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2,170 results for “clouds”
Simulation results from WRF-Chem with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China during January-March 2017
<p>Simulation results from WRF-Chem with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China during January-March 2017 including meteorology and air quality, which are in wrfout_YYYY-MM-DD_00_00_00 file on each day during January-March 2017. The file contains these variables:</p> <p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB, PM2_5_DRY, o3, no2, so2, co, nh3, EXTCOF55 and ALT.</p>
Simulation results from WRF-Chem with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China during October-December 2017
<p>Simulation results from WRF-Chem with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China during October-December 2017 including meteorology and air quality, which are in wrfout_YYYY-MM-DD_00_00_00 file on each day during October-December 2017. The file contains these variables:</p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB, PM2_5_DRY, o3, no2, so2, co, nh3, EXTCOF55 and ALT.
Simulation results from WRF-Chem with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China during April-June 2017
<p>Simulation results from WRF-Chem with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China during April-June 2017 including meteorology and air quality, which are in wrfout_YYYY-MM-DD_00_00_00 file on each day during April-June 2017. The file contains these variables:</p> <p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB, PM2_5_DRY, o3, no2, so2, co, nh3, EXTCOF55 and ALT.</p>
Simulation results from WRF-Chem with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China during July-September 2017
<p>Simulation results from WRF-Chem with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China during July-September 2017 including meteorology and air quality, which are in wrfout_YYYY-MM-DD_00_00_00 file on each day during July-September 2017. The file contains these variables:</p> <p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB, PM2_5_DRY, o3, no2, so2, co, nh3, EXTCOF55 and ALT.</p>
Indoor point cloud dataset for BIM related applications
<p>Annotated point cloud of the CRAS labs@FEUP. The point cloud is in ASCII format. Variables: Point X coordinate (m); Point Y coordinate (m); Point Z coordinate (m); Point colour (R); Point colour (G); Point colour (B); Intensity; Label. A total of 21 scans were made, producing 584,701,977 points. The point clouds from the 21 scans were registered with Leica Cyclone Register 360 (3 mm average error). The points were labeled according to 33 classes: 0-unassigned; 1-ceiling; 2-floor; 3-wall; 4-door; 5-window; 6-desk; 7-chair; 8-cabinet; 9-mobile cabinet; 10-shelf; 11-vents; 12-water tank; 13-bin; 14-box; 15-board; 16-computer; 17-screen; 18-printer; 19-vest; 20-switch; 21-paper dispenser; 22-alcohol dispenser; 23-cable; 24-phone; 25-robot; 26-water kettle; 27-stairs; 28-ladder; 29-oil heater; 30-divider; 31-hanger; 32-fan; 33-water dispenser.</p> <p>Additionally, the as-built IFC model of the space is provided in order to test Scan-to-BIM and Scan-vs-BIM algorithms.</p>
Simulation results from WRF-CHIMERE with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China for January-March 2017
<p>Simulation results from WRF-CHIMERE with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China for January-March 2017 including meteorology and air quality:</p><p>YYYYMM_wrfout.zip:</p><p>Meteorological file including wrfout_YYYY-MM-DD_00_00_00 on each day for January-March 2017, which contains these variables:</p><p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB</p><p>YYYYMM_out.zip</p><p>Air quality file including out.YYYYMMDD00_24_PHD.nc on each day for January-March 2017, which contains these variables:</p><p>PM25, O3, SO2, NO2, CO and optdaero.</p>
Simulation results from WRF-CHIMERE with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China for October-December 2017
<p>Simulation results from WRF-CHIMERE with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China for October-December 2017 including meteorology and air quality:</p><p>YYYYMM_wrfout.zip:</p><p>Meteorological file including wrfout_YYYY-MM-DD_00_00_00 on each day for October-December 2017, which contains these variables:</p><p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB</p><p>YYYYMM_out.zip</p><p>Air quality file including out.YYYYMMDD00_24_PHD.nc on each day for October-December 2017, which contains these variables:</p><p>PM25, O3, SO2, NO2, CO and optdaero.</p>
Simulation results from WRF-CHIMERE with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China for July-September 2017
<p>Simulation results from WRF-CHIMERE with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China for July-September 2017 including meteorology and air quality:</p><p>YYYYMM_wrfout.zip:</p><p>Meteorological file including wrfout_YYYY-MM-DD_00_00_00 on each day for July-September 2017, which contains these variables:</p><p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB</p><p>YYYYMM_out.zip</p><p>Air quality file including out.YYYYMMDD00_24_PHD.nc on each day for July-September 2017, which contains these variables:</p><p>PM25, O3, SO2, NO2, CO and optdaero.</p>
Simulation results from WRF-CHIMERE with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China for April-June 2017
<p>Simulation results from WRF-CHIMERE with enabling aerosol-radiation interactions and aerosol-cloud interactions in eastern China for April-June 2017 including meteorology and air quality:</p><p>YYYYMM_wrfout.zip:</p><p>Meteorological file including wrfout_YYYY-MM-DD_00_00_00 on each day for April-June 2017, which contains these variables:</p><p>T2, Q2, PSFC, U10, V10, RAINC, RAINSH, RAINNC, PBLH, SWDOWN, GLW, SWUPT, LWUPT, CLDFRA, QCLOUD, P, PB, T, VAPOR, PH, PHB</p><p>YYYYMM_out.zip</p><p>Air quality file including out.YYYYMMDD00_24_PHD.nc on each day for April-June 2017, which contains these variables:</p><p>PM25, O3, SO2, NO2, CO and optdaero.</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>
Synthetically generated clouds on ground-based solar observations
<p>A dataset consisting of Ca II & H-alpha images taken at the Paris Meudon Observatory. Synthetically generated cloud coverage has been applied to clean images, thereby creating an (cloudy, clean) pair--facilitating the training of cloud-removal algorithms.</p> <p><strong>Data description</strong></p> <p>The Ca-II and H-α synthetic dataset comprise respectively 319 and 367 pairs of shadow/shadow-free images, split into 223/96 and 256/111 training/testing pairs.</p> <p>Listed here are two zip archives:</p> <ol> <li>filament-bounding-boxes.zip -- bounding boxes of filaments that were used to compute the patched metrics.</li> <li>synthetic-clouds.zip -- the cloudy input/clean output images that are used to train machine learning algorithms.</li> </ol> <p>A PyTorch dataset has been created that handles the download, importing, and usage of this dataset. You can find this code at the github repository: <a href="https://github.com/jaypmorgan/cloud-removal">https://github.com/jaypmorgan/cloud-removal</a></p> <p><strong>Pre-processing routines</strong></p> <p>To generate this set of data, we have applied a series of pre-processing routines. These are:</p> <ol> <li>Correct determination of the solar limb (source code can be found at: <a href="https://gitlab.lis-lab.fr/presage/solar-limb-detection">https://gitlab.lis-lab.fr/presage/solar-limb-detection</a>).</li> <li>Scaling the solar disk to 420 pixels, and centring it at 511.5 pixels in the x and y dimensions.</li> <li>Setting background values outside the solar disk to 0.</li> <li>Normalising the disk intensity values into the range of 0-1.</li> </ol>
Data - A Functionalized Monte Carlo 3D Radiative Transfer Model: Radiative Effects of Clouds over Reflecting Surfaces
<p>Data and scripts associated with the article "A Functionalized Monte Carlo 3D Radiative Transfer Model: Radiative Effects of Clouds over Reflecting Surfaces"</p>
Points cloud of 3D CAD models of artificial reefs designed by E.Riera from "Unleashing the Potential of Artificial Reefs Design" (txt files)
<p>Here you will find points cloud of 3D CAD models in txt format.</p> <p>These are 3D CAD models of artificial reefs designed by E.Riera from the paper "Unleashing the Potential of Artificial Reefs Design" (<a href="https://doi.org/10.32942/X2G300">https://doi.org/10.32942/X2G300</a>).</p> <p>These txt files (with others not available on open access) have been used to run a script on R "Computation of fractal dimension" to compute the fractal dimension from the point clouds using the Minkowski-Bouligand method (or "box-counting") using the R statistical framework (version 4.0.3) and “est.boxcount” function of the package “Rdimtools” (You and Shung, 2022). </p>
A 1D Model for Nucleation of Ice from Aerosol Particles: An Application to a Mixed-Phase Arctic Stratus Cloud Layer
<p>A 1D Model for Nucleation of Ice from Aerosol Particles:</p> <p>An Application to a Mixed-Phase Arctic Stratus Cloud Layer</p> <p>Daniel A. Knopf<sup>1</sup>*, Israel Silber<sup>2</sup>, Nicole Riemer<sup>3</sup>, Ann M. Fridlind<sup>4</sup>, Andrew S. Ackerman<sup>4</sup></p> <p><sup>1</sup>School of Marine and Atmospheric Sciences, Stony Brook University, Stony Brook, NY, USA</p> <p><sup>2</sup>Department of Meteorology and Atmospheric Science, Pennsylvania State University, University Park, PA, USA</p> <p><sup>3</sup>Department of Atmospheric Sciences, University of Illinois at Urbana–Champaign, Urbana, IL, USA</p> <p><sup>4</sup>NASA Goddard Institute for Space Studies, New York, NY, USA</p> <p> </p> <p>Corresponding author: Daniel Knopf (daniel.knopf@stonybrook.edu)</p> <p> </p> <p><strong>This repository contains all model output data in netCDF format to reproduce simulation results and corresponding figures given in above listed publication.</strong></p> <p>File name description:</p> <p>Type of parameterization: <em>INN, INAS, ABIFM</em></p> <p>INP treatment, diagnostic or prognostic: <em>diag, prog</em></p> <p>Aerosol size distribution:</p> <p><em>05mu</em>: monodisperse 0.5 μm diameter</p> <p><em>15mu</em>: monodisperse 0.5 μm diameter</p> <p><em>polydisperse</em>: polydisperse particle size distribution</p> <p>Model initialization and thermodynamic data: <em>model_data</em></p> <p>Aerosol/INP data: <em>aerosol_data</em>, <em>polydisperse_data</em></p> <p>Change in ice nucleation efficiency: <em>10X, 100X</em></p>
Aerosol indirect-effect-derived PM2.5 contributions during the development of convective clouds over the Yellow Sea in July of 2017
<p>Meteorological processes are investigated during the development of deep convective clouds over the Yellow Sea inside a quasi-stationary rainy front in the East Asian region. WRF-Chem simulations were conducted to assess the aerosol-indirect-effect (AIE)-derived PM<sub>2.5</sub> contributions to the cloud microphysical process in deep convective clouds and the associated precipitation.</p> <p>In our dataset, you can find output of WRF-Chem baseline simulations (BASE) and other sensitivity simulated outputs (CONTROL). Furthermore, model variables of precipitations, cloud top temperature (CTT), surface-level pressure (SLP), convective available potential energy (CAPE), and hydrometer mixing ratios of cloud water (qcloud), raindrops (qrain), and ice (qice) can be found in these zip files.</p> <p>If you have any difficulty downloading these datasets, please feel free to contact the first author, kimhsung@gmail.com. All references and acknowledgments can be found in our paper</p>
Radiative transfer model and datasets for Li et al. (2023), 'Wintertime low-level clouds over sea ice cool the Arctic climate system'
<p>Source code for the radiative transfer model (RAPRAD) and cloud radiative flux data used in the study Li et al. (2022).</p>
Location-Aware Container Scaling (LACS) in Geo-distributed Clouds
<p>Datasets and code for the problem of location-aware container scaling (LACS) in geo-distributed clouds:</p> <p>Randomly extracted one day’s workload from WikiBench and NASA HTTP: AppWorkload.py</p> <p>Facebook subscribers by January 2020 to simulate the distribution of application requests among different user regions: FacebookUserData.csv</p> <p>Sprint IP Network location as 82 user regions from 35 countries on 6 continents: SprintLocation.csv</p> <p>Observation on the network latency matrix among 82 user centres: LatencyMatrix.py</p> <p>Representative code using openAI's gym environment: deepscale.py</p>
NPM3D dataset with instance label. Dataset used in paper "A Review of Panoptic Segmentation for Mobile Mapping Point Clouds"
<p>NPM3D is a public benchmark for point cloud semantic segmentation, with 10 classes including: ground, building, pole (road sign and traffic light), bollard, trash can, barrier, pedestrian, car, natural (vegetation) and unclassified. Results are evaluated only w.r.t. 9 classes, disregarding the "unclassified" label. The data has been captured with a mapping-grade mobile laser scanning system in different cities in France. There are 4 regions designated for training, all captured in Paris and Lille; and 3 regions for testing, captured in Dijon and Ajaccio. The standard 10-class version described above has actually been derived from a more fine-grained version of the dataset by keeping only the most frequent labels. The original annotations feature 50 different semantic classes (most of which are very rare), and also individual object instance labels for the training regions. For panoptic segmentation, a new version has been generated that still uses the 10 semantic category labels listed above, but also includes instance labels. The classes ground, building and barrier are considered "stuff" and are not separated into instances. As no instance labels are available for the 3 test regions, our version for panoptic (or pure instance) segmentation only contains 4 different regions from Paris and Lille. Instead of a fixed training/test split all experiments therefore use 4-fold cross-validation.</p>
Dataset: Periodic variation of mesoscale ultraviolet contrast at the cloud top of Venus
<p>Filename:<br> mean_A_283.txt : wavelength of 283 nm<br> mean_A_365.txt : wavelengths of 365 nm</p> <p>Description:<br> Empirical equigonal UV albedo of Venus as a function of the phase angle determined from Akatsuki UVI data.</p> <p>Format (text file): <br> 1st column: Phase angle (deg)<br> 2nd column: A:Mean albedo<br> 3rd column: SD:Standard deviation</p> <p> </p> <p>Filename:<br> period1_283nm.txt : Period 1, 283 nm<br> period1_365nm.txt : Period 1, 365 nm<br> period2_283nm.txt : Period 2, 283 nm<br> period2_365nm.txt : Period 2, 365 nm</p> <p>Description:<br> Time series of the standard deviation of mesoscale UV contrast and the mean brightness of Venus.</p> <p>Format (text file):<br> 1st column: Image file name<br> 2nd column: Elapsed time (h)<br> 3rd column: Phase angle (deg)<br> 4th column: Standard deviation (W/m2/str/m)<br> 5th column: Standard deviation in AM<br> 6th column: Standard deviation in PM<br> 7th column: Mean brightness (W/m2/str/m)<br> 8th column: Mean brightness in AM<br> 9th column: Mean brightness in PM</p> <p> </p> <p>Filename:<br> uvi_20160425_131340_283_l3b_v10.nc<br> uvi_20160425_151341_283_l3b_v10.nc<br> uvi_20160425_171339_283_l3b_v10.nc<br> uvi_20160506_101341_283_l3b_v10.nc<br> uvi_20160506_121340_283_l3b_v10.nc<br> uvi_20160506_141343_283_l3b_v10.nc<br> uvi_20160517_141339_283_l3b_v10.nc<br> uvi_20160517_161339_283_l3b_v10.nc</p> <p>Description:<br> 2.02-micrometer mapped data taken by the IR2 camera onboard Akatsuki in Level-3 netCDF format. The data have been corrected for the point-spread function, the flat-field pattern, and the detector temperature dependence by Sato et al. (2020, Icarus, 345, 113682, https://doi.org/10.1016/j.icarus.2020.113682).</p> <p>Format (binary file):<br> netCDF<br> The file names indicate the year, date and time of each observation.</p> <p> </p>
AT3D-PART2: Stochastically Generated Clouds
<p>The extinction fields are in netCDF format. They can be read with xarray using python, for example.</p> <p>Associated with:</p> <p>Loveridge, J., Levis, A., Di Girolamo, L., Holodovsky, V., Forster, L., Davis, A. B., and Schechner, Y. Y.: Retrieving 3D distributions of atmospheric particles using Atmospheric Tomography with 3D Radiative Transfer – Part 2: local optimization, Atmos. Meas. Tech. Discuss. [preprint], https://doi.org/10.5194/amt-2023-44, in review, 2023</p> <p>Data Generated using:</p> <p>Loveridge, J., Levis, A., Aides, A., Forster, L., and Holodovsky, V.:</p> <p>Atmospheric Tomography with 3D Radiative Transfer, v4.1.2,</p> <p>Zenodo [code], https://doi.org/10.5281/zenodo.7062466, 2022.</p> <p> </p>
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)
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.