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76 results for “atmospheric observation”
RT Dataset -- Updated radiative transfer model for Titan in the near-infrared wavelength range: Validation against Huygens atmospheric and surface measurements and application to the Cassini/VIMS observations of the Dragonfly landing area
<p>This dataset contains all Radiative Transfer (RT) results made for the paper.</p> <p>The data are stored in 5 zipped-folders names with the Cassini/VIMS cube flyby and id, or explicitly for Huygens/ULIS calibrated observations:</p> <ul> <li>TB_C1481624349_1</li> <li>T40_C1578266417_1</li> <li>T38_C1575509158_1</li> <li>T40_C1578263500_1</li> <li>T40_C1578263152_1</li> <li>ULIS_observations</li> </ul> <p>The TB_C1481624349_1 folder contains the Cassini/VIMS cube over HLS, the HLS end-member (End_member.txt), the surface albedo retrieved by Karkoschka et al. (2016) corrected for the photometry (HLS_Karkoschka_2016_spectrum.txt), and the inverted surface albedo (Surface_albedo.txt).</p> <p>In these folders, each VIMS pixel is stored in a .txt file with the following pattern:</p> <p><CUBE_ID>_<PIXEL_SAMPLE>_<PIXEL_LINE> .txt</p> <p>It starts with a header describing the observation: </p> <ul> <li>CUBE_ID: the VIMS cube id (`C1234567890_1` format)</li> <li>SAMPLE: the pixel sample number.</li> <li>LINE: the pixel line number.</li> <li>LONG: the pixel longitude (in degree).</li> <li>LAT: the pixel latitude (in degree).</li> <li>INC: the surface incident angle (in degree).</li> <li>EMI: the surface emergent angle (in degree).</li> <li>PHASE: the surface phase angle (in degree).</li> </ul> <p>For the Selk crater cubes (T40_C1578266417_1, T38_C1575509158_1, T40_C1578263500_1, T40_C1578263152_1), the header also contains the spatial sampling and the radiative transfer model outputs: </p> <ul> <li>Spatial sampling (km/pix).</li> <li>Fh: the haze scaling factor.</li> <li>Fm: the mist scaling factor.</li> <li>1-sigma (Fh): the 1-sigma uncertainty on Fh.</li> <li>1-sigma (Fm): the 1-sigma uncertainty on Fm.</li> <li>Reduced chi2: the reduced chi2. </li> </ul> <p>Then contains the observed spectra:</p> <ul> <li>Column 1: the VIMS channel central wavelength (in micrometers).</li> <li>Column 2: the VIMS pixel I/F.</li> <li>Column 3: the VIMS pixel I/F 1-sigma uncertainty. </li> </ul> <p>For the Selk crater cubes (T40_C1578266417_1, T38_C1575509158_1, T40_C1578263500_1, T40_C1578263152_1), 3 columns are added for: </p> <ul> <li>Column 4: the surface albedo.</li> <li>Column 5: the upper 1-sigma uncertainty on the surface albedo.</li> <li>Column 6 : the lower 1-sigma uncertainty on the surface albedo.</li> </ul> <p>The ULIS folder contains the Huygens/ULIS calibrated observations (in I/F) and the simulations with 1-sigma uncertainties as a function of the altitude (in km):</p> <ul> <li>Column 1: the VIMS channel central wavelength (in micrometers), stopped at the end of the Huygens/ULIS wavelength range.</li> <li>Column 2: the ULIS I/F.</li> <li>Column 3 : the simulated I/F.</li> <li>Column 4: the lower 1-sigma uncertainty on the simulation.</li> <li>Column 5 : the upper 1-sigma uncertainty on the simulation.</li> </ul>
Data for: Intercomparison of commercial analyzers for atmospheric ethane and methane observations
<p>Methane (CH<sub>4</sub>) is a strong greenhouse gas that has become the focus of climate mitigation policies in recent years. Ethane / methane ratios can be used to identify and partition the different sources of methane, especially in areas with natural gas mixed with biogenic methane emissions, such as cities. We assessed the precision, accuracy, and selectivity of three commercially available laser-based analyzers that have been marketed as measuring instantaneous dry mole fractions of methane and ethane in ambient air. The Aerodyne SuperDUAL instrument performed best of the three instruments but it requires expertise to operate and space for the large footprint. The Aeris Mira Ultra LDS analyzer also performed well for the price point and small footprint but required characterization of the water vapor dependence of reported concentrations and careful setup for use. The Picarro G2210-i precisely measured methane but it did not detect the 10 ppbv increases in ambient ethane detected by the other two instruments when sampling a plume of incompletely combusted natural gas. For long-term tower deployments or those with large mobile laboratories, the Aerodyne SuperDUAL provides the best precision for methane and ethane. For smaller mobile platforms, the Aeris MIRA is a more compact analyzer, and with careful use, can quantify thermogenic methane sources to sufficient precision for short term deployments in urban or oil and gas areas. We weighed the advantages of each instrument, including size, power requirement, ease of use on mobile platforms, and expertise needed to operate the instrument, and we recommend the Aerodyne SuperDUAL or the Aeris MIRA Ultra LDS depending on the situation.</p>
Supporting data for Assessing clouds using satellite observations through three generations of global atmosphere models
<p>Monthly data from CAM4, CAM5, and CAM6 that are needed to reproduce the analysis and figures in the manuscript entitled: Assessing clouds using satellite observations through three generations of global atmosphere models by Brian Medeiros, Jonah Shaw, Jennifer Kay, and Isaac Davis.</p>
Data for: Intercomparison of commercial analyzers for atmospheric ethane and methane observations
Open the record for dataset details and reuse information.
Data for "Source identification of atmospheric organic vapors in two European pine forests: Results from Vocus PTR-TOF observations"
<p>This file consists of the time series of the measured trace gases, meteorological parameters, and the concentrations of isoprene and monoterpenes in the Landes forest and at the SMEAR Ⅱ station, which have been analyzed in the manuscript "Source identification of atmospheric organic vapors in two European pine forests: Results from Vocus PTR-TOF observations". For more details, please contact the author (haiyan.li@helsinki.fi).</p>
Data for "Martian oxygen and hydrogen upper atmospheres responding to solar and dust storm drivers: Hisaki space telescope observations"
<p>Data files (.npy) and python codes (.ipynb) to produce the figures in the paper.</p> <p>Download the zip file (dataforfigures_v2.zip), open plot_figX.ipynb with Jupyter notebook, and run it.</p> <p> </p>
Lidar ratio–depolarization ratio relations of atmospheric dust aerosols: the T-matrix modeling and high spectral resolution polarization lidar observations
<p><strong>Data for publication:</strong></p> <p><em><strong>Lidar ratio–depolarization ratio relations of atmospheric dust aerosols: the T-matrix modeling and high spectral resolution polarization lidar observations.</strong></em></p> <p>Mail:</p> <p>sato@riam.kyushu-u.ac.jp </p> <p><a href="mailto:bilei@zju.edu.cn">bilei@zju.edu.cn</a></p>
Pyroconvection Classification based on Atmospheric Vertical Profiling Correlation with Extreme Fire Spread Observations
<p>1. Isochrones for Martorell, Santa Coloma Queralt, Torroella, Pobla Massaluca and Sierra Bermeja fires, in shapefile format. Each file has associated an attribute table identifying the hour (in UTC), the affected area, the rate of spread, and direction. Source: Catalan Fire and Rescue Service (Bombers de la Generalitat de Catalunya)</p> <p>2. ERA5 reanalysis data obtained for each fire, hourly and at different pressure levels (37) from the Copernicus Climate Change Service (C3S) Climate Data Store (CDS). The files are in netCDF format, and the variables requested were temperature, relative humidity, U-component of wind, V-component of wind. Source: Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Thépaut, J-N. (2018): ERA5 hourly data on pressure levels from 1979 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS). 10.24381/cds.bd0915c6</p> <p>3. Data from sondes launched in fires during the 2021 Spain wildfire campaign. The files are in CSV format, and there are two per fire: the sounding data corrected and the raw flight history. The information provided is Hour (UTC), Wind speed (m/s), Wind direction (true deg), Dew point (C), Latitude, Longitude, Altitude (in m MSL and m AGL), Pressure (Pascal), Speed (m/s), Heading (degrees), Temperature (C), Relative humidity (%), Internal temperature (C), Latitude, Longitude, Rise speed (m/s). Source: Catalan Fire and Rescue Service (Bombers de la Generalitat de Catalunya)</p> <p>4. Data from the closest weather station to each fire. The file is an Excel file. The table fields are: fire name, weather station name, day, hour, average temperature (°C), maximum temperature(°C), minimum temperature (°C), average relative humidity (%), precipitation (mm), wind speed (10 m, km/h), wind direction (10 m, degrees), wind gusts (10 m, km/h), pressure (hPa), radiation (W/m). Source: Meteo.cat, Servei Meteorològic de Catalunya</p> <p>5. Fire behavior resume for Martorell, Santa Coloma Queralt, Torroella, Pobla Massaluca, Llançà, Alfarràs and Sierra Bermeja fires (Spain). The differences in the data shown respond to the possibility of launching sondes, recreating isochrones, and observing the plume column during each fire. In those cases where the information was obtained through these three ways, the variables available are: column type, ABL and LCL height (m), sonde ID, rate of spread (km/h), ROS observed / ROS expected ratio, fireline intensity expected and observed (kW/m), and affected area (ha).</p> <p>6. Photographic registry of the fire plume evolution and a brief description of the pyroconvective moments in the Alfarràs, Martorell, Llançà, Torroella, Santa Coloma de Queralt, Pobla Massaluca, and Sierra Bermeja fires (Spain). Pictures sources: Catalan Fire and Rescue Service (Bombers de la Generalitat de Catalunya)</p>
Dataset for "Observational evidence for the non-suppression effect of atmospheric chemical modification on the ice nucleation activity of East Asian dust"
<p>These are datasets for the manuscript titled "Observational evidence for the non-suppression effect of atmospheric chemical modification on the ice nucleation activity of East Asian dust".</p>
On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model: Article Data
<p>NetCDF datatset of presented results from the publication titled "On the application of an observations-based machine learning parameterization of surface layer fluxes within an atmospheric large-eddy simulation model" in the Journal of Geophysical Research - Atmospheres, Paper #2021JD036214R.</p>
Demo data and model weights for "Direct Observation of Atmospheric Turbulence with a Video-rate Wide-field Wavefront Sensor"
<p>Demo data and model weights for "Direct Observation of Atmospheric Turbulence with a Video-rate Wide-field Wavefront Sensor". For usage, please refer to https://github.com/freemercury/Widefield_wavefront_sensor.</p>
Dataset 2 "Aerosol optical properties within the atmospheric boundary layer predicted from ground-based observations compared to Raman lidar retrievals during RITA-2021"
<p>This dataset provides additional profiles for the manuscript titled "Aerosol optical properties within the atmospheric boundary layer predicted from ground-based observations compared to Raman lidar retrievals during RITA-2021". It is available for those who are interested.</p>
Model Spectra for Morley et al. 2017 (Observing the Atmospheres of Known Temperate Earth-sized Planets with JWST)
<p>README</p> <p>This file contains the model spectra presented in Morley et al. 2017, Observing the Atmospheres of Known Temperate Earth-sized Planets with JWST. </p> <p>The models are organized as follows: </p> <p>##########################<br> ## Transmission spectra ##<br> ##########################</p> <p>transmission_spectra contains 4 folders:<br> alb0.0_massradiusrel includes models with Bond albedo=0.0, and planet masses assuming the Weiss & Marcy 2014 mass-radius relationship<br> alb0.3_massradiusrel includes models with Bond albedo=0.3, and planet masses assuming the Weiss & Marcy 2014 mass-radius relationship<br> alb0.0_measured_masses includes models with Bond albedo=0.0, and observed planet masses, as described in Morley et al. 2017<br> alb0.3_measured_masses includes models with Bond albedo=0.3, and observed planet masses, as described in Morley et al. 2017</p> <p>each file contains the wavelength and model transit depth from 0.3 to 250 microns, calculated at 1 cm-1 wavenumber resolution. </p> <p>The file name indicates the planet name, model surface pressure (in bar), Bond albedo, and the assumed composition (Earth-, Venus-, or<br> Titan-based compositions, calculated in chemical equilibrium at each layer of the model, as described in Morley et al. 2017). </p> <p>(e.g. trans_spect_massradiusrel_gj1132b_psurf0.1_alb0.0_chem_earth.p.txt is a model of GJ 1132b, assuming the Weiss/Marcy mass-radius relationship, with a surface pressure of 0.1 bar, Bond albedo of 0.0, and an Earth-based composition). </p> <p>##########################<br> ## Emission spectra ##<br> ##########################</p> <p>emission_spectra contains 4 folders:<br> alb0.0 includes models with Bond albedo=0.0<br> alb0.3 includes models with Bond albedo=0.3<br> alb0.7 includes models with Bond albedo=0.7<br> emission_spectra_dividestar includes models that have already been divided by a model stellar spectrum, for convenience. </p> <p><br> each file contains the wavelength and model thermal emission flux in W/m2/m from 0.3 to 250 microns, calculated at 1 cm-1 wavenumber resolution. </p> <p>The file name indicates the planet name, model surface pressure (in bar), Bond albedo, and the assumed composition (Earth-, Venus-, or<br> Titan-based compositions, calculated in chemical equilibrium at each layer of the model, as described in Morley et al. 2017). </p> <p>(e.g. gj1132b_psurf0.01_alb0.0_chem_earth.spec is a model of GJ 1132b with a surface pressure of 0.01 bar, Bond albedo of 0.0, and an Earth-based composition). </p> <p>The models in emission_spectra_dividestar instead contain the flux of the planet divided by the flux of the star. </p> <p> </p> <p>##########################<br> ## Eclipse Depths ##<br> ##########################</p> <p>eclipse_depths contains a file for each planet with calculated eclipse depths for each of the wide-band JWST MIRI filters (http://ircamera.as.arizona.edu/MIRI/pces.htm). </p> <p>(new version from Jan 29 2018 has fixed a bug in MIRI eclipse depths that affected the longest wavelength filter for GJ 1132b and LHS 1140b only). </p>
Selected CO2 Data from BErkeley Atmospheric CO2 Observation Network
<p>Selected CO<sub>2</sub>data from BErkeley Atmospheric CO<sub>2</sub> Observation Network (BEACO<sub>2</sub>N) for use in the characterization of the heterogeneity of greenhouse gas concentrations around the San Francisco Bay Area and the constraint of CO<sub>2 </sub>emissions from mobile sources.</p>
Supplement videos for manuscript Impacts of storm "Zyprian" on middle and upper atmosphere observed from Central European stations
<p>Supplement video material for the manuscript Impacts of storm “Zyprian” on middle and upper atmosphere observed from Central European stations. It contains loop of MSG satellite observation and infrasound animations.</p>
Datasets for :Atmospheric chemistry of oxalate: Insight into the role of relative humidity and acidity from high-resolution observation.
<p>The datasets in this file were used to generate figures for the paper titled"Atmospheric chemistry of oxalate: Insight into the role of relative humidity and acidity from high-resolution observation."</p> <p>The datasets include the hourly concentration of atmospheric composition and meteorological parameters in Nanjing Area.</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>
Dataset for JGR Atmospheres manuscript " An Observational Constraint of VOC Emissions for Air Quality Modeling Study in the Pearl River Delta Region"
<p>The file includes hourly observations of ground-level ozone (O<sub>3</sub>) and nitrogen dioxide (NO<sub>2</sub>) concentrations at 56 environmental monitoring stations in the Pearl River Delta (PRD) region in China during June 2018. The units are in μg/m<sup>3</sup>.</p>
Ground-based observation data from the article " Elucidating the impacts of various atmospheric ventilation conditions on local and transboundary ozone pollution patterns: A case study of Beijing, China"
<p>Hourly ground-level O<sub>3</sub>, CO and NO<sub>2</sub> observational data were retrieved from the China Environmental Monitoring Station, ultimately comprising 714 stations in North China and 35 stations in Beijing after data quality control. The hourly data from meteorological stations in China were obtained from the China Meteorological Data Service Centre, including observational values of such weather elements as temperature, pressure, relative humidity, wind, total cloud cover, and precipitation.</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.