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108 results for “cloud, simulations”
Leaf and wood classification framework for terrestrial LiDAR point clouds: Simulated data validation dataset
<p>Set of 200 3D point clouds used in the validation of "Leaf and wood classification framework for terrestrial LiDAR point clouds". This dataset is a collection of point clouds simulated by a Monte-Carlo ray tracing (librat) using four 3D tree models from the fourth phase RAMI exercise (Widlowski et al, 2015).</p>
Anisotropic Radiance/Reflectance Simulations for Clouds
<p>This data file contains anisotropic (sensor viewing azimuth angles range from 0 to 360° of every 5°) radiance and reflectance calculations at the top of the atmosphere for clouds.</p> <p> </p> <p><strong>Radiative transfer parameters:</strong></p> <p>1) sensor viewing zenith angle of 45°;</p> <p>2) solar zenith angle of 60°;</p> <p>3) solar azimuth angle of 0 (north);</p> <p>4) surface albedo (Lambertian): 0.03;</p> <p>5) cloud optical thickness: 10;</p> <p>6) cloud effective radius: 12 micron;</p> <p>7) cloud vertical location: 0.5 km to 1 km.</p> <p> </p> <p><strong>Data description:</strong></p> <p>rad_555: 1D array (size of 73), radiance at 555 nm, units in W/m²/nm/sr;</p> <p>ref_555: 1D array (size of 73), reflectance at 555 nm;</p> <p>rad_vis: 1D array (size of 73), broadband (350 - 700 nm) radiance, units in W/m²/nm/sr;</p> <p>ref_vis: 1D array (size of 73), broadband (350 - 700 nm) reflectance;</p> <p>vaa: 1D array (size of 73), sensor viewing azimuth angles, units in degree;</p> <p>vza0: single value, sensor viewing zenith angle, units in degree;</p> <p>saa0: single value, solar azimuth angle, units in degree;</p> <p>sza0: single value, solar zenith angle, units in degree.</p> <p> </p> <p><strong>Notes:</strong></p> <p>This data file is generated by the code included in EaR<strong>³</strong>T (<a href="https://github.com/hong-chen/er3t">https://github.com/hong-chen/er3t</a>, function <example_rad_02_libera_adm> in <examples/00_er3t_lrt.py>). The listed parameters such as solar and sensor viewing geometries, cloud optical properties, surface albedo etc. can be customized.</p>
CESM2 simulation output used in the study "On the links between ice nucleation, cloud phase, and climate sensitivity in CESM2"
<p>Provided is all CESM2 model output used to generate figures in the study, for which a preprint is at 'https://doi.org/10.22541/essoar.167214452.25853014/v1'. File names indicate the experiment names used in the study. For each model experiment, there is one file containing variables in a present-day (PD) simulation, plus a second file containing cloud feedbacks calculated by the Zelinka et al 2012 kernel method (comparing PD to PD with 4K warming uniformly added to sea surface temperatures).</p>
Simulations of stratocumulus cloud fields using RAMS model
<p>The simulations of the stratocumulus are performed with a domain size of km ( bin points) for 3 hours. The horizontal resolution is fixed at 100 m, and the vertical bin spacing is 50 m. The initial state of the simulations (DYCOMS-II case) is based on vertical profiles of potential temperature, moisture, and horizontal winds, that were adapted from Stevens et al. (2003). From these initial fields, 4 additional simulations are carried out by slightly modifying the temperature profiles to check their effects on the stratocumulus field and notably on entrainment rates. In addition, one extra simulation is realized by modifying the humidity profile. In summary, these 6 simulations are as follows: case 1) ‘Control’ is the basic simulation with the unmodified fields; case 2) ‘Control + layer 150 m’ is as ‘Control’ but the temperature inversion is 150 m above that of ’Control’ (less brutal than ‘Control’), expecting more entrainment; case 3) ‘Control + layer 300 m’ is as ‘Control’ but the temperature inversion is 300 m above that of ’Control’; case 4) ‘Control - 4K’ is as ‘Control’ but with a smaller temperature inversion, expecting more mixing; case 5) ‘Control + 4K’ is as ‘Control’ but with a stronger temperature inversion; and case 6) ‘Extra’ is as ‘Control’ but initialized using a slightly modified water vapor profile. Detailed application of the datasets is described in our under reviewing paper (Shang et al 2022).</p> <p>Reference:</p> <p>Shang, H., Hioki, S., Penide, G., Cornet, C., Letu, H., and Riedi, J.: Establishment of an analytical model for remote sensing of typical stratocumulus cloud profiles under various precipitation and entrainment conditions, Atmos. Chem. Phys. Discuss. [preprint], https://doi.org/10.5194/acp-2022-674, in review, 2022.</p> <p>Stevens, B., Lenschow, D. H., Vali, G., Gerber, H., Bandy, A., Blomquist, B., Brenguier, J.-L., Bretherton, C. S., Burnet, F., Campos, T., Chai, S., Faloona, I., Friesen, D., Haimov, S., Laursen, K., Lilly, D. K., Loehrer, S. M., Malinowski, S. P., Morley, B., Petters, M. D., Rogers, D. C., Russell, L., Savic-Jovcic, V., Snider, J. R., Straub, D., Szumowski, M. J., Takagi, H., Thornton, D. C., Tschudi, M., Twohy, C., Wetzel, M., and van Zanten, M. C.: Dynamics and Chemistry of Marine Stratocumulus—DYCOMS-II, Bulletin of the American Meteorological Society, 84, 579-594, 10.1175/BAMS-84-5-579 %J Bulletin of the American Meteorological Society, 2003.</p>
Supplementary data for the manuscript entitled "Evolution of the convective boundary layer in a WRF simulation nested down to 100 m resolution during a cloud-free case of LAFE 2017 and comparison to observations" (JGR Atmospheres)
<p>This dataset contains additional material to reproduce the simulation and some of the figures of the manuscipt entitled "Evolution of the convective boundary layer in a WRF simulation nested down to 100 m resolution during a cloud-free case of LAFE 2017 and comparison to observations" in the Journal of Geophysical Reasseach - Atmospheres.</p>
Simulated top-of-atmosphere (120 km) downward and upward solar and thermal-infrared irradiances and ice cloud optical thickness; calculated solar, TIR and net cloud radiative effect. Simulated with ice crystal properties for aggregates, droxtals, and plates based on Yang (2013).
<p>This dataset consists of three .nc files for ice crystal shapes of aggregates, plates, and droxtals. The files include ice cloud optical thickness <span class="math-tex">\(\tau\)</span> (550nm), the simulated upward and downward irradiances <span class="math-tex">\(F\)</span> at the top-of-atmosphere (with and without the presence of the ice cloud), and the calculated ice cloud radiative effect <span class="math-tex">\(\Delta F\)</span> (solar [0.3-3.5 <span class="math-tex">\(\mu\)</span>m], thermal-infrared [3.5-75 <span class="math-tex">\(\mu\)</span>m], and net). The data set allows the user to extract <span class="math-tex">\(\Delta F\)</span> values for their parameter combinations. The available cloudy and cloud-free irradiances further allow to calculate the cirrus radiative effect (RE) by scaling the 'cloudy' RE with the required cloud cover. This serves as a first-approximation because, as 3D effects are neglected.</p>
Data presented in "Fitting cumulus cloud size distributions from idealized cloud resolving model simulations"
<p>Updated repository containing the data produced and analzed in the manuscript entitled "Fitting cumulus cloud size distributions from idealized cloud resolving model simulations" by J. Savre and G. Craig, submitted to the Journal of Advances in Modeling Earth Systems.</p> <p>New uploads: time series file (T_S), vertical profiles (profiles_tot.nc) and horizontal slices at 2km (slice_z_2000_bis.nc). Uploaded files concern the LBA case only.</p>
AMIP and AMIP-P4K simulation dataset with Park-RH and Gauss-PDF cloud scheme
<p>This dataset includes AMIP and AMIP-P4K simulations using Park-RH and Gauss-PDF cloud schemes, respectively. </p> <p>Park-RH cloud scheme is the default cloud macrophysical scheme in NCAR CAM5. </p> <p>Gauss-PDF cloud scheme is a statistical cloud scheme, developed by Qin et al. (2018). </p> <p>All simulations are AMIP-type simulations from 1979 to 1988. The monthly output is archived here. </p> <p>Details of the model setup and the difference of two cloud schemes could be found in its related submitted manuscript. </p>
Data of the paper: Ensemble daily simulations for elucidating cloud–aerosol interactions under a large spread of realistic environmental conditions
<p>Data of the paper: Ensemble daily simulations for elucidating cloud–aerosol interactions under a large spread of realistic environmental conditions</p> <p> </p> <p>The name of variable are as in the paper</p>
The asymmetric diurnal latent heat flux in Chi-Lan montane cloud-fog forest: CLM simulations and sap flow observations
<p>Chilan_30min_sap_flow_V_2020JJA.csv recorded the data of sap flow velocity during JJA 2020.</p> <p>CL_CTR.*.nc is the analyzed CTR simulations which consider fog interception as a source of canopy water.</p> <p>CL_EXP.*.nc is the analyzed EXP simulations that do not allow the canopy to hold the water.</p>
3D Multi-fluid MHD Simulation of the Early Time Behavior of an Artificial Plasma Cloud in the Bottom Side Ionosphere
<p>This repository contains the necessary files for reproducing each of the figures in Ober et. al (2021) - currently a manuscript. Each file is named after the associated figure number and stored in the HDF5 format - with maximum compression. In general all chemical species physical variables are stored with four indexed dimensions: (species, z-axis, y-axis, x-axis).</p> <p> </p> <p>The details for chemical species are based in corresponding index values with string array ‘species’:<br> </p> <p>0 -> Ba</p> <p>1 -> O</p> <p>2 -> O+</p> <p>3 -> Ba+</p> <p>4 -> e-</p> <p> </p> <p>Axis values are in units of meters (m) from spatial origin </p> <p>X-axis (1, 300)</p> <p>Y-axis (1, 300)</p> <p>Z-axis (1, 888)</p> <p> </p> <p>Chemical species number densities in units of meters cubed (m^3)</p> <p>number_density ( 5, 888, 300, 300 ) </p> <p> </p> <p>Chemical species Temperature in units of Kelvin (K)</p> <p>temperature ( 5, 888, 300, 300 ) </p> <p> </p> <p>Chemical species velocity component values - meters per second (m/s)</p> <p>velocity_x ( 5, 888, 300, 300 ) </p> <p>velocity_y ( 5, 888, 300, 300 ) </p> <p>velocity_z ( 5, 888, 300, 300 ) </p> <p> </p> <p>Magnetic field in units of Teslas (T)</p> <p>magnetic_x ( 1, 888, 300, 300 ) </p> <p>magnetic_y ( 1, 888, 300, 300 ) </p> <p>magnetic_z ( 1, 888, 300, 300 ) </p> <p> </p> <p>NOTE: To help meet the data set limit, magnetic field variables are excluded from file “_figure-1.h5”. For "__figure-7b.h5" and "__figure-7c.h5" only magnetic field components are included.</p>
2DVD dataset for GMD publication - Simulated prognostic approach of graupel density in a bulk-type cloud microphysics scheme and evaluation during the ICE-POP field campaign
<p>This archive contains the 2DVD measurement of graupel particles used in the GMD paper "Simulated prognostic approach of graupel density in a bulk-type cloud microphysics scheme and evaluation during the ICE-POP field campaign".</p><p>For each identified graupel particle, the following are included:</p><ul><li>Volume-equivalent diameter (mm)</li><li>Density (g cm-3)</li><li>Fall velocity (m s-1)</li></ul>
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>
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>
Data for "First simulations of feedback algorithm-regulated marine cloud brightening"
<p>Data for the manuscript "First simulations of feedback algorithm-regulated marine cloud brightening," submitted for publication in the journal Geophysical Research Letters.</p>
Simulations for convection during the intensive observation period of the TWP-ICE field campaign: results from cloud-resolving model and convective parameterization schemes
<p>Simulated convections from cloud-resolving model and convective parameterization schemes during the intensive observation period of the TWP-ICE field campaign, which are used to investigate the scale-awareness problem of convective parameterization schemes. The corresponded observations are also included in this dataset.</p>
Data and analysis scripts for "Impact of grid spacing, convective parameterization and cloud microphysics in ICON simulations of a warm conveyor belt"
<p>The data relates to the analysis of "Impact of grid spacing, convective parameterization and cloud microphysics in ICON simulations of a warm conveyor belt" and is in support and reference to the work titled as the same for a research article authored by Choudhary and Voigt. The file 'data.zip' contains different directories named as per the computations related to particular analysis as mentioned below:</p> <table> <tbody> <tr> <td> <p>Directory names and content inside 'data.zip'</p> </td> </tr> <tr> <td> <p>Name of directory</p> </td> <td> <p>Content</p> </td> </tr> <tr> <td> <p>trajectories </p> </td> <td> <p>Output from run of LAGRANTO tool on regridded ICON simulations<strong><sup>*</sup></strong> (the input data is not provided here). The output contains computations of 'warm conveyor belt (WCB)' trajectories and different variables traced along them which are stored in different files named as outtrace1.nc and so on. The subdirectories are named as per the resolution, convection and cloud microphysics parametrization used in model setup, for e.g. '0.025convon/1m' corresponds to 2.5 km resolution with parametrized convection and 1-moment cloud microphysics. The file with name ending with 'box' corresponds to different subclasses of trajectory as described in the work. </p> </td> </tr> <tr> <td> <p>dhr</p> </td> <td> <p>diabatic heating rate, traced along WCB and binned in pressure levels (for 1-moment cloud microphysics)</p> </td> </tr> <tr> <td> <p>dynamical variables</p> </td> <td> <p>av- absolute vorticity, pv- potential vorticity and w- vertical wind traced along WCB and binned in pressure levels (for 1-moment cloud microphysics)</p> </td> </tr> <tr> <td> <p>statistics</p> </td> <td> <p>statistics (different statistical computation of WCB parcels ascent for 1- and 2- moment cloud microphysics, details described in work)</p> </td> </tr> <tr> <td> <p>theta_e</p> </td> <td> <p>equivalent potential temperature for 2.5 km explicit convection simulation (.pkl file) for 2016.09.22 12UTC, 2016.09.23 12UTC and 2016.09.24 12UTC in files numbered 1, 2 and 3 respectively.</p> </td> </tr> <tr> <td> <p>ipv</p> </td> <td> <p>same as above but for isentropic potential vorticity</p> </td> </tr> <tr> <td> <p>mslp</p> </td> <td> <p>mslp.nc- same as above but for mean sea level pressure field </p> </td> </tr> <tr> <td> <p>cyclone_track</p> </td> <td> <p>File name with convention 'nawdexnwp-5km-mis-0001_vladiana_cyclonepressure.nc' corresponds to file containing central mean sea level pressure of cyclone Vladiana during the simulation period for 5 km resolution with parametrized convection and 1-moment cloud microphysics. </p> <p>coord_1m.npy- the coordinate/ location of cyclone Vladiana based on central mean sea level pressure </p> </td> </tr> <tr> <td> <p>pte</p> </td> <td> <p>Pressure tendency equation (PTE) analysis. Subdirectories are named after different resolutions of simulation with 1-moment cloud microphysics and parametrized convection (except for 10, 5 and 2.5 km where convection is explicit).</p> <p>Further, the .npy files are named as per the different vertically integrated tendency terms of PTE equation they represent, as mentioned below:</p> <p>dp_dt: surface pressure</p> <p>dfi_dt: geopotential (upper boundary of integral)</p> <p>i_diab_res: diabatic heating (including residual)</p> <p>i_itt: virtual temperature</p> <p>i_tadv: temperature advection</p> <p>i_vmt: vertical motions</p> </td> </tr> </tbody> </table> <p> </p> <p><strong>*</strong>Note: The original ICON simulations which are not part of this dataset were carried out by Prof. Aiko Voigt (University of Vienna) at the Mistral High Performance Computing system of the German Climate Computing Center (DKRZ) in Hamburg, Germany. The primary data of the ICON simulations (run scripts, namelists, scripts for lateral boundary data) are published at KITopen of Karlsruhe Institute of Technology, https://doi.org/10.5445/IR/1000123695. Note that the KITopen dataset includes all simulations of \cite{svchd20}, from which a subset is analyzed here.</p> <p> </p> <p>The file 'scripts.zip' contains two directories which is related with analysis presented in the work:</p> <p>'plotting_scripts' includes script for creating different plots/ figures showcased in the work </p> <p>'processing_scripts' includes computations for producing different quantities/ data that are named as described above in the table for 'data.zip'</p> <p> </p>
An evaluation of cloud-precipitation structures in mixed-phase stratocumuli over the southern ocean in kilometer-scale ICON simulations during CAPRICORN
<p>The repository contains ICON simulated outputs (Cntrl_ICON.nc.gz, No_Gr_ICON.nc.gz, No_Ice_ICON.nc.gz, 100m_Vert_ICON.nc.gz, and 50m_Vert_ICON.nc.gz) and two km mean and ensemble forward simulated results (Cntrl_PAMTRA.gz, No_Gr_PAMTRA.gz, No_Ice_PAMTRA.gz, 100m_Vert_PAMTRA.gz, and 50m_Vert_PAMTRA.gz) for the two day period (00:00 UTC on March 26, 2016 to 00:00 UTC on March 28, 2016). The prefix in the filenames corresponds to the experiments described in the manuscript.</p>
Data and scripts for the paper: Evaluating and improving a PDF cloud scheme using high-resolution super-large-domain simulations
<p>This is a collection of the processed data, plotting scripts, and processing scrips used in the making of the publication: "Evaluating and improving a PDF cloud scheme using high-resolution super-large-domain simulations" By Griewank, Schemann, and Neggers. </p> <p>WARNING: These files were used exclusively used by Philipp Griewank on his local work station, and the main purpose of uploading them is for reasons of scientific transparency. They are not highly optimized tools intended for general usage, so comments are hit and miss. Feel free to contact Philipp Griewank (currently philipp.griewank@uni-koeln.de) with any questions. </p>
Data for Cloud resolving WRF simulations of precipitation and soil moisture over the central Tibetan Plateau: an assessment of various physics options
<p>This dataset accompanies the submitted paper in the Earth and Space Science:Cloud resolving WRF simulations of precipitation and soil moisture over the central Tibetan Plateau: an assessment of various physics options.</p>
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Allen Brain Atlas
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Annotated Behaviour and Observability Dataset (ABODe)
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International Brain Laboratory public data
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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.