________________________________________________________<br> <br> "SNOWPACK_JGR.zip" contains both input and output data for SNOWPACK as well as the model configuration as used in the submitted manuscript "Towards a fully physical representation of snow on Arctic sea ice using a 3D snow-atmosphere model".</p> <p>The zip file contains 2 main folders: </p> <ul> <li>SNOWPACK_JGR_ALPHA1</li> <li>SNOWPACK_JGR_ALPHA3</li> </ul> <p>In the SNOWPACK_JGR_ALPHA1 (reference "SP_R" simulation) and SNOWPACK_JGR_ALPHA3 (comparison "SP_C" scenario) folders you find the corresponding inputs, outputs and configuration as used in the paper, as well as the settings used - which only differ by the changed "SCHMIDT_DRIFT_FUDGE" value that is found in each SNOWPACK_JGR_ALPHA/setup/io.ini file. The input data is already linked accordingly in each io.ini file.</p> <p>To reproduce the results, download and compile the source code for the adjusted SNOWPACK model first, which can be obtained from https://gitlabext.wsl.ch/snow-models/snowpack.git under the "snowpack_mosaic" branch. After installing, you can run the provided model setup uploaded here.</p>
Data for "Energy Surplus and Atmosphere – Land-Surface "Tug of War" Induced by Climate Change Control Future Evapotranspiration"
<p>USGS gauges used in manuscript "<strong>Energy Surplus and An Atmosphere-Land-Surface “Tug of War” Control Future Evapotranspiration"</strong>. USGS_CTL15_Gage.mat contains the USGS gauge ID, and one can use retrieve_daily_streamflow.m to download the corresponding streamflow time series. </p>
Supporting data for: Emissions background, climate, and season determine the impacts of past and future pandemic lockdowns on atmospheric composition and climate
<p>COVID-19 pandemic responses affected atmospheric composition and climate. These effects are historically contingent, depending on the background emissions, climate, and season in which they occur. We used the GISS ModelE Earth System Model to evaluate how atmospheric and climate impacts depend on the decade and season in which lockdowns occurred. Data underlying the figures and analysis are provided as Python numpy arrays as a courtesy for peer reviewers. These data are annual means of diagnostic variables from ModelE.</p>
Data for the Journal article "Reduced whereas still noteworthy atmospheric pollution of trace elements in China"
<p>This dataset provides the trace elements emission inventory with 27km resolution for China in 2017, the model simulated concentrations of trace elements for China in 2017 and the associated health risks of trace elements for China in 2017.</p>
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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)
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.