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2,170 results for “clouds”
Model output from "A model intercomparison of CCN-limited tenuous clouds in the high Arctic"
<p>Model output from "A model intercomparison of CCN-limited tenuous clouds in the high Arctic", accepted for publication in Atmospheric Chemistry and Physics, 2018, same authors. The intercomparison includes output from three large-eddy simulation models (UCLALES-SALSA, MIMICA, and COSMO-LES) and three numerical weather prediction models (COSMO-NWP, WRF, and UM-CASIM) for a case study of high-Arctic tenuous cloud based on observations from the 2008 Arctic Summer Cloud Ocean Study (ASCOS) campaign. See publication for details. The discussion preprint for peer review can be found at https://doi.org/10.5194/acp-2017-1128.</p>
Cloud droplet growth due to supersaturation fluctuations in stratiform clouds
<p>Datasets with numerical setups and direct numerical simulation (DNS) data for "Cloud-droplet growth due to supersaturation fluctuations in stratiform clouds", <em>Atmosph. Chem. Phys.</em>, DOI: https://doi.org/10.5194/acp-2018-644</p>
SMA/VLA data toward five CMZ clouds and Sgr D
<p>SMA/VLA data toward five CMZ clouds (20 km/s, 50 km/s, G0.253+0.016, Sgr B1-off, Sgr C) and Sgr D. Details of these images can be found in the published paper Lu et al. 2019, Astrophysical Journal, vol. 872, article id. 171 (https://ui.adsabs.harvard.edu/abs/2019ApJ...872..171L/abstract).</p> <p>The files are named as follows:</p> <p>[Target]_[Telescope]_[Lines(or continnuum)]_[Imagecontent].fits</p> <p>The target is one of 20kms, 50kms, G0253, SgrB1off, SgrC, and SgrD.</p> <p>The telescope is either SMA (for the Submillimeter Array) or the VLA (for the Karl G. Jansky Very Large Array).</p> <p>The lines/continuum is continuum, H2Omaser (for VLA data), or CH3OH/SiO (for SMA data).</p> <p>The image content is either IM (images without primary beam correction) or PB (primary beams).</p> <p>To apply the primary beam correction and get the flux-corrected images, divide IM by PB pixel-by-pixel (for related CASA tasks, see https://casa.nrao.edu/docs/taskref/impbcor-task.html)</p> <p>For example, 20kms_SMA_CH3OH_IM.fits</p> <p>-- the target is the 20 km/s cloud;</p> <p>-- the telescope is the Submillimeter Array;</p> <p>-- the observed line is the CH3OH 4(2,2)-3(1,2) transition;</p> <p>-- the file is the image without primary beam correction.</p>
Experimental CCN properties of 6 pollenkitts and two mixtures with ammonium sulfate reported in the study "Cloud condensation nuclei activity of six pollenkitts and the influence of their surface activity" by Prisle et al. (2019)
<p>Critical dry particle size Dp<sub>50</sub> measured for supersaturations 0.1–1.4% with a DMT CCN Counter (CCN-100). Size and composition resolved, and size-averaged hygroscopicity values calculated using the method presented by Rose et al. (2010), Cloud condensation nuclei in polluted air and biomass burning smoke near the mega-city Guangzhou, China - Part 1: Size-resolved measurements and implications for the modeling of aerosol particle hygroscopicity and CCN activity, <em>Atmospheric Chemistry and Physics</em>, <em>10</em>, 3365–3383.</p>
Experimental surface tensions of aqueous poplar and ragweed pollenkitts reported in the study "Cloud condensation nuclei activity of six pollenkitts and the influence of their surface activity" by Prisle et al. (2019)
<p>Surface tensions measured with a ramé-hart goniometer (Model 250) for aqueous solutions of varying concentrations comprising poplar and ragweed pollenkitts and their mixtures with ammonium sulfate</p>
Critical droplet properties for particles containing 6 pollenkitts evaluated with 4 Köhler models for the study "Cloud condensation nuclei activity of six pollenkitts and the influence of their surface activity" by Prisle et al. (2019)
<p>Critical droplet supersaturation and surface tension, calculated with 3 thermodynamic Köhler models presented in Prisle et al., <em>Surfactants in cloud droplet activation: mixed organic-inorganic particles</em>, Atmospheric Chemistry and Physics, 10, 5663-5683, doi:10.5194/acp-10-5663-2010, and with the model presented in Prisle et al., <em>A simple representation of surface active organic aerosol in cloud droplet formation</em>, Atmospheric Chemistry and Physics, 11, 4073-4083, doi: 10.5194/acp-11-4073-2011, for particles of varying size comprising 6 pollenkitts and mixtures with ammonium sulfate.</p>
Canary Islands La Palma: cloud cover map (2015)
<p>A multi-temporal cloud cover map related to 10 Landsat 8 images in "La Palma" PA from August to December 2015.</p> <p>The 10 layers were related to the following dates: August 9<sup>th</sup>, August 25<sup>th</sup>, September 10<sup>th</sup>, September 26<sup>th</sup>, October 12<sup>nd</sup>, October 28<sup>th</sup>, November 13rd, November 29<sup>th</sup>, December 15<sup>th</sup>, December 31<sup>st</sup>.</p> <p>The maps was produced at 30 meters spatial resolution and projected in WGS84/UTM28N.</p> <p>The map has binary values where value 1 indicates cloud cover pixels whereas value 0 indicates No cloud cover pixels.</p>
Canary Islands La Palma: cloud cover map (2016)
<p>A multi-temporal cloud cover map related to 5 Sentinel-2A images in "La Palma" PA from December 2015to April 2016.</p> <p>The 5 layers representing the cloud cover (as OR among different temporal and spatial acquisitions in each month) were related to the following dates: December 2015, January 2016, February 2016, March 2016 and April 2016 .</p> <p>The maps was produced at 60 meters spatial resolution and projected in WGS84/UTM28N.</p> <p>The map has binary values where value 1 indicates cloud cover pixels whereas value 0 indicates No cloud cover pixels.</p>
Sonora 2018: Cloud-free, solar composition, solar C/O substellar evolution models
<p>Tables of the evolution and photometry of brown dwarfs and extrasolar giant planets based on the model described in Marley et al. (2019, in prep.). This publication provides a detailed description of the physics included in the model. The main differences with the previously published tables by Saumon & Marley (2008) are</p> <p>• updated "Sonora 2018" atmosphere models used as the surface boundary conditions, primarily involving improvements in the opacities of H2, CH4 and alkali resonance lines. The new atmosphere models are described in Marley et al. (2019, in prep.).</p> <p>• including metals in the interior equation of state as an additional amount of helium. Currently, the evolution tracks are for solar C/O ratio (defined relative to solar, so C/O=1 here) with cloudless atmospheres.</p> <p>V2.0 adds more photometry tables and evolution for [M/H]=-0.5 and +0.5 to the previous 0.0.</p> <p><a href="/api/files/e3bc80f3-7127-421c-af47-ecfe7e44c40c/tables_m+0.0_2018.tar.gz?versionId=54186e1a-1688-40c5-8405-f289953f134b">tables_m+0.0_2018.tar.gz</a> is now deprecated as V1.0</p> <p><a href="https://zenodo.org/api/files/e3bc80f3-7127-421c-af47-ecfe7e44c40c/Sonora_Bobcat_Tables.tar.gz">Sonora_Bobcat_Tables.tar.gz</a> is current V2.0 and is a superset of V1.0</p> <p>Please cite Marley et al. (2019, in prep.) if you use these in a publication.</p>
Traces in seconds for cloud computing research
<p>Traces in seconds for research in allocation in cloud computing. The code to generate this traces can be obtained from <a href="https://github.com/asi-uniovi/traces-seconds">https://github.com/asi-uniovi/traces-seconds</a>. Additional explanation of these traces is in the paper <em>Influence of the trace resolution and length in the cost optimization process in cloud computing</em>, published in the 2019 International Symposium on Performance Evaluation of Computer and Telecommunication Systems (SPECTS 2019). Please, cite this paper if you use this traces in your research.</p>
Cloud-Repro: Reproducible Workflow on a Public Cloud for Computational Fluid Dynamics
<p>In a new effort to make our research transparent and reproducible by others, we developed a workflow to run computational studies on a public cloud. It uses Docker containers to create an image of the application software stack. We also adopt several tools that facilitate creating and managing virtual machines on compute nodes and submitting jobs to these nodes. The configuration files for these tools are part of an expanded "reproducibility package" that includes workflow definitions for cloud computing, in addition to input files and instructions. This facilitates re-creating the cloud environment to re-run the computations under the same conditions.</p> <p>The present Zenodo dataset contains all secondary data required to reproduce the figures of the manuscript ("Reproducible Workflow on a Public Cloud for Computational Fluid Dynamics") without running the CFD simulations again.</p>
Data for the publication "Impact of Isolated Atmospheric Aging processes on the Cloud Condensation Nucleiactivation of Soot Particles"
<p>The repository contains the data for the paper:</p> <p>Friebel, F., Lobo, P., Neubauer, D., Lohmann, U., Drossaart van Dusseldorp, S., Mühlhofer, E., and Mensah, A. A.: Impact of isolated atmospheric aging processes on the cloud condensation nuclei activation of soot particles, Atmos. Chem. Phys., 19, 15545–15567, https://doi.org/10.5194/acp-19-15545-2019, 2019.</p> <p>Note that the scripts to plot this data are to be found in the accompanying package (http://dx.doi.org/10.5281/zenodo.3452036)</p>
Non-equilibrium fractionation during ice cloud formation in iCAM5: evaluating the common parameterization of supersaturation as a linear function of temperature
<p>This archive includes data and python scripts to create the figures in</p> <p>Duetsch, M., Blossey, P. N., Steig, E. J., and Nusbaumer, J. M. (2019). Non-equilibrium fractionation during ice cloud formation in iCAM5: evaluating the common parameterization of supersaturation as a linear function of temperature. Submitted to J. Adv. Model. Earth Sy.</p> <p><strong>Model</strong></p> <p>Model output data are saved in model.tar.gz</p> <p>For all simulations, monthly averages of the following variables are saved in h0 files (used for Figures 3, 6, 8, 9):<br> T: temperature (Si_real simulations only)<br> PS: surface pressure (Si_real simulations only)<br> U: zonal wind (Si_real simulations only)<br> V: meridional wind (Si_real simulations only)<br> LANDFRAC: fraction of surface area covered by land<br> PRECT_H2O: total precipitation rate for H2O<br> PRECT_HDO: total precipitation rate for HDO<br> PRECT_H218O: total precipitation rate for H218O<br> H2OV: H2O mixing ratio for vapor<br> HDOV: HDO mixing ratio for vapor<br> H218OV: H218O mixing ratio for vapor<br> H2OI: H2O mixing ratio for cloud ice<br> HDOI: HDO mixing ratio for cloud ice<br> H218OI: H218O mixing ratio for cloud ice<br> H2OL: H2O mixing ratio for cloud liquid<br> HDOL: HDO mixing ratio for cloud liquid<br> H218OL: H218O mixing ratio for cloud liquid</p> <p>For the control and Si_real microphysical sensitivity simulations, 6-hourly averages of the following variables are saved in h1 files (used for Figures 4, 5, 7):<br> PRECL_H2O: Large-scale precipitation rate for H2O (Si_real simulations only)<br> PRECL_HDO: Large-scale precipitation rate for HDO<br> PRECL_H218O: Large-scale precipitation rate for H218O<br> PRECL_SAT: Large-scale precipitation rate for Si tracer (Si_real simulations only)<br> PRECL_TMP: Large-scale precipitation rate for T tracer (Si_real simulations only)<br> PRECL_RVD: Large-scale precipitation rate for R^D tracer<br> PRECL_RVO: Large-scale precipitation rate for R^18O tracer</p> <p>To limit the size of the data set, only the first 5 years of the simulations are saved.</p> <p><strong>Measurements</strong></p> <p>Ice core measurements for present-day climate and last glacial maximum are saved in icecores_PD.txt and icecores_LGM.txt, respectively.</p> <p>LGM values are averaged from 19000 BCE to 16000 BCE, PD values are averaged from 1000 BCE to 2000 CE.</p> <p>Antarctic surface snow measurements from Masson-Delmotte et al. (2008) are available at https://doi.org/10.1594/PANGAEA.681697</p> <p><strong>Python scripts</strong></p> <p>Scripts run with python 3.6</p> <p>Required packages:<br> - numpy<br> - matplotlib<br> - netCDF4<br> - datetime<br> - Basemap<br> - copy<br> - intergrid<br> - cmocean</p>
APPUiO PaaS container cloud tracefile
<p>Anonymized data set from APPUiO Public Container Cloud. For more info, please refer to README.md</p>
Fig. 5 in Abundance and microhabitat use of the Endangered toad Rhinella yanachaga (Anura: Bufonidae) in the cloud forest of Yanachaga Chemillén National Park, Peru
Fig. 5. Comparisons of the sizes of Rhinella yanachaga individuals. (A) Differences in size of females in dry and wet seasons. (B) Differences in size of males in dry and wet seasons. (C) Differences in size between males and females. (D) Differences in the size of individuals with respect to microhabitats. The different letters indicate significant differences at p ≤ 0.05 according to the Tukey test after GLMM. Error bars represent standard errors.
Fig. 4 in Abundance and microhabitat use of the Endangered toad Rhinella yanachaga (Anura: Bufonidae) in the cloud forest of Yanachaga Chemillén National Park, Peru
Fig. 4. Correlations between the elevation and size of Rhinella yanachaga according to sex. The model presents correlation factors of r2 = 0.34; p = 0.01 for females and r2 = 0.003; p = 0.41 for males. The colors indicate sex (blue for males and red for females) and the shaded areas indicate the 95% confidence limits.
Fig. 2 in Abundance and microhabitat use of the Endangered toad Rhinella yanachaga (Anura: Bufonidae) in the cloud forest of Yanachaga Chemillén National Park, Peru
Fig. 2. Number of Rhinella yanachaga individuals per transect, T1 = 2,800–2,700 m, T2 = 2,700–2,600 m, T3 = 2,600–2,500 m, and T4 = 2,500–2,400 m, according to the elevation gradient for both sexes, in (A) dry season and (B) wet season. (C) Correlation between the abundance of Rhinella yanachaga and elevation for both sexes and seasons. The grey band indicates the 95% confidence limits.
Fig. 3 in Abundance and microhabitat use of the Endangered toad Rhinella yanachaga (Anura: Bufonidae) in the cloud forest of Yanachaga Chemillén National Park, Peru
Fig. 3. Numbers of individuals according to sex and length (SVL) ranges of Rhinella yanachaga in four transects, T1 = 2,800–2,700 m, T2 = 2,700–2,600 m, T3 = 2,600–2,500 m, and T4 = 2,500–2,400 m, according to the elevation gradient in the wet and dry seasons.
Fig. 1 in Abundance and microhabitat use of the Endangered toad Rhinella yanachaga (Anura: Bufonidae) in the cloud forest of Yanachaga Chemillén National Park, Peru
Fig. 1. (A) Map of the study area. The white circle indicates the location of the study site (San Alberto) within Yanachaga-Chemillén National Park, Oxapampa, Pasco; (B) lateral view, (C) ventral view, and (D) dorsal view of Rhinella yanachaga in life. Map by Vladimir Camel (A), photos by Shirley Huamán-Trucios (B–D).
Fig. 2 in Insights Into The Spatial And Temporal Ecology Of The Sunda Clouded Leopard Neofelis Diardi
Fig. 2. Frequency distribution of hourly activity for Sunda clouded leopard based on 135 independent photo-captures.
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International Brain Laboratory public data
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OpenNeuro
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