Find research datasets worth reusing
Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.
89
datasets available to search
ShareScore release 0.9.0
Dataset results
89 results for “Stratocumulus”
Data for paper "Stratocumulus adjustments to aerosol perturbations disentangled with a causal approach"
<p>Timeseries data used for the causal effect estimation of the paper "Stratocumulus adjustments to aerosol perturbations disentangled with a causal approach".</p> <p>This dataset contains several cloud parameters and meteorological co-variates corresponding to the evolution of the South-East Atlantic stratocumulus deck for the time period January 2016 to December 2017 and the spatial domain [lon1,lon2,lat1,lat2]=[0, 10, -20, -10]. </p> <p>The processing code used to generate the timeseries data, as well as the analysis code are uploaded separately on Zenodo. The input raw satellite and reanalysis data for the processing code are from EUMETSAT (Copyright (c) (2020) EUMETSAT), NASA and COPERNICUS data (generated using Copernicus Climate Change Service information [2022]). </p> <p> </p> <p>Citations for the raw data sources: </p> <p>Finkensieper, S., Meirink, J.-F., van Zadelhoff, G.-J., Hanschmann, T., Benas, N., Stengel, M., Fuchs, P., Hollmann, R., Kaiser, J., Werscheck, M.: CLAAS-2.1: CM SAF CLoud property dAtAset using SEVIRI - Edition 2.1. Satellite Application Facility on Climate Monitoring (2020). <a href="https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V002_01">https://doi.org/10.5676/EUM_SAF_CM/CLAAS/V002_01</a></p> <p>Huffman, G.J., Stocker, E.F., Bolvin, D.T., Nelkin, E.J., Tan, J.: GPMIMERG Final Precipitation L3 Half Hourly 0.1 degree x 0.1 degree V06. MD, Goddard Earth Sciences Data and Information Services Center (GES DISC) (2019). <a href="https://doi.org/10.5067/GPM/IMERG/3B-HH/06">https://doi.org/10.5067/GPM/IMERG/3B-HH/06</a>.</p> <p>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horanyi, A., Munoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Th ́ebaut, J.-N.: ERA5 hourly data on single levels from 1959 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS) (2018). <a href="https://doi.org/10.24381/cds.adbb2d47">https://doi.org/10.24381/cds.adbb2d47</a></p> <p>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horanyi, A., Munoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., Th ́ebaut, J.-N.: ERA5 hourly data on pressure levels from 1959 to present. Copernicus Climate Change Service (C3S) Climate Data Store (CDS) (2018). <a href="https://doi.org/10.24381/cds.bd0915c6">https://doi.org/10.24381/cds.bd0915c6</a></p>
Effects of secondary ice processes on a stratocumulus to cumulus transition during a cold-air outbreak
<p>Dataset of model simulations discussed in paper "Effects of secondary ice processes on a stratocumulus to cumulus transition during a cold-air outbreak", <a href="https://doi.org/10.1016/j.atmosres.2022.106302">https://doi.org/10.1016/j.atmosres.2022.106302</a></p>
Data from Large-eddy simulation data of stratocumulus advecting over cold and warm waters
<p>Data from Large-eddy simulation data of stratocumulus advecting over cold and warm waters. The LES model used is the System for Atmospheric Modeling (SAM) model (http://rossby.msrc.sunysb.edu/~marat/SAM.html). </p>
CASCCAD: Cumulus And Stratocumulus Cloudsat-CAlipso Dataset
<p>In these two datasets, we document spatial distributions and profiles of stratocumulus (Sc) and cumulus (Cu) clouds on a global scale.</p> <p>To this end, we design an original discrimination algorithm (DA) that distinguishes Sc and Cu based on three observable cloud-properties: cloud top height (CTH), horizontal cloud fraction (HCF) and vertical cloud fraction variability (VCF). These simple criteria are sufficient to characterize the distinctive shape of Cu, which have a limited horizontal extent and highly variable CTH as opposed to Sc, which cover larger areas and have a small and stable geometrical thickness. The DA is utilized on instantaneous profiles of active-sensor Cloud-Aerosols Lidar and Infrared Pathfinder Satellite Observations (CALIPSO), specifically the General Circulation Model - Oriented CALIPSO Cloud Product (CALIPSO-GOCCP), and CloudSat-CALIPSO combined observations.</p> <p>By documenting the geographical distribution of Sc and Cu clouds, these datasets make it possible to evaluate the shallow convection (Cu type) and boundary layer (Sc type) clouds in state-of-the art climate models, which are typically generated by distinct parametrizations.</p>
Model output from "Potential impacts of marine fuel regulations on an Arctic stratocumulus case and its radiative response"
<p>Model output from "Potential impacts of marine fuel regulations on an Arctic stratocumulus case and its radiative response", in review at Atmospheric Chemistry and Physics, 2024, same authors.<br>The simulations were done using MIMICA version 4 (Savre at el., 2014) and the data includes all model output presented in the publication.</p>
NCAS CDAO Sky-Camera Time-Lapse Video for 2007-09-04 17:50 UTC (showing Stratocumulus clouds that develop wave-like asperitas features).
<p>A time-lapse video showing Stratocumulus clouds that develop wave-like asperitas features. During the early part of the sequence, there appear to be layers of cloud at two distinct levels. These are lit differently by the setting sun. The asperitas features begin to appear approximately half way through the sequence. Altocumulus undulatus clouds can be seen towards the end. This video has been created from images taken by the Sky-Camera at the National Centre for Atmospheric Science (NCAS) Capel Dewi Atmospheric Observatory (CDAO) near Aberystwyth, UK - formerly known as the Natural Environment Research Council (NERC) Mesosphere-Stratosphere-Troposphere (MST) Radar Facility. The images are freely available under a UK Open Government Licence from http://tinyurl.com/nerc-mstrf-sky-camera/ . For more details visit http://mst.nerc.ac.uk .</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>
Supplement to the "Response to the Referee" of the article "Simulation of marine stratocumulus using the super-droplet method: Numerical convergence and comparison to a double-moment bulk scheme"
<p>This is the supplement to the "Response to the Referee" of the article "Simulation of marine stratocumulus using the super-droplet method: Numerical convergence and comparison to a double-moment bulk scheme".</p> <p><a href="https://zenodo.org/api/files/87a1c801-d2f8-4c0c-b408-db56481cf97d/Movie%201_w_theta_v_t.mp4">Movie 1_w_theta_v_t.mp4</a>: Time evolution of vertical profiles of buoyancy production and its decomposition.</p> <p><a href="https://zenodo.org/api/files/6eabdd4d-61fd-4f7a-8656-0c9d1fb7ce6f/Movie%202_scatter_w_theta_v_t.mp4">Movie 2_scatter_w_theta_v_t.mp4</a>: Time evolution of scatter plots of w vs theta_v.</p> <p><a href="https://zenodo.org/api/files/6eabdd4d-61fd-4f7a-8656-0c9d1fb7ce6f/Movie%203_boy_incloud_real_z_t.mp4">Movie 3_boy_incloud_real_z_t.mp4</a>: Time evolution of buoyancy production and cloud fraction, and time series of cloud cover.</p> <p><a href="https://zenodo.org/api/files/2f5a17d9-6cb1-4769-88cd-65fd8efd0225/qr_cross.mp4">qr_cross.mp4</a>: Time evolution of cross section of rain water mixing ratio (q<sub>r</sub>) of SDM and SN14 simulation.</p>
Diurnal cycle of the semi-direct effect from a persistent absorbing aerosol layer over marine stratocumulus in large-eddy simulations: supporting dataset
<p>Relevant data for reproducing figures and tables from Herbert et al., 2020, Atmospheric Chemistry and Physics: "Diurnal cycle of the semi-direct effect from a persistent absorbing aerosol layer over marine stratocumulus in large-eddy simulations".</p> <p>The tar file contains the relevant netcdf files that contain simulation output from the MET Office large eddy model (LEM). Each netcdf file corresponds to a single model setup and experiment - details of which can be found within the published manuscript. Information concerning the LEM, and details on how to obtain access to the code, can be found at http://appconv.metoffice.com/LEM/index.html.</p> <p>The file 'relevant_information.pdf' contains two tables. The first provides the names of each netcdf file that was used to produce each figure in the manuscript. The second provides the metadata for the netcdf variables that were used to produce the figures and tables.</p> <p>The netcdf filename provides information on the experiment setup as follows:</p> <p>run-identifier-number _ length-of-run _ AOD-of-layer _ GAP-between-cloud-and-layer _ THICKNESS-of-layer _ 'bigarray' _ special-setups-for-sensitivity-experiments</p> <p>For example, r1360_8day_transient_aod02_0dz_250th_bigarray_nodrizz is run number r1360, simulating 8 days, with the aerosol layer properties AOD=0.2, cloud-to-aerosol gap of 0m, layer thickness of 250m, in the sensitivity setup where precipitation has been switched off.</p>
An LES perturbed parameter ensemble of free-tropospheric cloud-controlling factors on stratocumulus
<p>This dataset contains a perturbed parameter ensemble of large-eddy simulations to assess the effect of two free-tropospheric cloud-controlling factors on stratocumulus clouds properties. The simulations were run on the UK Met Office/NERC cloud model (MONC) for the DYCOMS-II RF01 nocturnal stratocumulus case (Stevens et. al., 2005). Each simulation had the same initial conditions except for the two perturbed parameters, which were the jumps in moisture and temperature at the temperature inversion at cloud top. The corresponding analysis code can be found at this <a href="https://github.com/eers1/dycoms_analysis">dycoms_analysis Github page</a>. </p><p> </p><p>Stevens, B., Moeng, C. H., Ackerman, A. S., Bretherton, C. S., Chlond, A., de Roode, S., . . . Zhu, P. (2005). Evaluation of large-eddy simulations via observations of nocturnal marine stratocumulus. Monthly Weather Review , 133 (6), 1443–1462. doi: 10.1175/MWR2930.1</p>
Supplement to the article "Simulation of marine stratocumulus using the super-droplet method: Numerical convergence and comparison to a double-moment bulk scheme"
<p>This is a supplement to the article "Simulation of marine stratocumulus using the super-droplet method: Numerical convergence and comparison to a double-moment bulk scheme".</p> <p>The time evolution of horizontal distribution of LWP:</p> <ul> <li>SDM_lwp_2d_sdm.mp4: from nine SDM runs with different grid resolutions.</li> <li>SN14_lwp_2d_sn14.mp4: from nine SN14 runs with different grid resolutions.</li> </ul> <p>The time evolution of vertical profiles:</p> <ul> <li>sdm_profile_t.mp4: from nine SDM runs with different grid resolutions.</li> <li>sn14_profile_t.mp4: from nine SN14 runs with different grid resolutions.</li> <li>sdm_incloud_t.mp4: vertical profiles in cloudy areas and cloud holes in SDM runs.</li> <li>sn14_incloud_t.mp4: vertical profiles in cloudy areas and cloud holes in SN14 runs.</li> <li>sdm_50x5_incloud_t.mp4: comparison between original SDM and SDM without sedimentation.</li> </ul>
Data for paper "Investigating the sign of stratocumulus adjustments to aerosols in the global storm-resolving model ICON"
<p>Data for paper "Investigating the sign of stratocumulus adjustments to aerosols in the global storm-resolving model ICON". The code used to generate, analyze and plot these data is provided separately on Zenodo. The zip files named 2.zip_file_name are used in the 2.make_comparison_plots notebooks in the companion Zenodo software repository. The zip files named 3.zip_file_name are generated using the code contained in 1.process_data in the software repository and used for the analyses in 3.calculate_causal_effects in the software repository. </p> <p><strong>References - Code ______________________________________________</strong></p> <p>J. Runge et al. (2015): Identifying causal gateways and mediators in complex spatio-temporal systems. Nature Communications, 6, 8502. <a href="http://doi.org/10.1038/ncomms9502">http://doi.org/10.1038/ncomms9502</a></p> <p>J. Runge, Necessary and sufficient graphical conditions for optimal adjustment sets in causal graphical models with hidden variables, Advances in Neural Information Processing Systems, 2021, 34. <a href="https://proceedings.neurips.cc/paper/2021/hash/8485ae387a981d783f8764e508151cd9-Abstract.html">https://proceedings.neurips.cc/paper/2021/hash/8485ae387a981d783f8764e508151cd9-Abstract.html</a></p> <p><strong>References - Data ______________________________________________</strong></p> <p><em>SEVIRI</em> <br>Benas, N., Solodovnik, I., Stengel, M., Hüser, I., Karlsson, K.-G., Håkansson, N., Johansson, E., Eliasson, S., Schröder, M., Hollmann, R., and Meirink, J. F.: CLAAS-3: The Third Edition of the CM SAF Cloud Data Record Based on SEVIRI Observations, Earth System , Science Data Discussions, pp. 1-38, <a href="https://doi.org/10.5194/essd-2023-79">https://doi.org/10.5194/essd-2023-79</a>, 2023.</p> <p><em>GOES</em><br>Walther, A. and Straka, W.: Algorithm Theoretical Basis Document For Daytime Cloud Optical and Microphysical Properties (DCOMP), 2020</p> <p><em>ERA5</em><br>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thébaut, J.-N.: ERA5 Hourly Data on Single Levels from 1959 to Present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), <a href="https://doi.org/10.24381/cds.adbb2d47">https://doi.org/10.24381/cds.adbb2d47</a>, 2018a.<br>Hersbach, H., Bell, B., Berrisford, P., Biavati, G., Horányi, A., Muñoz Sabater, J., Nicolas, J., Peubey, C., Radu, R., Rozum, I., Schepers, D., Simmons, A., Soci, C., Dee, D., and Thébaut, J.-N.: ERA5 Hourly Data on Pressure Levels from 1959 to Present, Copernicus Climate Change Service (C3S) Climate Data Store (CDS), <a href="https://doi.org/10.24381/cds.bd0915c6">https://doi.org/10.24381/cds.bd0915c6</a>, 2018b.</p> <p><em>GPM</em><br>Huffman, G., Stocker, E., Bolvin, D., Nelkin, E., and Tan, J.: GPM IMERG Final Precipitation L3 Half Hourly 0.1 Degree x 0.1 Degree V07, Greenbelt, MD, Goddard Earth Sciences Data and Information Services Center (GES DISC), <a href="https://doi.org/10.5067/GPM/IMERG/3B-HH/07">https://doi.org/10.5067/GPM/IMERG/3B-</a><a href="https://doi.org/10.5067/GPM/IMERG/3B-HH/07">HH/07</a>, 2023</p> <p><em>MODIS</em><br>Platnick, S. et al. MODIS atmosphere L3 daily product. NASA<a href="https://doi.org/10.5067/MODIS/MOD08_D3.006"> https://doi.org/10.5067/MODIS/MOD08_D3.006 </a>, 2015.</p> <p><em>MIDAS</em><br>Eastman, R., McCoy, I. L., Schulz, H., and Wood, R.: A Survey of Radiative and Physical Properties of North Atlantic Mesoscale Cloud Morphologies from Multiple Identification Methodologies, EGUsphere, pp. 1–33, <a href="https://doi.org/10.5194/egusphere-2023-2118">https://doi.org/10.5194/egusphere-2023-2118</a>, 2023. <br>McCoy, I. L., McCoy, D. T., Wood, R., Zuidema, P., and Bender, F. A.-M.: The Role of Mesoscale Cloud Morphology in the Shortwave Cloud Feedback, Geophysical Research Letters, 50, e2022GL101 042, <a href="https://doi.org/10.1029/2022GL101042">https://doi.org/10.1029/2022GL101042</a>, 2023.</p> <p><em>ICON</em><br>Zängl, G., Reinert, D., Rípodas, P., and Baldauf, M.: The ICON (ICOsahedral Non-hydrostatic) Modelling Framework of DWD and MPI-M: Description of the Non-Hydrostatic Dynamical Core, Quarterly Journal of the Royal Meteorological Society, 141, 563–579, <a href="https://doi.org/10.1002/qj.2378">https://doi.org/10.1002/qj.2378</a>, 2015<br><a href="https://code.mpimet.mpg.de/projects/iconpublic/wiki/Instructions_to_obtain_the_ICON_model_code_with_a_personal_non-commercial_research_license">https://code.mpimet.mpg.de/projects/iconpublic/wiki/Instructions_to_obtain_the_ICON_model_code_with_a_personal_non-commercial_research_license </a></p>
Improving stratocumulus cloud amounts in a 200-m resolution multi-scale modeling framework through tuning of its interior physics Part 2
<p>This dataset includes model outputs averaged from day 2 to day 15 using the multiscale modeling framework (MMF, also referred to as ``superparameterization'') for</p> <ul> <li>Low-resolution MMF (LR): SP_newsst_long_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_32_x_120z1200m.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc <ul> <li>crm_nx = 32, crm_ny = 1, crm_dx = 1200 m, crm_dt = 5s, crm_nx_rad = 16, crm_ny_rad=1</li> </ul> </li> <li>High-resolution MMF (HR): UP_newsst_long_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z200m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc <ul> <li>crm_nx = 64, crm_ny = 1, crm_dx = 200 m, crm_dt = 0.5s, crm_nx_rad = 16, crm_ny_rad=1</li> </ul> </li> <li>Same as HR, but considers hyperviscosity with tau = 30s (HRh30): UPhyperlag30_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z100m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> <li>Same as HR, but considers hyperviscosity with tau = 150s (HRh15): UPhyperlag15_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z100m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> <li>Same as HRh, but considers both hyperviscosity and sedimentation (HRhs15) with tau = 30s and sigmag = 1.5: HPhyper_sedi15_long_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z200m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> <li>Same as HRh, but considers both hyperviscosity and sedimentation (HRhs12) with tau = 30s and sigmag = 1.2: HPhyper_sedi12_long_newsst_fluxout_L125_ERA5_2008_F-MMF1_frontera_ne16pg2_r05_oQU240_CRM1_64_x_120z200m.0.5s_crm_nx_rad_16_np_768_nlev_125.frontera.cam.h0.2008-10_2To15.nc</li> </ul>
Improving stratocumulus cloud amounts in a 200-m resolution multi-scale modeling framework through tuning of its interior physics Part 1
<p>This dataset includes 6-month simulations using the ne30pg2 grid. Monthly averaged output files from six experiments are included.</p> <ul> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1 <ul> <li>The config options for this control simulation is <pre>CAM_CONFIG_OPTS = -mach summit -phys default -use_MMF -crm samxx -nlev 60 -crm_nz 50 -crm_dt 10 -crm_dx 2000 -crm_nx 64 -crm_ny 1 -crm_nx_rad 4 -crm_ny_rad 1 -rad rrtmgp -rrtmgpxx -MMF_microphysics_scheme sam1mom -chem none -nlev 125 -crm_nz 115 -crm_dt 2 -crm_dx 200 -crm_nx 256 -crm_ny 1 -crm_nx_rad 4 -crm_ny_rad 1 -use_MMF_VT -cppdefs ' -DMMF_ESMT -DMMF_USE_ESMT -DMMF_HYPERVISCOSITY -DMMF_SEDIMENTATION ' </pre> </li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED <ul> <li>The HV.SED control case is the same as the control (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1), but considered both hyperciscosity and sedimentation processes.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_1E-04 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_1E-04.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_5E-04 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_5E-04.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_5E-04_QI_5E-05 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_1E-04 and ice from QI_1E-04 (default) to QI_5E-05.</li> </ul> </li> <li>E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED.QW_5E-04_QI_8E-05 <ul> <li>Same as the HV.SED control case (E3SM.INCITE2022-LOW-CLD-00.ne30pg2.F2010-MMF1.L125_115.NXY_256x1.HV.SED), but changed the autoconversion thresholds for liquid from QW_1E-03 (default) to QW_1E-04 and ice from QI_1E-04 (default) to QI_8E-05.</li> </ul> </li> </ul>
Large eddy simulations of precipitating marine stratocumulus are sensitive to free tropospheric aerosol concentrations
<p>A sample of prognostic and diagnostic Control simulation data generated for our study. Data include the time series and vertical profiles used in the analysis of our study into the importance of free tropospheric aerosol in marine stratocumulus clouds. Full datasets are available on request from the author.</p>
Data for Aircraft observations of aerosol and microphysical quantities of stratocumulus in autumn over Guangxi Province, China: Diurnal variation, vertical distribution and aerosol-cloud relationship
<p>Data for Aircraft observations of aerosol and microphysical quantities of stratocumulus in autumn over Guangxi Province, China: Diurnal variation, vertical distribution and aerosol-cloud relationship</p>
Data for the publication 'The impact of warm and moist air mass perturbations on Arctic mixed-phase stratocumulus'
<p>This repository contains the data for the paper: 'Eirund, G. K., Possner, A. and Lohmann, U.: The impact of warm and moist air mass perturbations on Arctic mixed-phase stratocumulus.', published in J. Climate, 2020 (doi: 10.1175/JCLI-D-20-0163.1.).</p> <p>Each netcdf file contains the data for the respective simulation indicated by the prefix of the filename. Important information on the data are included in the README file.</p> <p> </p>
First ISCCP Regional Experiment (FIRE) Atlantic Stratocumulus Transition Experiment (ASTEX) Centre Meteorologie Spatiale (CMS) Daily Solar Irradiance Data
The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution clouddata.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.These files are calculations of the daily solar irradiance at the surface, based on observations by the METEOSAT. The file naming convention is: esqDDMMYYx.fis where DDMMYY is the dateThese files are: I2 pixels, 376 pixels/row, 326 rows. Each pixel has a spatial resolution of 0.04 degrees.The header of each file claims there are two channels, although the provided documentation states that there is only one channel per file.The units are: flux [tenths of Joule/cm^2]
First ISCCP Regional Experiment (FIRE) Atlantic Stratocumulus Transition Experiment (ASTEX) Centre Meteorologie Spatiale (CMS) Monthly Solar Irradiance Data
The First ISCCP Regional Experiments have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to improve basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between the ISCCP data, GCM parameterizations, and higher space and time resolution cloud data.To-date, four intensive field-observation periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems.These files are calculations of the monthly solar irradiance at the surface, based on observations by the METEOSAT. The file naming convention is: esmxx.fis where xx is the month number of 1992.These files are: I2 pixels, 376 pixels/row, 326 rows. Each pixel has a spatial resolution of 0.04 degrees.The header of each file claims there are two channels, although the provided documentation states that there is only one channel per file.The units are: flux [tenths of Joule/cm^2]
First ISCCP Regional Experiment (FIRE) Marine Stratocumulus ISCCP Stage DX Pixel Level Cloud Product - Revised Algorithm Data
The First ISCCP Regional Experiments (FIRE) have been designed to improve data products and cloud/radiation parameterizations used in general circulation models (GCMs). Specifically, the goals of FIRE are (1) to seek the basic understanding of the interaction of physical processes in determining life cycles of cirrus and marine stratocumulus systems and the radiative properties of these clouds during their life cycles and (2) to investigate the interrelationships between International Satellite Cloud Climatology Project (ISCCP) data, GCM parameterizations, and higher space and time resolution cloud data. To-date, four intensive field-observation (IFO) periods were planned and executed: a cirrus IFO (October 13 - November 2, 1986); a marine stratocumulus IFO off the southwestern coast of California (June 29 - July 20, 1987); a second cirrus IFO in southeastern Kansas (November 13 - December 7, 1991); and a second marine stratocumulus IFO in the eastern North Atlantic Ocean (June 1 - June 28, 1992). Each mission combined coordinated satellite, airborne, and surface observations with modeling studies to investigate the cloud properties and physical processes of the cloud systems. A subset of the ISCCP Stage DX Cloud Product (Revised Algorithm) are included for the FIRE Marine Stratocumulus region.
ScienceDex guides
Understand access before you commit
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.