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2,170 results for “clouds”
Sceilig Mhichíl - Sampled Point Cloud (2.5D)
Sceilig Mhichíl is an island west of the Iveragh Peninsula in County Kerry, Ireland. The island is a UNESCO World Heritage Site and is best known for its well-preserved, medieval Gaelic monastery. The island is also known as the [filming site](https://youtu.be/g_0lk8BY0Vk) of Star Wars Episode VII "The Force Awakens". The topographical dataset of Sceilig Mhichíl is produced by the Geological Survey Ireland (GSI) and is licensed under a Creative Commons Attribution 4.0 International (CC BY 4.0) licence. The original raster data (2.5D, 0.1m resolution) can be downloaded from the [GSI Open Topo Viewer](https://dcenr.maps.arcgis.com/apps/webappviewer/index.html?id=b7c4b0e763964070ad69bf8c1572c9f5). Great information about Sceilig Mhichíl can be found in [this report](https://www.chg.gov.ie/app/uploads/2015/10/skellig-michael-world-heritage-site.pdf) from the Irish Dept. of Culture, Heritage and the Gaeltach. Source: Objaverse 1.0 / Sketchfab
Scottish Boatbuilding School: point cloud(II)
The Scottish Boatbuilding School at the Scottish Maritime Museum in Irvine was established in 2014 to provide education and qualification in both traditional and modern boat building, as well as vessel conservation and restoration. This point cloud generated from photogrammetry as a part of 'Scanning the Horizon' project, provides a unique glimpse into the school's boatbuilding workshop, which is not accessible for the public. This particular model shows the two skiffs as they are being constructed: a result of a very quick data capture! Listen to Martin, the Boatbuilding School Manager as he talks about a joint project with Garnock Connections and Rathbone Training Trust, which transform the lives of young people from local community through work based training in the form of apprenticeships and traineeships. To discover more about the boatbuilding workshop visit: https://skfb.ly/6MyMF Source: Objaverse 1.0 / Sketchfab
Oxford point cloud
Based on the idea of "Building Rome in a Day" : https://grail.cs.washington.edu/rome/ Point cloud generated using VisualSFM and an images database from : http://www.robots.ox.ac.uk/~vgg/data/oxbuildings/index.html You can probably find a better version of this at the above link. Source: Objaverse 1.0 / Sketchfab
Point cloud of of the church
PL: Skan kościoła św. Stanisława Biskupa Kościelna 4, 62-110 Damasławek, Polska. EN: Piont cloud of the St. Stanisław Church in Damasławek, Poland. Source: Objaverse 1.0 / Sketchfab
Guggenheim interior point cloud New York
Guggenheim interior point cloud. Such a great building very rough needs a lot of cleaning Source: Objaverse 1.0 / Sketchfab
Torre de Aragón, Spain (Point Cloud)
The so-called Torre de Aragón is a medieval fortress that is part of a much larger defensive complex located in Molina de Aragón (Guadalajara, Spain). The tower is located in the highest part of the area, dominating the valley generated by the Gallo river. Its strategic position meant that the tower was still considered useful in the 19th century, so it was rebuilt for military purposes. The Torre de Aragón is integrated into the defensive system of the medieval city of Molina de Aragón, one of the most extensive military complexes of the Middle Ages preserved in Spain today. The city of Molina de Aragón had a strategic character during the Middle Ages as it was located on the border of the Kingdoms of Castilla and Aragón. The Aragon tower was built on the remains of a previous Islamic fortress. It is pentagonal in shape to increase its resistance to impact from artillery, especially from catapults. Point cloud was made using 150 FARO Focus3D scan positions Source: Objaverse 1.0 / Sketchfab
MV Spartan (Point Cloud)
*MV Spartan* was built in 1942 and is one of only two surviving Scottish-built 'puffers': a type of steam-powered cargo vessel first built during the 1850s for use on the Clyde and Forth Canal. Now Spartan is on the Designated List of the National Historic Ships Committee and it was the first vessel in the museum's collection when it was established in 1983. *MV Spartan* is one of the most important objects in the Scottish Maritime Museum's collection. It sits just outside the Linthouse building, where most of our collection is housed. The vessel is not availabe to visit for the public, but we have a [virtual tour available online](https://www.scottishmaritimemuseum.org/virtualtours/) if you fancy a walk up the deck and inside! The model was created as a part of 'Scanning The Horizon' 3D digitisation project. We would like to thank [Historic Environment Scotland](historicenvironment.scot) and [Ulmus Media](https://ulmusmedia.co.uk/) for their help during this project. Source: Objaverse 1.0 / Sketchfab
POINT CLOUD Mural by Fintan Magee
Mural Arty by Fintan Magee. Located at Teggs Lane, Chippendale, Sydney More info https://artout.live/chippendale/teggs-lane-art/ Source: Objaverse 1.0 / Sketchfab
Svetitskhoveli area point cloud
Svetitskhoveli catedral point cloud. Mavic air 2 footage processed with agisoft metashape. Mtskheta, Georgia. Source: Objaverse 1.0 / Sketchfab
Pattern and driver of the compositional variations in a tropical cloud forest: Comparing vascular epiphytes with terrestrial woody plants
<p>β-diversity patterns (the compositional variations across sites) and their drivers are the major concerns of biodiversity research and conservation practices, whereas such information remains scarce for vascular epiphytes, especially in tropical forest communities. This study aimed to reveal the pattern and driving process of the compositional variations of vascular epiphytes in a tropical cloud forest on Hainan island, southern China, and their differences from those of terrestrial woody plants. To this end, we quantified their between-habitat compositional variations and distinguished the underlying components of β-diversity (nestedness and turnover). We then examined the relative roles of niche-based and neutral processes in driving the compositional variations by using a null model approach. Our results showed that the between-habitat compositional variations were significant for both plant assemblages and stronger in vascular epiphytes than in terrestrial woody plants. The turnover component of β-diversity was significantly stronger in terrestrial woody plants, accounting for 73.16%–80.08% of the variations. By contrast, the nestedness component was significantly stronger in vascular epiphytes and characterized 46.82%–67.5% of the variations. Besides, the compositional variations of both plant assemblages, especially terrestrial woody plants, were generally poorly fitted by the simulated niche-based scenarios but well fitted by the simulated neutral scenarios. Overall, the compositional variations of both plant assemblages were significant and mainly due to dispersal limitation, albeit to varying degrees. Hence, further studies of these plant assemblages at local scales should not be ideologically limited to the niche-based framework. Moreover, the stronger nestedness observed in vascular epiphytes suggests the greater importance of prioritizing conservation efforts in the species-rich habitats for these plants.</p>
Effect of Long-Range Transported Fire Aerosols on Cloud Condensation Nuclei Concentrations and Cloud Properties at High Latitudes
<p>The tab-delimited files contain Aerosol chemical composition data, size distribution data measured by AMS, ACSM, MAAP, and SMPS, and DMPS at the SMEAR IV station (Puijo) and the Zepplein observatory (Zep) . Please read the file named 'Information.txt' for more details about the files.</p>
Dataset for paper "Process Modeling of Aerosol-cloud Interaction in Summertime Precipitating Shallow Cumulus over the Western North Atlantic"
<p>This dataset contains LES model input and post-processed model output used in paper "Process Modeling of Aerosol-cloud Interaction in Summertime Precipitating Shallow Cumulus over the Western North Atlantic".</p>
Converging findings of climate models and satellite observations on the positive impact of European forests on cloud cover
<p>Overview:<br>This repository hosts a comprehensive dataset resulting from a Space for Time (S4T) analysis (<em>Duveiller et al. 2018</em>). The dataset spans monthly data from 2004 to 2014, providing detailed insights into cloud cover dynamics and land cover characteristics. Leveraging observations from the Cloud CCI MODIS-Aqua dataset (<em>Stengel et al. 2017</em>) and RegCM5 (<em>Giorgi et al. 2023</em>) model outputs at 0.05 degrees resolution, it offers valuable resources for researchers studying atmospheric and terrestrial interactions.</p> <p>Contents:</p> <p>s4t_ESACCI.zip:<br>Output of the space-for-time algorithm applied to the Global MODIS-Aqua cloud cover data for low, medium, and high clouds.<br>s4t_RegCM5.zip:<br>Output of the space for time algorithm applied to the European RegCM5 cloud data for low, medium, and high clouds.<br>Variables:</p> <p>Cloud Area Fractions:<br>Includes low (cll), medium (clm), and high (clh) cloud area fractions, expressed as percentages.<br>Cloud layers are categorized based on cloud top pressure (CTP), following the convention of the International Satellite Cloud Climatology Project.</p>
94 GHz cloud radar simulation data using NICAM and Joint simulator for evaluation of ground-based radar and application to the EarthCARE satellite
<p><strong>Overview</strong></p> <p>These data are snapshots of simulated radar reflectivity and Doppler velocity from the 94 GHz Cloud Profiling Radar (CPR). The simulations were conducted using the Joint-Simulator for Satellite Sensors (Joint-Simulator; Hashino et al., 2013; Roh et al., 2020), and the input data was from the Nonhydorstatic Icosahedral Atmospheric Model (NICAM; Satoh et al., 2014). To focus on the region of interest with high resolution, NICAM transformed the grid (the stretched NICAM; Tomita 2008a) using a G-Level 10 (GL10) horizontal resolution with a stretch-factor of 100 (the ratio between the maximum and minimum grid intervals), where the minimum grid interval is approximately 800 m. We simulated two cases of rain events in September 2019. The first case (case 1) is the tropical cyclone (TC) Faxai. The second is a weak frontal system (case 2). In case 1 the integration and analysis time was from 00 UTC on 8 September to 00 UTC on 9 September 2019. In case 2 the integration and analysis time was from 00 UTC on 20 September to 00 UTC on 21 September 2019. We evaluated the data using the gournd observation and introduced a methodology for using the CPR data for model evaluations. We simulated Doppler velocity of the EarthCARE CPR. </p> <p> </p> <p><strong>Directory structure and file format</strong></p> <p>There are two files.</p> <p>For Ground_CPR, there are simuation data based on the ground.</p> <p>For Satellite_CPR, there are simulation data bsed on instrument setting of EarthCARE CPR.</p> <p>(Note the order of the array of Satelltie_CPR is different from Ground_CPR.)</p> <p>The files are in the NetCDF4 format.</p> <p>The files have the following name format.</p> <p>AAA_XXX_TIME_EASE.nc</p> <p>AAA: Ecare (CPR simulation withot random errors), Mode(CPR simulation with randome errors based on window observaion mode)</p> <p>XXX: NICAM Single Moment sheme 6 category (NSW6) and NICAM Dobule Moment scheme 6 category(NDW6)</p> <p>TIME: The simulation time</p> <p>EASE: EarthCARE Active SEnsor simulator (EASE)</p> <p> </p> <p> </p> <p> </p> <p>These data are only one snapshot data and limited variables becuase of file size issue.</p> <p>If you have any questions or want additional data, please contact the email below</p> <p>Contacts:</p> <p>Woosub Roh (ws-roh@aori.u-tokyo.ac.jp)</p>
Data to the Supporting Information to "Radiative Heating of High-Level Clouds and its Impacts on Climate"
<p><strong>Author:</strong> Kerstin Haslehner kerstin.haslehner@univie.ac.at</p> <p>This archive includes the ICON-ESM output files used to study the radiative heating of high-level clouds.</p> <p>This dataset is related to the manuscript "Radiative Heating of High-Level Clouds and its Impacts on Climate" by Kerstin Haslehner, Blaž Gasparini and Aiko Voigt, which will be submitted to the Journal of Geophysical Research: Atmospheres.</p> <p>The output data was originally created as part of a Master's thesis by Kerstin Haslehner at the Department of Meteorology and Geophysics at University Vienna. This thesis is available here: https://utheses.univie.ac.at/detail/67015/#</p> <p> </p> <p>Descriptions of names:</p> <p>"clouds off": radiative heating of all clouds turned off</p> <p>"ice-off": radiative heating of when clouds at temperatures colder than -35°C are turned off</p> <p>"ice-off-warmbase": radiative heating of high-level clouds turned off</p> <p>"ice-on": radiative heating of all clouds active, reference run</p> <p>"diagice-cirrus": contains output variables that diagnose the radiative heating of clouds at temperatures colder than -35°C</p> <p>"hl": height levels</p>
Data to "Radiative Heating of High-Level Clouds and its Impacts on Climate"
<p><strong>Author:</strong> Kerstin Haslehner kerstin.haslehner@univie.ac.at</p> <p>This archive includes the ICON-ESM output files used to study the radiative heating of high-level clouds.</p> <p>This dataset is related to the manuscript "Radiative Heating of High-Level Clouds and its Impacts on Climate" by Kerstin Haslehner, Blaž Gasparini and Aiko Voigt, which will be submitted to the Journal of Geophysical Research: Atmospheres.</p> <p>The output data was originally created as part of a Master's thesis by Kerstin Haslehner at the Department of Meteorology and Geophysics at University Vienna. This thesis is available here: https://utheses.univie.ac.at/detail/67015/#</p> <p> </p> <p>Descriptions of names:</p> <p>"clouds off": radiative heating of all clouds turned off</p> <p>"ice-off-warmbase": radiative heating of high-level clouds turned off</p> <p>"ice-on": radiative heating of all clouds active, reference run</p> <p>"diagice-warmbase": contains output variables that diagnose the radiative heating of high-level clouds</p> <p>"diagice-cirrus": contains output variables that diagnose the radiative heating of clouds at temperatures colder than -35°C</p> <p>"ua700": zonal wind at 700hPa</p> <p>"mastrfu": mass stream function, calculated with CDO</p> <p>"hl": height levels</p> <p>"wap500hPa": omega at 500hPa</p>
Applying innovative cloud computing technology for the effective management of Groundwater resources to promote SUStainable food security within the Sokoto Basin, Nigeria (AGSUS)
Open the record for dataset details and reuse information.
Data from: Supersaturation and critical size of cloud condensation nuclei in marine stratus clouds
<p>Observations of marine stratus clouds in clean air off the Californian coast reveal a functional relationship between the number of cloud condensation nuclei (CCN) and supersaturation. Satellite-derived liquid droplet density estimates the number density of CCN. Combining the estimated supersaturation using Kohler theory, global maps of supersaturation and the critical activation size of CCN are estimated. Here, we show that high supersaturation >0.5% persists over the oceans with a critical CCN size of 25-30 nm, which is smaller than the conventional wisdom of 60 nm. Independent support for such high supersaturation in the marine cloud is obtained from CCN measurements provided by the "Atmospheric Tomography Mission". Higher supersaturation implies smaller activation size for CCN making cloud formation more sensitive to changes in aerosol nucleation.</p>
Data for: A protocol for model intercomparison of impacts of Marine Cloud Brightening Climate Intervention
<p>Replication data for "A protocol for model intercomparison of impacts of Marine Cloud Brightening Climate Intervention" submitted to Geophysical Model Development.</p>
Observational dataset for "Using Satellite and ARM Observations to Evaluate Cold Air Outbreak Cloud Transitions in E3SM Global Storm-Resolving Simulations"
<p>This observational dataset include the DOE ARM ground based observations and satellite for the paper titled “Using Satellite and ARM Observations to Evaluate Cold Air Outbreak Cloud Transitions in E3SM Global Storm-Resolving Simulations” on GRL.</p> <p>For the original data source, all ARM observational data sets used in this study are publicly available from the ARM data archive site (https://adc.arm.gov/discovery/#/results/iopShortName::amf2019comble/datastream::anxarmbeatmM1.c1/datastream::anxarmbecldradM1.c1/datastream::anxarsclkazr1kolliasM1.c0). MODIS MOD06 L2 cloud product are publicly available from (https://ladsweb.modaps.eosdis.nasa.gov/missions-and-measurements/products/MOD06 L2, DOI:10.5067/MODIS/MOD06 L2.061).</p> <p>CloudSat products can be ordered from the CloudSat Data Processing center (https://www.cloudsat.cira.colostate.edu/order/). To download CloudSat data, a new user must first create an account by filling out the signup form (https://www.cloudsat.cira.colostate.edu/accounts/signup/).</p>
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