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2,170 results for “clouds”
FIGURE 2 in Two new flea beetle genera: Alasia alpina gen. et sp. nov. from a Costa Rican cloud forest and Pseudostenophyma gen. nov. from Brazil (Coleoptera: Chrysomelidae: Alticinae)
FIGURE 2. Alasia alpina, male aedeagus: a) ventral view; b) lateral view; c) dorsal view.
FIGURE 3 in Two new flea beetle genera: Alasia alpina gen. et sp. nov. from a Costa Rican cloud forest and Pseudostenophyma gen. nov. from Brazil (Coleoptera: Chrysomelidae: Alticinae)
FIGURE 3. Alasia alpina: a) female spermatheca; b) female vaginal palpi; c) metafemoral spring.
FIGURE 2 in A new species of Pristimantis (Anura: Strabomantidae) from Andean cloud forests of northern Peru
FIGURE 2. Adult female holotype of Pristimantis bustamante (MHNC 8638, SVL = 21.2).
FIGURE 4 in A new species of Pristimantis (Anura: Strabomantidae) from Andean cloud forests of northern Peru
FIGURE 4. Plantar surfaces of the holotype of Pristimantis bustamante (MHNC 8638, SVL = 21.2).
FIGURE 1 in A new genus of Psychodinae (Diptera, Psychodidae) from phytotelmata in a Honduran cloud forest
FIGURE 1. Location of the collecting area, the Cusuco National Park.
FIGURE 1 in Microdrile Oligochaeta in bromeliad pools of a Honduran cloud forest
FIGURE 1. Map to show the location of the study site.
FIGURE 5 in Anticyphon gen. nov., a new genus of Scirtidae (Coleoptera: Scirtoidea) inhabiting high altitude Andean cloud forests and páramo formation
FIGURE 5. Anticyphon oyonensis sp. nov., abdomen. A) male, B) female.
Daily cloud-gap-filled Terra–Aqua MODIS NDSI dataset over High Mountain Asia (2000-2024)
<p>1. The daily cloud-gap-filled (CGF) MODIS normalized difference snow index (NDSI) dataset over High Mountain Asia (HMA) (2000-2024) is generated by combining of the cubic spline interpolation (CSI) method and the Spatio-Temporal Weighted (STW) method. This dataset is derived from daily 500 m MOD10A1 (Terra) and MYD10A1 (Aqua) products.</p> <p>2. The cloud persistence days (CPD) dataset is also provided. The CPD represents the number of consecutive days of cloud observed for a pixel from the last cloud-free observation to the next cloud-free observation. And the CPD is used to determine the combination of CSI and STW method, which is expressed as: when CPD < 8 d, the CSI method is used; when CPD ≥ 8 d, the STW is used.</p> <p>3. The CGF MODIS NDSI dataset is provided in ENVI standard format (.img) and the CPD dataset is provided in Geotiff format. And they are all provided in a geographic projection using the WGS84 coordinate system at a 0.005° (about 500 m) resolution. The NDSI value ranges from 0~100 and the CPD value ranges from 0~366. The fill value of both dataset is set to 255 (outside the track coverage of MODIS product).</p> <p>4. The CGF MODIS NDSI dataset contains 25 compressed packages (named after the normal year) of the daily CGF MODIS NDSI dataset over HMA (2000-2024), and after uncompressing the files are named as “YYYYDDD_HMA_MODIS_NDSI_0.5km.img”. The CPD dataset contains 25 compressed packages (named after the normal year) of the daily CPD dataset over HMA (2000-2024), and after uncompressing the files are named as “YYYYDDD_CPD.tif”. The YYYY represents the year and the DDD represents Julian day (001-365/366).</p> <p>5. The accuracy of this dataset has been well evaluated based on in-situ snow depth (SD) observations and high-resolution snow cover maps derived from Landsat images. The detailed information can be found in the paper (Deng, G., Tang, Z., Dong, C., Shao, D., & Wang, X. (2024). Development and Evaluation of a Cloud-Gap-Filled MODIS Normalized Difference Snow Index Product over High Mountain Asia. <em>Remote Sensing</em>, <em>16</em>(1), 192. https://doi.org/<a href="https://doi.org/10.3390/rs16010192">10.3390/rs16010192</a>).</p>
Confronting Large-Eddy Simulations with Stereo Camera Data by means of reconstructed hemispheric Cloud Size Distributions
<p>Dataset to produce the results of the publication: "<em>Confronting Large-Eddy Simulations with Stereo Camera Data by means of reconstructed hemispheric Cloud Size Distributions</em>". This dataset supports the findings presented in the publication and includes comprehensive resources for replicating its analysis and visualization. </p> <p> The dataset encompasses:</p> <ul> <li><strong>Dutch Atmospheric Large-Eddy Simulation (DALES) Data</strong><br> <ul> <li>Configuration files</li> <li>Selected simulation output data</li> </ul> </li> <li><strong>Image Data</strong> <ul> <li>Rendered stereo camera images from the DALES output</li> <li>Actual stereo camera images</li> <li>Cloud masks generated from these images</li> </ul> </li> <li><strong>Camera-Based Reconstructions</strong> <ul> <li>Reconstructed cloud fields from the rendered camera images</li> <li>Reconstructed cloud fields from the actual camera images</li> </ul> </li> <li><strong>Derived Cloud Metrics</strong> <ul> <li>Cloud base areas, cloud base heights, and cloud cover from the camera-based reconstructions</li> </ul> </li> <li><strong>Observational Data</strong> <ul> <li>Radiosondes, Ceilometer, and Cloudnet measurements</li> <li>Cloud cover from radiation measurements</li> <li>Mixed layer height from the Doppler lidar</li> </ul> </li> <li><strong>Reproduction Scripts</strong> <ul> <li>Scripts to reproduce the analysis and figures</li> </ul> </li> </ul> <p> </p> <p>This research was supported by the U.S. Department of Energy's Atmospheric System Research, an Office of Science Biological and Environmental Research program, under grant DE-SC0022126 and by the German Research Foundation (DFG) under project number 430226822 (https://gepris.dfg.de/gepris/projekt/430226822). The Gauss Centre for Supercomputing e.V. (https://www.gauss-centre.eu/) is acknowledged for providing computing time on the Gauss Centre for Supercomputing (GCS) supercomputer JUWELS at the Jülich Supercomputing Centre (JSC) under the projects RCONGM and VIRTUALLAB. JOYCE data were provided by the Institute for Geophysics and Meteorology of the University of Cologne. JOYCE is a collaborative research platform between University of Cologne and Forschungszentrum Jülich within the European research infrastructure ACTRIS. We acknowledge ACTRIS and the Finnish Meteorological Institute for providing Cloudnet data which is available for download from https://cloudnet.fmi.fi. We acknowledge ECMWF for providing IFS model data.</p>
Adiabatic Cloud Droplet Activation and Growth
Width of the droplet spectrum is an important parameter affecting cloud radiative properties as well as drizzle and rain development in warm ice-free clouds. We consider formation and growth of cloud droplets in an air parcel rising across the subcloud layer, reaching the cloud base, and continuing its rise carrying cloud droplets continuously growing by condensation of water vapor. We focus on the evolution of the spectral width. We show that the simplified droplet growth equation often applied in cloud simulations is not accurate enough to faithfully predict width of the droplet spectrum aloft, especially in polluted weak-updraft shallow clouds such as a continental stratocumulus.
Dataset for the chemistry of water-soluble fraction in cloud water and aerosols collected at Mt. Tianjing in 2018 and 2020
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Dataset for evolution characteristics of convective clouds over south China
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FIGURE 1 in Three new species of Epidendrum (Orchidaceae: Laeliinae) from the cloud forests of Chota, northern Peru
FIGURE 1. Distribution map of new species of Epidendrum. Prepared by James Chamaya.
Model used to calculate cloud albedo susceptibility in E3SM v2
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Data used to calculate cloud albedo susceptibility in E3SM v2
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Golden Gate Bridge - Point Cloud Portrait
Point cloud obtained from drone video Source: Objaverse 1.0 / Sketchfab
Cloud Regime Identification over the SGP
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Dataset for "Impact of geostationary interferometric infrared sounder observations from long- and middle-wave bands on weather forecasts with a locally cloud-resolving global model"
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JSS Data from Function-specific Scheduling Policies in Cloud-Edge, Multi-Region Serverless Systems
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The Impact of Aerosol on Cloud Water: A Heuristic Perspective
<p>Data used in for the study "The Impact of Aerosol on Cloud Water: A Heuristic Perspective."</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.