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2,170 results for “clouds”
Data of "Aerosol-cloud interactions near cloud base deteriorating the haze pollution in East China"
<p><span>The attached data is observations from ground to 1200 m a.g.l. using a tethered airship in Yangtze Rive Delta of China. The data is for analysis and figures in the study of "Aerosol-cloud interactions near cloud base deteriorating the haze pollution in East China".</span></p>
Data and code repository for the "Uncertainties in cloud-radiative heating within an idealized extratropical cyclone"
<p><strong>Author:</strong> Behrooz Keshtgar, behrooz.keshtgar@kit.edu</p> <p>This archive contains the post-processed data used to generate the figures and the code repository for the publication "Uncertainties in cloud-radiative heating within an idealized extratropical cyclone" by Behrooz Keshtgar, Aiko Voigt, Bernhard Mayer and Corinna Hoose.</p> <p>Description of the <strong>data</strong>:</p> <p>figure1.nc: precipitation rate, cloud cover, surface pressure, and cloud classes on day 4.5 of the ICON-NWP baroclinic life cycle simulation.</p> <p>figure2.nc: spatially and temporally averaged profiles of cloud water, ice mass content, and cloud fractions from ICON-LEM simulations.</p> <p>figure4.nc: spatially and temporally averaged cloud-radiative heating profiles from ICON-LEM simulations and offline radiation calculations for each LEM domain.</p> <p>figure5.nc: cross-section of radiative heating rates for 3D and 1D radiative transfer calculations in the shallow cumulus domain.</p> <p>figure6.nc: spatially averaged cloud-radiative heating profiles from 3D and 1D radiation calculations for each LEM domain.</p> <p>figure7.nc: cross-section of cloud-radiative heating calculated with the ice optics of Fu and Baum_ghm in the WCB ascent region.</p> <p>figure8.nc: spatially and temporally averaged profiles of cloud-radiative heating from 1D radiation calculations with different ice optics for each LEM domain.</p> <p>figure9.nc: spatially and temporally averaged profiles of cloud-radiative heating from 1D radiation calculations with LEM and NWP clouds for each LEM domain.</p> <p>figure10.nc: spatially and temporally averaged density and cloud-radiative heating profiles from different offline radiation calculations for each LEM domain.</p> <p>figure11.nc: profiles of the mean absolute difference of cloud-radiative heating from different offline radiation calculations at different resolutions for each LEM domain.</p> <p> </p> <p>The <strong>keshtgar-etal-2024-cyclone-crh-uncertainties-main.zip</strong> is the copy of the published git repository for the model run and analysis scripts. The repository contains</p> <p>- Scripts for the ICON model simulations</p> <p>- Scripts for the offline radiative transfer calculations with LibRadTran and the post-processing routine</p> <p>- Python scripts and Jupyter Notebooks for the analysis in the paper</p>
dataset for "basic setting", "+ binary semantic loss", "+ class weights", "+ height weights", "+ region weights", "+ elastic distortion and subsampling", "+ TreeMix" in paper Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning
<p>dataset for "basic setting", "+ binary semantic loss", "+ class weights", "+ height weights", "+ region weights", "+ elastic distortion and subsampling", "+ TreeMix" in paper Automated forest inventory: analysis of high-density airborne LiDAR point clouds with 3D deep learning</p>
FIG. 1 in Epiphyllous bryophyte diversity in lowland rain forest and lowland cloud forest of French Guiana
FIG. 1. ― Species accumulation curves and estimated total number of species (*) of epiphyllous bryophytes in the understory of lowland cloud forest (LCF) and lowland rain forest (LRF) at Nouragues, French Guiana.
Products and Models for "Nightside clouds and disequilibrium chemistry on the hot Jupiter WASP-43b"
<p>Hot Jupiters are among the best-studied exoplanets, but it is still poorly understood how their chemical composition and cloud properties vary with longitude. Theoretical models predict that clouds may condense on the nightside and that molecular abundances can be driven out of equilibrium by zonal winds. Here we report a phase-resolved emission spectrum of the hot Jupiter WASP-43b measured from 5-12 μm with JWST's Mid-Infrared Instrument (MIRI). The spectra reveal a large day-night temperature contrast (with average brightness temperatures of 1524±35 and 863±23 Kelvin, respectively) and evidence for water absorption at all orbital phases. Comparisons with three-dimensional atmospheric models show that both the phase curve shape and emission spectra strongly suggest the presence of nightside clouds which become optically thick to thermal emission at pressures greater than ~100 mbar. The dayside is consistent with a cloudless atmosphere above the mid-infrared photosphere. Contrary to expectations from equilibrium chemistry but consistent with disequilibrium kinetics models, methane is not detected on the nightside (2σ upper limit of 1-6 parts per million, depending on model assumptions).</p>
Data for: Bulk microphysics schemes may perform better with a unified cloud-rain category
<p>Bulk microphysics schemes continue to face challenges due in part to the necessary simplification of hydrometeor properties and processes that are inherent to any parameterization. In all operational bulk schemes, one such simplification is the division of liquid water into two subcategories (cloud and rain) when predicting the evolution of warm clouds. It was previously found that biases in collisional growth in a bulk scheme with these separate liquid water categories can be mitigated with a unified liquid water category in which cloud and rain are contained within the same category. In this study, we examine the effect of artificially separating the liquid water category on other microphysical processes and in more realistic settings. Both our idealized 1D and 3D results show that a unified category bulk scheme is fundamentally better at predicting the timing and intensity of rain from warm-phase cumulus clouds compared to a traditional (separate) category bulk scheme. This is because a unified category bulk scheme allows a bimodal distribution to exist within one traditional "rain" category, whereas separate category bulk schemes only have one mode per category. This advantage allows the unified bulk scheme to retain the information of the largest droplets even as they fall through a layer of small raindrops. A separate category bulk scheme fails to represent this bimodal feature in comparison.</p>
Data for iPyCLES v1.0: A New Isotope-Enabled Large-Eddy Simulator for Mixed-Phase Clouds
Open the record for dataset details and reuse information.
Fig. 23 in A new genus of Cicadellini (Hemiptera: Cicadellidae) from the Oaxacan Cloud Forest, with taxonomic notes on allied red-striped genera
Fig. 23. Gillonella ampulla Nielson & Godoy, 1995 photographed in situ. Photo taken by Andrey Peraza.
Figs 19–22 in A new genus of Cicadellini (Hemiptera: Cicadellidae) from the Oaxacan Cloud Forest, with taxonomic notes on allied red-striped genera
Figs 19–22. Gillonella ampulla Nielson & Godoy, 1995, ♂ from Braulio Carrillo (MNCR), male terminalia. 19. Aedeagus, lateral view. 20. Aedeagus, posterior view. 21. Asymmetrical processes adjoined to anal tube, dorsal view. 22. Connective, ventral view.
Fig. 10 in A new genus of Cicadellini (Hemiptera: Cicadellidae) from the Oaxacan Cloud Forest, with taxonomic notes on allied red-striped genera
Fig. 10. Habitat of Christopherus mictlantecuhtli gen. et sp. nov. Ancient Cloud Forest from Sierra Juárez, Oaxaca, Mexico. Images taken by Juvenal Aragón-Parada.
Figs 11–13 in A new genus of Cicadellini (Hemiptera: Cicadellidae) from the Oaxacan Cloud Forest, with taxonomic notes on allied red-striped genera
Figs 11–13. Gillonella ampulla Nielson & Godoy, 1995, holotype, ♂ (CASENT 19796), habitus. 11. Body, dorsal view. 12. Body, lateral view. 13. Face, anterior view.
Figs 14–18 in A new genus of Cicadellini (Hemiptera: Cicadellidae) from the Oaxacan Cloud Forest, with taxonomic notes on allied red-striped genera
Figs 14–18. Gillonella ampulla Nielson & Godoy, 1995, holotype, ♂ (CASENT 19796), terminalia. 14. Pygofer and subgenital plate, lateral view. 15. Anal tube, lateral view. 16. Style, ventral view. 17. Aedeagus, basal atrial process, and asymmetrical processes adjoined to anal tube, lateral view. 18. Labels. a = minute tooth on posterior margin; b = dorsum of pygofer sclerotized; c = joint of aedeagus with asymmetrical processes adjoined to anal tube; d = asymmetrical processes tips.
Figs 4–8 in A new genus of Cicadellini (Hemiptera: Cicadellidae) from the Oaxacan Cloud Forest, with taxonomic notes on allied red-striped genera
Figs 4–8. Christopherus mictlantecuhtli gen. et sp. nov., holotype, ♂ (INHS), terminalia. 4. Pygofer, lateral view. 5. Subgenital plates and styles, ventral view. 6. Styles, ventral view. 7. Aedeagus and basal atrial process, lateral view. 8. Aedeagus and paraphysis, posterior view.
Figs 1–3 in A new genus of Cicadellini (Hemiptera: Cicadellidae) from the Oaxacan Cloud Forest, with taxonomic notes on allied red-striped genera
Figs 1–3. Christopherus mictlantecuhtli gen. et sp. nov., holotype, ♂ (INHS), overall habitus. 1. Body, dorsal view. 2. Body, lateral view. 3. Face, anterior view.
The Data for Cloud Removal in Full-disk Solar Images Using Deep Learning
<p><em>Cloud Removal in Full-disk Solar Images Using Deep Learning. For more information, please refer to README.md.</em></p> <p><em>Please note that the data and model parameters zip is not available in github. Please download them in this file.</em></p> <p>If you need help, feel free to contact me at <a href="mailto:szh@kust.edu.cn" target="_blank" rel="noopener">szh@kust.edu.cn</a> or <a href="mailto:dp24163@163.com" target="_blank" rel="noopener">dp24163@163.com</a>.</p>
Output from ICON v2.6.2.2 cloud locking simulations: 3D radiative fluxes and additional atmospheric variables
<p>Simulation output from a cloud locking experiment carried out by A. Voigt with ICON version 2.6.2.2, originally for use in M. Huber’s PhD thesis (<a href="https://utheses.univie.ac.at/detail/63548/">https://utheses.univie.ac.at/detail/63548/</a>) and described therein. This subset was processed by E.K. Van de Koot for use in a study by McGraw et al (submitted 2024). Vertically-resolved radiative flux output is in the ‘phy_3d’ files, while ‘atm_2d’ and ‘atm_3d’ include additional atmospheric quantities, such as temperatures, specific humidity, and 2D radiative fluxes at the top-of-atmosphere and surface. Each file name is prefaced with the name of the relevant simulation (e.g. ‘amip_T1C1W1’), which follows nomenclature described in the Huber thesis.</p> <p>*Updated Dec 4, 2024 to fix a very small issue on vertical levels in the 'phy' files.</p>
Supplementary data for "Mechanism of surface solar irradiance variability under broken cloud cover"
<p>Open Data for manuscript to be submitted in ACP: "Mechanisms of surface solar irradiance variability under broken clouds". Refer to the README for details. Most (larger) files within the .zip archives are gzipped. Use `gunzip` to decompress.</p>
Spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing
<p>We produced a spatiotemporal dataset of dengue influencing factors in Brazil based on geospatial big data cloud computing from 2001-2024.</p> <p>GDP and building surface area are yearly data.</p> <p>PDSI is monthly data.</p>
Cloud Feedbacks and Organized Convection in Radiative-Convective Equilibrium
<p>Derived data and scripts used in Stauffer and Wing (2024).</p> <p>The standardized RCEMIP output, including time and domain mean profiles, is hosted by the German Climate Computing Center (DKRZ) and is publicly available at <a href="http://hdl.handle.net/21.14101/d4beee8e-6996-453e-bbd1-ff53b6874c0e">http://hdl.handle.net/21.14101/d4beee8e-6996-453e-bbd1-ff53b6874c0e</a>.</p> <p>Descriptions of each of the data files and the variables contained within them are discussed in a README file. The jupyter notebook contains the scripts used to plot the figures used in Stauffer and Wing (2024).</p> <p>Updated 01 April 2025 to include corrected Index of Organization values.</p>
Fractal Analysis of Clouds in DYAMOND Summer Simulations (revised)
<p><strong>Data accompanying "<em>The Fractal Nature of Clouds in Global Storm-Resolving Models</em>", by H. M. Christensen and O. Driver, submitted to Geophysical Research Letters.</strong></p> <p> </p> <p><strong>Summary</strong></p> <p>We compute the fractal dimension of clouds in the DYAMOND Summer simulations: https://www.esiwace.eu/services/dyamond/summer<br> This is compared to the dimension computed using the Himawari 8 satellite.</p> <p>The simulations span 1 August--10 September 2016. We use data between 25<sup>o</sup>S-25<sup>o</sup>N, 80-200<sup>o</sup>E. A binary cloud field is defined for the model simulations using outgoing long wave radiation using a given threshold. For Himawari observations we use the derived Cloud Top Temperature product, with a consistent threshold: see paper for details. Any pixel with outgoing long wave radiation or cloud top temperature below these values is defined as 'cloudy'.</p> <p> </p> <p><strong>Available model and satellite derived data</strong></p> <p>[model identifier]_clouds_230.csv</p> <p>Contains sets of Area-Perimeter data couplets for each selected timestamp in the DYAMOND simulation indicated by [model identifier], using the 230 K cloud top temperature threshold.</p> <p>[model identifier]_dims_threshold.csv</p> <p>Contains the fractal dimension measured for each selected timestamp in the DYAMOND simulation indicated by [model identifier], as a function of threshold. This is the Area-Perimeter fractal dimension, <span class="math-tex">\(P \propto A^{D/2}\)</span>. This can be obtained as the gradient of the regression line through the logarithm of the data in the 'clouds' files, multiplied by two. Data are provided for the following thresholds: 200, 210, 220, 230, 240, 250, 260 K.</p> <p>Since the satellite fields are only available during daylight hours, we provide and analyse the data at 0200, 0300, and 0400 UTC for both satellite and model data (or the closest available timestamp to these times for each model).</p> <p> </p> <p><strong>Acknowledgements</strong></p> <p>H.M.C. was funded by Natural Environment Research Council grant number NE/P018238/1.</p> <p>DYAMOND data management was provided by the German Climate Computing Center (DKRZ) and supported through the projects ESiWACE and ESiWACE2. The projects ESiWACE and ESiWACE2 have received funding from the European Union’s Horizon 2020 research and innovation programme under grant agreements No 675191 and 823988. This work used resources of the Deutsches Klimarechenzentrum (DKRZ) granted by its Scientific Steering Committee (WLA) under project IDs bk1040 and bb1153.</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.