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2,170 results for “clouds”
CERES and GEO-Enhanced TOA, Within-Atmosphere and Surface Fluxes, Clouds and Aerosols 3-Hourly Terra Edition4A
CER_SYN1deg-3Hour_Terra-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top of Atmosphere (TOA), Within-Atmosphere and Surface Fluxes, Clouds and Aerosols 3-Hourly Terra Edition4A data product, which was collected using Imaging Radiometers on the Geostationary Satellites platform and CERES Flight Model 1 (FM1), CERES FM2, CERES Scanner, and MODIS on Terra. Data collection for this product is complete.The CERES Synoptic (SYN) 1 degree (SYN1deg) products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, surface fluxes, and computed fluxes adjusted or constrained to the CERES-observed TOA fluxes. The calculated fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the NASA Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a three-hourly temporal resolution and 1°-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions.CERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to accurately model variability between CERES observations. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrowband to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique ensures GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5°x5 ° latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1° equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation.CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the protoflight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit on board the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched on board Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched on board the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched on board the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.
THIR/Nimbus-7 Level 1 Cloud Data for SBUV/TOMS V001 (THIRN7L1BCLT) at GES DISC
THIRN7L1BCLT is the Nimbus-7 Temperature-Humidity Infrared Radiometer (THIR) Level 1 Cloud Data for SBUV/TOMS (BCLT) product and contains total cloud amounts; radiances at three cloud altitudes: low (below 2km), middle (2 to 7 km depending on latitude), and height (above the middle cloud layer); cirrus and deep convective clouds; and mean and RMS deviations of cloud and surface radiances. Data are averaged orbit by orbit onto each of the Nimbus 7 TOMS IFOV, which vary from 50 km to 50 km at nadir to 200 km x 200 km at the edges, as well as SBUV 180 km x 180 km IFOVs. The BCLT product includes improved cloud estimation compared to the earlier CLT product.Each file contains one day of data (~14 orbits per day). Spatial coverage is global. The data are available from October 31, 1978 (day of year 304) through October 27, 1984 (day of year 301).This product was previously available from the NSSDC with the identifier ESAD-00196 (old ID 78-098A-10E)
CERES and GEO-Enhanced TOA, Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Monthly-Averaged 1-Hourly Terra Edition4A
CER_SYN1deg-MHour_Terra-MODIS_Edition4A is the Clouds and the Earth's Radiant Energy System (CERES) and geostationary (GEO)-Enhanced Top-of-Atmosphere (TOA), Within-Atmosphere and Surface Fluxes, Clouds and Aerosols Monthly-Averaged 1-Hourly Terra Edition4A data product. Data was collected using the CERES Imaging Radiometers on the Geostationary Satellites platform and CERES Flight Model 1 (FM1), FM2, CERES Scanner, and Moderate-Resolution Imaging Spectroradiometer (MODIS) on the Terra platform. Data collection for this product is complete.The CERES Synoptic (SYN) 1 degree (SYN1deg) products provide CERES-observed temporally interpolated TOA radiative fluxes and coincident MODIS-derived cloud and aerosol properties and include geostationary-derived cloud properties and broadband fluxes that have been carefully normalized with CERES fluxes to maintain the CERES calibration. They also contain computed initial TOA, in-atmosphere, and surface fluxes and computed fluxes that have been adjusted or constrained to the CERES-observed TOA fluxes. The computed fluxes are produced using the Langley Fu-Liou radiative transfer model. Computations use MODIS, geostationary satellite cloud properties, and atmospheric profiles provided by the Global Modeling and Assimilation Office (GMAO). The adjustments to clouds and atmospheric properties are also provided. The computations are made for all-sky, clear-sky, pristine (clear-sky without aerosols), and all-sky without aerosol conditions. This product provides parameters on a monthly-averaged one-hourly temporal resolution and 1°-regional spatial scales. Fluxes are provided for clear-sky and all-sky conditions in the longwave (LW), shortwave (SW), and window (WN) regions.The CERES SYN1deg products use 1-hourly radiances and cloud property data from geostationary (GEO) imagers to model variability between CERES observations accurately. Several steps are involved in using GEO data to enhance diurnal sampling. First, GEO radiances are cross-calibrated with the MODIS imager using only data that is coincident in time and ray-matched in angle. Next, the GEO cloud retrievals are inferred from the calibrated GEO radiances. The GEO radiances are converted from narrowband to broadband using empirical regressions and then to broadband GEO TOA fluxes using Angular Distribution Models (ADMs) and directional models. A normalization technique is used to ensure GEO and CERES TOA fluxes are consistent. Instantaneous matched gridded fluxes from CERES and GEO are regressed against one another over a month from 5°x5 ° latitude-longitude regions. The regression relation is then applied to all GEO fluxes to remove biases that depend upon cloud amount, solar and view zenith angles, and regional dependencies. The regional means are determined for 1° equal-angle grid boxes calculated by first interpolating each parameter for any missing times of the CERES/GEO observations to produce a complete 1-hourly time series for the month. Monthly means are calculated using the combination of observed and interpolated parameters from all days containing at least one CERES observation.CERES is a key Earth Observing System (EOS) program component. The CERES instruments provide radiometric measurements of the Earth's atmosphere from three broadband channels. The CERES missions follow the successful Earth Radiation Budget Experiment (ERBE) mission. The first CERES instrument, the proto flight model (PFM), was launched on November 27, 1997, as part of the Tropical Rainfall Measuring Mission (TRMM). Two CERES instruments (FM1 and FM2) were launched into polar orbit onboard the Earth Observing System (EOS) flagship Terra on December 18, 1999. Two additional CERES instruments (FM3 and FM4) were launched onboard Earth Observing System (EOS) Aqua on May 4, 2002. The CERES FM5 instrument was launched onboard the Suomi National Polar-orbiting Partnership (NPP) satellite on October 28, 2011. The newest CERES instrument (FM6) was launched onboard the Joint Polar-Orbiting Satellite System 1 (JPSS-1) satellite, now called NOAA-20, on November 18, 2017.
GPM GROUND VALIDATION NCAR CLOUD MICROPHYSICS PARTICLE PROBES MC3E
The GPM Ground Validation NCAR Cloud Microphysics Particle Probes MC3E dataset was collected during the Midlatitude Continental Convective Clouds Experiment (MC3E), which took place in central Oklahoma during the April-June 2011 period. The overarching goal was to provide the most complete characterization of convective cloud systems, precipitation, and the environment that has ever been obtained, providing constraints for model cumulus parameterization and space-based rainfall retrieval algorithms over land that had never before been available. The GPM Ground Validation NCAR Cloud Microphysics Particle Probes MC3E dataset was obtained from three instruments carried aboard the University of North Dakota (UND) Cessna Citation aircraft. These probes, the 2D-C, Cloud Imaging Probe (CIP) and High Volume Precipitation Spectrometer (HVPS-3), collected particle size distributions and particle images which were processed by the National Center for Atmospheric Research (NCAR). Data were collected April 22, 2011 through June 2, 2011.
AIRS/Aqua L2 Cloud-Cleared Infrared Radiances (AIRS+AMSU+HSB) V006 (AIRH2CCF) at GES DISC
The Atmospheric Infrared Sounder (AIRS) is a grating spectrometer (R = 1200) aboard the second Earth Observing System (EOS) polar-orbiting platform, EOS Aqua. In combination with the Advanced Microwave Sounding Unit (AMSU) and the Humidity Sounder for Brazil (HSB), AIRS constitutes an innovative atmospheric sounding group of visible, infrared, and microwave sensors. This product is similar to AIRI2CCF. However, it contains science retrievals that use the HSB. Because the HSB instrument lived only from September 2002 through January 2003 when it terminally failed, the data set covers these five months only. Cloud-Cleared Radiances contain calibrated, geolocated channel-by-channel AIRS infrared radiances (milliWatts/m2/cm-1/steradian) that would have been observed within each AMSU footprint if there were no clouds in the FOV and produced along with the AIRS Standard Product, as they are the radiances used to retrieve the Standard Product. Nevertheless, they are an order of magnitude larger in data volume than the remainder of the Standard Products and, many Standard Product users are expected to have little interest in the Cloud Cleared Radiance. For these reasons they are a separate output file, but like the Standard Product, are generated at all locations.An AIRS granule has been set as 6 minutes of data, 30 footprints cross track by 45 lines along track for each of the approximate 2378 channels. There are 240 granules per day, with an orbit repeat cycle of approximately 16 day.
A combinatorial screen of the CLOUD uncovers a synergy targeting the androgen receptor
GEO Series GSE94783. Homo sapiens. 8 samples. Type: Expression profiling by high throughput sequencing.
A Prader-Willi locus lncRNA cloud modulates diurnal genes and energy expenditure.
GEO Series GSE43575. Mus musculus. 15 samples. Type: Genome binding/occupancy profiling by high throughput sequencing; Expression profiling by high throughput sequencing.
Spatial distribution and impacts of aerosols on clouds under Meiyu frontal weatherbackground over central China based on aircraft observations
<p>Data from six flights carried out across sixdays were selected for this study.</p>
Air parcels extracted from LES simulations of a deep convective cloud observed during DC3 field campaign
<p>The dataset consists of 500 files describing air parcels retrieved from the simulation of the deep convective cloud event observed on May 29, 2012, during the DC3 field campaign. Values for coordinate (X, Y, Z), velocity components (U, V, W), potential temperature (PTIL), total water content (QT), water vapor (QV), condensate density (QC), precipitation density (QR), net precipitation change (QR_FLUX), relative humidity (RH), buoyancy (BUOY), temperature (T), pressure (P), resolved TKE (KRES), SGS TKE (SGS_TKE), turbulent diffusion (TDIFF) are provided for each parcel at every time step of the simulation.</p>
GOES-16 cloud-motion wind and ASCAT ocean surface wind data for the article "Evolution of an atmospheric Kármán vortex street from high-resolution satellite winds: Guadalupe Island case study"
<p>This repository contains GOES-16 cloud-motion winds and ASCAT ocean surface winds derived for and analysed in the article "Evolution of an atmospheric Kármán vortex street from high-resolution satellite winds: Guadalupe Island case study".</p> <p> </p> <p><strong>GOES-16 Local Cloud-Motion Vectors</strong></p> <p>Data in two ASCII text files: <em>raw5x5g16b2_2018d129_1437z_2232z_north.txt</em> and <em>raw5x5g16b2_2018d129_1437z_2232z_south.txt</em>, with the former containing data for the upper half and the latter for the lower half of the study domain between ~26<sup>o</sup>N and ~29.5<sup>o</sup>N. Both files include 96 records, each record corresponding to a specific 5-minute time interval between 14:37 UTC and 22:32 UTC on 9 May 2018—9 May is day of year 129. The start and end times are given at the beginning of each record in YYYYDDDHHMM format, where Y is year, D is day of year, H is hour, and M is minute. For example, the first record contains data between 14:37 UTC and 14:42 UTC, as indicated by the start and end times of 20181291437 and 20181291442. Then follows the four column headers LAT LON SPD DIR, corresponding to latitude (degree), longitude (degree), wind speed (m/s), and wind direction (meteorological convention, degree north), respectively—note that no cloud-top height/pressure value was calculated for the wind vectors. Each subsequent line is a single GOES-16 local cloud-motion vector, derived from 5x5-pixel band 2 (0.64 micron visible red band) image templates, which represent an area of ~2.5x2.5 km<sup>2</sup> at the subsatellite point.</p> <p> </p> <p><strong>MODIS–GOES-16 3D Cloud-Motion Vectors</strong></p> <p>Data in two netCDF files: <em>MOD.A2018129.1810-75_ABI_CONUS_band_02_goes16.nc</em> and <em>MYD.A2018129.2120-75_ABI_CONUS_band_02_goes16.nc</em>, which correspond to the MODIS Terra and MODIS Aqua overpasses, respectively. These joint MODIS–GOES-16 wind retrievals were derived using ~8x8 km<sup>2</sup> red band image templates sampled every 2 km. The data files are self-explanatory, but the variables "lat", "lon", "V_3D", and "H_3D" provide the latitude (degree), longitude (degree), the [east-west, north-south] wind components (m/s), and the geometric stereo height (m) for each wind retrieval.</p> <p> </p> <p><strong>ASCAT Ocean Surface Wind Vectors</strong></p> <p>Data in two netCDF files: <em>ascat_20180509_030000_metopa_59945_srv_o_063_ovw_new.nc</em> and <em>ascat_20180509_040000_metopb_29259_srv_o_063_ovw_new.nc</em>, which correspond to the MetOp-A and MetOp-B overpasses, respectively. These ASCAT ocean surface retrievals are stress-equivalent winds at 10 m height, given on a 6.25-km grid. The data files are self-explanatory, but the variables "lat", "lon", "wind_speed", and "wind_dir" provide the latitude (degree), longitude (degree), the wind speed (m/s), and the wind direction (oceanographic convention, degree north) for each wind retrieval. <em>Note that wind direction follows the oceanographic convention and refers to the direction towards which the wind blows (equivalent to meteorological wind direction minus 180<sup>o</sup>)!</em></p>
Output of CAM simulations performed for study "Impact of cloud physics on the Greenland Ice Sheet near-surface climate: a study with the Community Atmosphere Model"
<p>Output of CAM simulations performed for study "Impact of cloud physics on the Greenland Ice Sheet near-surface climate: a study with the Community Atmosphere Model" in JGR-Atmospheres (2020). </p> <p>Output are NetCDF files containing annual means (named 'yearmean', 2007-2013), or multi-annual monthly means ('ymonmean', 2007-2012) of various variables that are of interest and/or used for analysis in this study. The file name starts with the variable name. Fields are global, at a resolution of 0.9 x 1.25 degrees latitude/longitude.</p> <p>The test simulations are named (as discussed in the paper):</p> <p>cam4_clm5<br> cam5_clm5<br> cam6_noicenucl_clm5<br> cam6_noclubb_clm5<br> cam6_mg1_clm5<br> cam6</p>
Generated datasets for Yue et al. (2020, Earth and Space Science): "Combining In-situ and Satellite Observations to Understand the Vertical Structure of Tropical Anvil Cloud Microphysical Properties During the TC4 Experiment"
<p>This archive contains the data sets generated from the research conducted by Yue et al. (2020) titled "Combining In-situ and Satellite Observations to Understand the Vertical Structure of Tropical Anvil Cloud Microphysical Properties During the TC4 Experiment" published in Earth and Space Science. The method to generated the following data sets is described in Yue et al. (2020) and stored as Matlab .mat files.</p> <p>CombiningTC4_Satellite_eof_cov_mat.mat contains the correlation matrix shown in Figure 1a.</p> <p>TC4_processed.mat contains the correlation matrix shown in Figure 1b.</p> <p>RO_processed.mat contains the correlation matrix shown in Figure 2a.</p> <p>RVOD_processed.mat contains the correlation matrix shown in Figure 2b.</p> <p>ICE_processed.mat contains the correlation matrix shown in Figure 2c.</p> <p> </p> <p> </p>
2000 AU-scale cores identified by dendrogram in three massive clouds in the CMZ
<p>The full catalog of the 2000 AU-scale cores identified by dendrogram in three massive clouds in the CMZ. Paper will be submitted to AAS journals.</p>
Supplementary Data: Cloud-based multi-dimensional parallel dynamic programming algorithm for a hydropower station system
<p>The files in this record contain data for cloud-based multi-dimensional parallel dynamic programming algorithm for a hydropower station system considered for publication in Water Resources Research.</p> <p>The files consist of:</p> <ul> <li>cascade reservoir system data;</li> <li>Source code and results of the parallel dynamic programming algorithm program on the physical machine;</li> <li>Source code and results of the parallel dynamic programming algorithm program on the cloud virtual machine;</li> </ul>
Artifacts of Paper "Understanding and Discovering Software Configuration Dependencies in Cloud and Datacenter Systems"
<p>This package contains all the artifacts (i.e. codes & datasets) we use in our paper "Understanding and Discovering Software Configuration Dependencies in Cloud and Datacenter Systems" accepted to FSE 2020.</p>
Artifacts of Paper "Understanding and Discovering Software Configuration Dependencies in Cloud and Datacenter Systems"
<p>This package contains all the artifacts (i.e. codes & datasets) we use in our paper "Understanding and Discovering Software Configuration Dependencies in Cloud and Datacenter Systems" accepted to FSE 2020.</p>
Supporting Data for Sagoo et al. GRL: Observationally Constrained Cloud Phase Unmasks Orbitally Driven Climate Feedbacks
<p>This dataset includes CESM climatologies relevant for Sagoo et al.: “Observationally Constrained Cloud Phase Unmasks Orbitally Driven Climate Feedbacks” in Geophysical Research Letters. Data is included for high (HI) and low (LO) obliquity experiments with the default model (OOTB) and with supercooled liquid fraction constrained to satellite observations (SLF1 and SLF2), as described in the manuscript. </p>
A Satellite-Detected Terrestrial Gamma-ray Flash Produced by a Cloud-to-Ground Lightning Leader
<p>The data supports the manuscript entitled “A Satellite-Detected Terrestrial Gamma-ray Flash Produced by a Cloud-to-Ground Lightning Leader” that is submitted to GRL. These files can be opened by MATLAB. The data can be used freely for scientific purposes with appropriate citation.</p>
Convective cloud mergers and their impact on lightning activity in a severe squall-line system over the Beijing Metropolitan Region
<p>1. Radar_CR includes the 2D combined reflectivity data used to draw the figures (Fig. 2, 3) in this manuscript, and Radar_mref includes the 3D reflectivity data used to draw the figures (Fig. 5, 6, 7, 8) which has been translated into the Cartesian coordinates.</p> <p>2. VDRAS gives the 3D dynamic field data to draw the figure 10. </p> <p>3. Raw lightning data located by BLNet.</p> <p> </p>
Aggregated WRF-Chem outputs in "Evaluation of cloud and precipitation response to aerosols in WRF-Chem with satellite observations"
<p>This dataset contains the aggregated output of WRF-Chem simulations by Zhoukun Liu. </p> <p>The "MOR" in file name denotes the data from simulation which used Morrison scheme and "LIN" denotes the data from simulation which used Lin scheme.</p> <p>In "MOR_110km_core.nc" and "LIN_110km_core.nc", the variables are averaged over the cloudy pixels that have the highest 10% of cloud optical thickness in each 110km scene.</p> <p>In "MOR_110km_incloud.nc" and "LIN_110km_incloud.nc", the variables are averaged over all the cloudy pixels in each 110km scene.</p> <p>"MOR_110km_R.nc" and "LIN_110km_R.nc" contains the precipitaiton characteristics from models for each 110km scene. </p> <p> </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.