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2,170 results for “Cloud”
Arctic Gridded surface cloud fraction radiative kernels (GCF-CRKs)
<p><span>These <a name="OLE_LINK1"></a>gridded surface cloud fraction radiative kernels (GCF-CRKs) are created by integrating refined downwelling surface shortwave radiation (DSSR) estimates and a high-precision cloud fraction (CF). The DSSR is corrected by a CF-dependent model, which leveraging the correlation between the top-of-atmosphere (TOA) shortwave radiative parameters and surface radiation, combined with high-precision fused CF datasets from multiple satellite sources. </span></p> <p><span><span> </span>There are five individual files. “SFC_SW_Kernel_Arc.nc” is for CRKs of all clouds, “SFC_SW_lowcloud_Kernel_Arc.nc” is for CRKs of low-level clouds, “SFC_SW_midlowcloud_Kernel_Arc.nc” is for CRKs of mid-low-level clouds, “SFC_SW_midhighcloud_Kernel_Arc.nc” is for CRKs of mid-high-level clouds, and “SFC_SW_highcloud_Kernel_Arc.nc” is for CRKs of high-level clouds. The four cloud layers are derived from four pressure layers (surface to 700 hPa, 700-500 hPa, 500-300 hPa, and 300-50 hPa, representing low, middle-low, middle-high, and high clouds, respectively) based on the CERES-SYN stratification standard.</span></p> <p><span> </span></p> <p><span>The file format is netcdf4, and was created by Matlab. To read these files, any software supporting netcdf4 can be used. These files only involved sunlit months from Apr to Sep during 2000-2020, with the longitude from -180°~180° and the latitude from 60°N~90°N.</span></p>
RGB orthophoto mosaic, DSM, 3d point cloud and LIDAR LAZ of the flash flood damages in Karavelovo and Bogdan vilages, Bulgaria- September 2, 2022
<p>The present dataset contains geospatial resources aimed at investigating and assessing the consequences of a flash flood of debris flow character, relatively significant in extent and magnitude of damage, in the area of two villages in the Municipality of Karlovo, located in central Bulgaria, which happened on September 2, 2022. For this purpose, an integrated approach based on the combination of digital photogrammetry with high spatial resolution and spatial accuracy, based on a fixed wing unmanned aerial system, and laser altimetry (LIDAR), based on a multirotor unmanned platform, was used. The data collection was carried out 2 days after the occurrence of the disaster, resulting in the generation of valuable information resources that allow not only to spatially and quantitatively determine the damage of the disaster, but also to reveal the mechanism of occurrence of the phenomenon: 1) orthophoto mosaic, Digital surface model-DSM and 3D point cloud (from photogrammetry) 2) Classified 3D point cloud- from LIDAR survey.</p>
Simultaneous lidar and radar measurements for aeroso-cloud interaction studies
<p>Optical and microphysical aerosol and cloud properties derived from lidar and radar for aeroso-cloud interaction studies.</p>
Line luminosities of Galactic and Magellanic Cloud Wolf-Rayet stars
<p>Template (optical, continuum subtracted) emission line spectra of Milky Way and Magellanic Cloud Wolf-Rayet stars, presented in Appendix B of Crowther, Rate & Bestenlehner (2023, MNRAS, in press; https://arxiv.org/abs/2301.11297). Ascii format, wavelength (Angstrom) versus monochromatic luminosity (erg/s/Angstrom). Separate templates are provided for single-only and all (single+binary) since binaries are often contaminated by (Balmer) absorption lines from companion OB stars. Highly reddened sources (primarily in Milky Way) also exhibit strong interstellar features (CaII H&K, NaI D, DIBs at 4430, 5780, 5797 Angstrom) and some datasets involved have not been corrected for atmospheric telluric features. Owing to the heterogeneous origin of individual datasets, templates cover a range of wavelength regions, and some exclude the 6070-6400 Ang region, owing to detector gaps for ANU 2.3m + DBS spectroscopy. Separate templates are provided for each galaxy with the exception of WN/C and WO stars, which are also combined into a single dataset (owing to the low total numbers involved). Templates are degraded to a uniform resolution of 10 Angstrom, with average radial velocity corrections of 284 km/s (LMC) and 162 km/s (SMC) applied. Further details are provided in the README.txt file.</p> <p>Please cite Crowther, Rate & Bestenlehner in publications if these templates are used in research. </p>
Population genomics reveals differences in genetic structure between two endemic arboreal rodent species in threatened cloud forest habitat
<p>SNPs obtained by UNEAK pipeline for <em>Habromys schmidlyi </em>and <em>Reithrodontomys microdon</em>. </p> <p>Pleae cite as: </p> <p>Colunga-Salas P., T Marines-Macías, G Hernández-Canchola, S Barbosa, C Ramírez, JB Searle, L León-Paniagua. 2022. <strong>Population genomics reveals differences in genetic structure between two endemic arboreal rodent species in threatened cloud forest habitat</strong>. Mammalian Reasearch. Doi: 10.1007/s13364-022-00667-x</p>
Spatial and Temporal Availability of Cloud-free Optical Observations in the Tropics
<p>These data comprise three layers describing the spatial and temporal distribution of cloud-free optical observations over the tropics. The test datasets shared here are derived from the combination of Landsat and Sentinel-2 satellite data and represent a portion of the full datasets. The test data correspond to year 2020 over Mesoamerica. </p> <ul> <li>Spatial data: the Meso2020_validObs contains one band 'valid_obs' indicating the number of cloud-free observations at the pixel level. </li> <li>Temporal data: the maximumWaitDate2020 contains two bands 'max' and 'maxDay' corresponding to the number of maximum consecutive days without data in a year and final date in which the maximum number of consecutive days without data occurred, respectively.</li> </ul>
Sensible heat fluxes control cloud trail strength
<p>This dataset contains a minimal set of data used to create figures in Johnston et al. (2023) Sensible Heat Fluxes Control Cloud Trail Strength, Quarterly Journal of the Royal Meteorological Society. Additional liquid water path data from the control experiment (H250E250) is also provided to better visualise the cloud field in this central experiment.</p>
Measuring the counterion cloud of soft microgels using SANS with contrast variation
<p>The behavior of microgels and other soft and compressible colloidal particles depends on particle concentration in ways that are absent in their hard-particulate counterparts. For instance, poly-N-isopropylacrylamide (pNIPAM) microgels can spontanously deswell and reduce suspension polydispersity at high concentrations. Despite the pNIPAM network in these microgels is uncharged, the key to understand this distinct behavior relies on the existence of peripheric charged groups, which provide stability when deswollen, and the associated counterion cloud. When in close proximity, clouds of different particles overlap, effectively freeing the associated counterions, which are then able to exert an osmotic pressure that can potentially cause the microgels to change size. Up to to now, however, no direct measurement of such an ionic cloud exists, perhaps even for hard colloids, where it is referred to as electric double layer. Here, we use small-angle neutron scattering with contrast variation with different ions to isolate the change in the form factor directly related to the counterion cloud and obtain its radius and width. Our results highlight that modeling of microgel suspensions must unavoidably and explicitly consider the presence of this cloud, which is present for nearly all microgel particles synthesized today.</p>
Trajectory data with sensitivities to cloud microphysical parameters
<p>The netCDF-4 file "north_south_cluster.nc" contains twenty trajectories that are associated with the extratropical cyclone "Vladiana" which occurred between 22-25 September 2016 over the North Atlantic.</p> <p>The trajectories are selected such that ten start their fastest ascent in the south and the north, respectively. From those ten trajectories, five ascend slowly (slantwise), and five ascend fast (convective).</p> <p>"vis_example.nc" are twelve fast ascending trajectories that may be used to showcase different visual analysis methods. </p> <p>The data contains sensitivities of rain mass density (QR) to different parameters of cloud microphysical processes (variables starting with 'd'). The sensitivities are computed with algorithmic differentiation via Hieronymus et al. (2022). The trajectories are taken from a simulation by Oertel et al. (2020) using the NWP model COSMO version 5.1, where the online trajectory scheme by Miltenberger et al. (2013) was applied.</p>
Data for Figure 8 of Publication "Additional global climate cooling by clouds due to ice crystal complexity"
<p>This repository contains the data to produce Figure 8 in the paper:</p> <p>"Järvinen, E., Jourdan, O., Neubauer, D., Yao, B., Liu, C., Andreae, M. O., Lohmann, U., Wendisch, M., McFarquhar, G. M., Leisner, T., and Schnaiter, M.: Additional global climate cooling by clouds due to ice crystal complexity, Atmos. Chem. Phys., 18, 15767–15781, https://doi.org/10.5194/acp-18-15767-2018, 2018."</p> <p>Note that the scripts are to be found in the accompanying package (http://dx.doi.org/10.5281/zenodo.8095469)</p>
Data for figures in the Publication "The importance of mixed-phase and ice clouds for climate sensitivity in the global aerosol–climate model ECHAM6-HAM2"
<p>This repository contains the data to produce figures for the paper:</p> <p>"Lohmann, U. and Neubauer, D.: The importance of mixed-phase and ice clouds for climate sensitivity in the global aerosol–climate model ECHAM6-HAM2, Atmos. Chem. Phys., 18, 8807–8828, https://doi.org/10.5194/acp-18-8807-2018, 2018."</p> <p>Note that the scripts are to be found in the accompanying package (https://doi.org/10.5281/zenodo.8183412)</p>
Harmonised branded foods dataset for FNS Cloud case studies (2017-2020)
<p>The dataset was compiled by merging and harmonisation of different datasets, which were compiled in four countries (Netherlands, Germany, Slovenia, Switzerland) in period 2017-2020 with the use of different methodologies. Dataset was prepared to support conduction of case studies within the HORIZON2020 FNS Cloud project. The dataset includes selected mandatory food labelling information about nutrition composition of foods/drinks within three categories, that are important for reformulation strategies: soft drinks, breakfast cereals and yoghurts. For one of the included datasets, data aslo include category of fruit and vegetable juices (including nectars).</p>
Data of "Towards a More Reliable Forecast of Ice Supersaturation: Concept of a One-Moment Ice Cloud Scheme that Avoids Saturation Adjustment"
<p>These are the data used for generating the figures in the ACP article "Towards a More Reliable Forecast of Ice Supersaturation: Concept of a One-Moment Ice Cloud Scheme that Avoids Saturation Adjustment" by Sperber and Gierens.</p> <p>The data sets labeled "Box" have been generated by the stochastic box model, "adj" refers to the parameterisation using saturation adjustment and data labeled "par" originate from the newly developed parameterisation.</p> <p>The label "const" followed by a number refers to simulations with a constant updraught of the speed specified by the number in cm/s. The label "cos" refers to the simulations in which the updraught velocity follows a cosine function in time.</p> <p>"a10" labels simulations with less initial clear sky humidity fluctuations of plus/minus 10% instead of plus/minus 25%. "al0028" labels simulations with a higher deposition rate of 0.0028 1/s instead of 0.0003 1/s. "step10" labels simulations with a longer time step of 10 minutes instead of 1 minute.</p> <p>"Box_const2_rh1.txt" contains data from a simulation similar to "Box_const2.txt" but with an initial mean relative humidity of 100% instead of 110%. "Box_het.txt" contains data from a simulation including heterogeneous nucleation. "Box_slow_nuc.txt" contains data from a simulation where the deposition rate increases over time from zero after nucleation in every air parcel. "Box_upvar.txt" contains data from a simulation, where the updraught velocity in every air parcel varies randomly between 1 cm/s and 3 cm/s and the deposition rate inside the air parcel depends on the updraught velocity at the time of nucleation.</p> <p> </p> <p>The columns in the "Box" files represent from left to right:</p> <p>1. Time since the simulation start in s</p> <p>2. Cloud fraction</p> <p>3. Mean relative humidity across all air parcels</p> <p>4. Mean specific humidity across all air parcels</p> <p>5. Mean specific ice content across all air parcels</p> <p>6. Mean relative humidity across all cloudy air parcels</p> <p>7. Mean relative humidity across all clear air parcels</p> <p>8. Mean equilibrium supersaturation</p> <p>9. Mean threshold relative humidity for homogeneous nucleation</p> <p>10. Mean deposition rate across all cloudy air parcels</p> <p>11. Mean updraught velocity</p> <p> </p> <p>The columns in the "adj" files represent from left to right:</p> <p>1. Time since the simulation start in s</p> <p>2. Cloud fraction</p> <p>3. Mean relative humidity</p> <p>4. Mean specific humidity</p> <p>5. Mean specific ice content</p> <p>6. In-cloud Humidity</p> <p>7. Clear sky humidity</p> <p> </p> <p>The columns in the "par" files represent from left to right:</p> <p>1. Time since the simulation start in s</p> <p>2. Cloud fraction</p> <p>3. Mean relative humidity</p> <p>4. Mean specific humidity</p> <p>5. Mean specific ice content</p> <p>6. In-cloud Humidity</p> <p>7. Clear sky humidity</p> <p>8. Obsolete</p> <p>9. Equilibrium supersaturation</p>
Structural Diversity from the NEON Discrete-Return LiDAR Point Cloud in 2013-2022
Structural diversity, characterizing the volumetric capacity and physical arrangement of biotic components in an ecosystem, controls critical ecosystem functions like light interception, hydrology, and microclimate. This product generates structural diversity metrics for the NEON sites, sourced from the Discrete-Return LiDAR Point Cloud from the NEON Aerial Observation Platform (DP1.30003.001; collected in March 2023). Using R programming, we computed the metrics detailing height, heterogeneity, and density at 30 m, aligned to the Landsat grids, for 243 site years in 57 NEON sites from 2013 to 2022.
Sparse point clouds derived from UAV overflights of the fifteen NPP study sites at Jornada Basin LTER in 2019
This data package contains sparse point clouds for each of the 15 NPP study sites at the Jornada Basin LTER in southern New Mexico, USA. The point clouds were derived from raw images collected during uncrewed aerial vehicle (UAV) overflight missions conducted in late summer and early autumn of 2019 (1 overflight day per site). For each site one or two missions were flown during an afternoon using a DJI Phantom 4 UAV, and between 450 and 1300 12.4 megapixel RGB images were captured. A subset of images captured at each site were loaded into Agisoft Metashape software to derive points using a structure from motion method. The raw images are not provided but can be made available via project PIs. This data package includes point clouds for all 15 sites as geotiff raster files. Other derived products from these UAV missions, including orthomosaic photos, digital elevation models, and digital terrain models, are available in other EDI data packages (knb-lter-jrn.210543001, knb-lter-jrn.210543002, and knb-lter-jrn.210543003, respectively). This study is complete.
Vegetation profile and canopy height of Tabonuco, Colorado and Cloud Forests
The overall height and the presence of vegetation in different height intervals above ground is recorded above points on a 5 x 5 m grid in three hectare-sized plots in the Luquillo Experimental Forest, Puerto Rico. One plots each is in tabonuco forest (350 m elevation), colorado forest (650 m) and cloud forest (1000 m). The first records were made shortly before Hurricane Hugo, in 1989. Post hurricane measurements have been made five time to six time in each plot, the most recent been in 1998 for all plots. The results show the damage to forest structure wreaked by Hurricane Hugo, steady recovery of forest structure after Hugo, minor damage by Hurricane Hortense in 1996, and resumption of recovery after Hortense. Maximum canopy height and the vertical distribution of vegetation in the forests are returning to pre-hurricane Hugo values. Support for this work was provided by grants BSR-8811902, DEB-9411973, DEB-9705814 , DEB-0080538, DEB-0218039 , DEB-0620910 , DEB-1239764, DEB-1546686, and DEB-1831952 from the National Science Foundation to the University of Puerto Rico as part of the Luquillo Long-Term Ecological Research Program. Additional support provided by the University of Puerto Rico and the International Institute of Tropical Forestry, USDA Forest Service.
Photogrammetric Point Cloud and DSM from UAV campaign at Niwot Ridge, 2017.
Elevation data from 14 August 2017 collected as part of unmanned aerial vehicle (UAV)/drone campaign during Summer 2017. Investigating snow depth varaibility and spatiotemporal variations and controls on vegetation productivity within the Niwot Ridge LTER Saddle Catchment.
alexandru-uta/cloud_network_variability_data: Cloud Network Variability Data
<p># Dataset on Cloud Network Variability</p> <p>This is the dataset obtained while benchmarking public and private clouds for the following article:</p> <p>### Alexandru Uta, Alexandru Custura, Dmitry Duplyakin, Ivo Jimenez, Jan Rellermeyer, Carlos Maltzahn, Robert Ricci, Alexandru Iosup. Is Big Data Performance Reproducible in Modern Cloud Networks?. In proceedings of 17th USENIX Symposium on Networked Systems Design and Implementation (NSDI), 2020, February 25-27, Santa Clara, USA.</p> <p>The dataset contains bandwidth variability data for:<br> - Amazon EC2<br> - Google Compute Engine<br> - Microsoft Azure (limited data)<br> - Scaleway (limited data)<br> - SURFsara HPCCloud</p> <p>The dataset contains TCP latency (RTT) data for:<br> - Amazon EC2<br> - Google Compute Engine</p> <p>The dataset contains data regarding token bucket sizes (explained in depth in the aforementioned article) for Amazon EC2.</p> <p>Please note that the full archive is over 3.5 GB of data, made up of hundreds of thousands of small files. This is why we decided to archive the data, which we then split into smaller files, to accommodate for the Github max file size of 100 MB.</p> <p>===== DETAILS ON THE ARCHIVE FORMAT =====</p> <p><strong>1. The Bandwidth Variability data:</strong><br> - archived in the bandwidth_variability_data.tar.bz2.parta, .partb, .partc<br> - after unpacking the archive, the data is split in directories per machine type, and experiment type, for example:<br> --- perfvar-aws-m5xlarge-fullspeed: contains iperf3 output files for continuous communication between 2 m5.xlarge VMs in Amazon EC2.<br> --- perfvar-google-4cpu-bursty-5s30s: contains iperf3 output files for bursty communication (5 seconds communication, 30 seconds break; repeat)</p> <p><strong>2. The Latency Variability data:</strong><br> - archived in the file latency_study.tar.bz2<br> - after unpacking the archive, the directories contain TCP dump RTT data and iperf3 outputs</p> <p><strong>3. The Token Bucket AWS study:</strong><br> - archived in the file token_bucket_study.tar.bz2<br> - contains files of form INSTANCE_TYPE-REGION-TIMESTAMP.{bw, raw, tb}<br> - files with extension .raw contain raw iperf3 client output<br> - files with extension .bw contain bandwidth samples taken at 1 second intervals from the iperf3 utility<br> - files with extension .tb contain a triple of form <time_to_token_bucket_depletion, high_token_bucket_bandwidth, low_token_bucket_bandwidth><br> - example of a file name: c5.2xlarge-us-west-1-1567733720.raw</p> <p> </p>
CESM2 cloud locking suite single-level fields
<p>Selected single-level Community Atmosphere Model version 6 (CAM6) fields from four simulations of the Community Earth System Model version 2.0.1 (CESM2). Names of the simulations as they appear in the GRL manuscript (and corresponding native case name) are: CTL (B1850_c201_CTL), CLOCK (B1850_c201_CLOCK), FCTL (F1850JJB_c201_CTL), and FLOCK (F1850JJB_c201_CLOCK). Note that CLOCK only spans 24 yr while the others span 25 yr. All simulations are forced by prescribed pre-industrial atmospheric composition. CTL and CLOCK use prognostic atmosphere, ocean, land, and sea ice models; FCTL and FLOCK use prescribed monthly mean sea-surface temperatures and sea ice concentrations taken from CTL. In CLOCK and FLOCK, cloud properties seen by the radiation scheme are prescribed ("locked") and are sourced from randomly selected years of a 3-year data pool comprised of Years 20-22 of CTL.</p>
CESM2 cloud locking suite multi-level fields for FCTL simulation
<p>Selected multi-level Community Atmosphere Model version 6 (CAM6) fields from the FCTL simulation of the Community Earth System Model version 2.0.1 (CESM2). The simulation is labeled "FCTL" in the GRL manuscript but has a native case name of "F1850JJB_c201_CTL" in the file names. FCTL is forced by prescribed pre-industrial atmospheric composition, and monthly mean sea-surface temperatures and sea ice concentrations taken from an existing pre-industrial fully coupled simulation ("CTL").</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.