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2,170 results for “clouds”
Understanding cirrus clouds using explainable machine learning
<p>This repository contains the data for the paper:</p> <p>Authors: Kai Jeggle , David Neubauer , Gustau Camps-Valls and Ulrike Lohmann<br> Titel: Understanding cirrus clouds using explainable machine learning<br> Date: 2023</p> <p>Note that the scripts can be found in the accompanying package (https://github.com/tabularaza27/explaining_cirrus)</p>
E3SMv2 cloud evaluation with COSP
<p>These are the simulator output from the model simulations for Zhang et al. (2023). The v1 and v2 simulations are the last 30-year (1985-2014) data from the 165-year historical simulations using E3SMv1 and v2. The four sensitivity tests with the 6-year simulations are to isolate the impact of changes in model parameter choices in its turbulence, shallow convection, and cloud macrophysics parameterization (CLUBB), microphysical parameterization (MG2), and deep convection scheme (ZM), as well as model physics changes in convective triggering.</p> <p>The simulations of version 1 and version 2 are reproduced using maintenance branches maint-1.0 (https://github.com/E3SM-Project/E3SM/tree/maint-1.0) and maint-2.0 (https://github.com/E3SM-Project/E3SM/tree/maint-2.0), respectively.</p>
Word-to-word transcripts of the interviews/consultations on What do FNS-Cloud Food Researchers Want to Know?
<p>Word to word transcripts of 11 semi-structured interviews carried out during the period April-October 2020 on 'What do FNS-Cloud Food Researchers Want to Know'. The notes were independently taken by the two interviewers during the interviews and combined to produce almost word-to-word transcripts of the interviews. Included in this Excel are also the questions addressed. The semi-guided interviews were undertaken to ensure the content validity of trainings for the FNS-Cloud community, and to reflect the training needs and preferences of FNS-Cloud project partners.</p> <p>Both interviewers (2 persons) and interviewed (15 persons) were food science professionals participating in the FNS-Cloud project (H2020 No. 863059). The Interviews, which lasted 30 minutes, aimed at identifying training needs and preferences related to open science and the use of the project datasets, tools and services: what partners want to learn, how they prefer to learn, and who are their ideal teachers. </p> <p>Inductive coding of the transcripts was done with the NVivo 12 Pro© software for qualitative analysis, following an iterative approach involving three researchers reviewing interview transcripts, codes, sub-codes, and coded phrases. The results of the qualitative analysis are presented in the paper: Teaching Open Science. What do FNS-Cloud Food Researchers Want to Know? presented at the 8th International Conference on Higher Education Advances (HEAd’22),<a href="http://headconf.org/"> </a><a href="http://headconf.org/">HEAd'22 | June 14-17, 2022 · Valencia, Spain (headconf.org)</a> and published as a Peer-reviewed article in the: Proceedings of the 8th International Conference on Higher Education Advances (HEAd’22) (includes DOI and ISBN) </p>
Initiation Process of a Winter Cloud-to-Ground Lightning Flash
<p>Lightning location results data. This dataset includes complete data, which means it has not undergone quality control. The time needs to be subtracted from the corresponding paper (Initiation Process of a Winter Cloud-to-Ground Lightning Flash) by 1.165ms.</p>
Monthly global grids of lidar-consistent opaque cloud fractions and altitude of attenuation 2008-2034 according to predictions from CESM2 and IPSL-CM6 GCMs
<p>Monthly global grids of opaque cloud fractions and altitude of attenuation 2008-2034 according to predictions from CESM and LMDZ GCMs.</p> <ul> <li>One file describe the CESM2 prediction (cesm.nc); one file describes the IPSL-CM6 predictions (lmdz.nc).</li> <li>Predictions from both models were obtained following the RCP8.5 scenario.</li> <li>The lidar-consistent variables were generated by the COSP lidar simulator (v1.4).</li> </ul> <p>See 10.5194/egusphere-2022-1187 for a description of the dataset and its interpretation</p>
First observational evidence of the relationship between the tropospheric temperature and cirrus cloud occurrence over India
<p>This dataset contains the normalized relative backscatter (NRB) coefficients and cloud fractions observed using Micro Pulse Lidar over Kattankulathur (12.82<sup>o</sup>N, 80.04<sup>o</sup>E ) presented in the paper.</p> <p> </p> <p> </p>
The exemplary basin modeling with Global Hydrologic Data Cloud (GHDC) and SHUD model.
<p><strong>GHDC</strong> = Global Hydrological Data Cloud</p> <p><strong>SHUD</strong> = Simulator of Hydrologic Unstructured Domains</p> <p> </p> <p>Files in the package:</p> <p> </p> <ul> <li> <p>Conestoga - The data retrived from GHDC and the modeling result by SHUD model, for Gonestoga, Pennsylvania, USA.</p> </li> <li> <p>Gummara - The data retrived from GHDC and the modeling result by SHUD model, for Gummara, Ethiopia</p> </li> <li> <p>heihe - The data retrived from GHDC and the modeling result by SHUD model, for Heihe Headwater, Gansu, China</p> </li> <li> <p>threeBasin - The R analysis code for the three basin simulations, including loading data and visualization.</p> </li> </ul> <p> </p> <p>The file structure in each basin folder:</p> <table> <thead> <tr> <th>FOLDER</th> <th>FILE OR SUBFOLDER(BOLD)</th> <th>DESCRIPTION</th> </tr> </thead> <tbody> <tr> <td>ETV</td> <td>-</td> <td>General ETV data.</td> </tr> <tr> <td> </td> <td>dem.tif</td> <td>DEM subset of ASTER Global DEM.</td> </tr> <tr> <td> </td> <td>Soil.csv</td> <td>Soil texture of soil layer reclassified from HWSD subset.</td> </tr> <tr> <td> </td> <td>Geol.csv</td> <td>Soil texture of geology layer reclassified from HWSD subset.</td> </tr> <tr> <td> </td> <td>hwsd.Geol.csv</td> <td>Soil texture, organic matter and bulk density of geology layer HWSD subset.</td> </tr> <tr> <td> </td> <td>hwsd.Soil.csv</td> <td>Soil texture, organic matter and bulk density of soil layer HWSD subset.</td> </tr> <tr> <td> </td> <td>hwsd.tif</td> <td>HWSD data subset.</td> </tr> <tr> <td> </td> <td>buff.shp, .dfb, .prj, .shx</td> <td>Shapefile of buffered polygon from user-provided watershed.</td> </tr> <tr> <td> </td> <td>outlets.shp, .dfb, .prj, .shx</td> <td>Shapefile of watershed pourpoint, calculated from DEM.</td> </tr> <tr> <td> </td> <td>stm_dem.shp, .dfb, .prj, .shx</td> <td>Shapefile of watershed pourpoint, calculated from DEM.</td> </tr> <tr> <td> </td> <td>wbd_dem.shp, .dfb, .prj, .shx</td> <td>Shapefile of watershed pourpoint, calculated from DEM.</td> </tr> <tr> <td> </td> <td><strong>TSD</strong></td> <td>Time-series forcing files.</td> </tr> <tr> <td> </td> <td><strong>GCS</strong></td> <td>Subfolder, terriestial data in GCS</td> </tr> <tr> <td> </td> <td><strong>PCS</strong></td> <td>Subfolder, terriestial data in PCS.</td> </tr> <tr> <td> </td> <td><strong>PCS</strong>/landuse.tif</td> <td>Landuse raster subset.</td> </tr> <tr> <td> </td> <td><strong>PCS</strong>/soil.tif</td> <td>Soil classification raster subset.</td> </tr> <tr> <td> </td> <td><strong>PCS</strong>/geology.tif</td> <td>Geology classification raster subset.</td> </tr> <tr> <td> </td> <td><strong>Figure</strong></td> <td>The figures during pre-processing.</td> </tr> <tr> <td> </td> <td><strong>Figure</strong>/ETV_watershed_delineation</td> <td>Watershed delineation, include boundary, river, pourpoint.</td> </tr> <tr> <td> </td> <td><strong>Figure</strong>/dem_buf</td> <td>The DEM, and buffer zone.</td> </tr> <tr> <td> </td> <td><strong>Figure</strong>/ETV_LDAS</td> <td>The coverage of reanalysis grid.</td> </tr> <tr> <td> </td> <td><strong>Figure</strong>/ETV_Landuse</td> <td>The landuse classification of the research area</td> </tr> <tr> <td> </td> <td><strong>Figure</strong>/ETV_Soil</td> <td>The soil classification of the research area</td> </tr> <tr> <td> </td> <td><strong>Figure</strong>/ETV_Geol</td> <td>The geology classification of the research area</td> </tr> <tr> <td>Modeling</td> <td>-</td> <td> </td> </tr> <tr> <td> </td> <td><strong>Figure</strong></td> <td>The figures during model deployment</td> </tr> <tr> <td> </td> <td><strong>input</strong></td> <td>Model input folder.</td> </tr> <tr> <td> </td> <td>GCS</td> <td>Spatial data for model deployment in GCS.</td> </tr> <tr> <td> </td> <td>PCS</td> <td>Spatial data for model deployment in PCS.</td> </tr> <tr> <td> </td> <td>deployConfig.txt</td> <td>The configuration file for model deployment script.</td> </tr> <tr> <td>StaticFiles</td> <td>-</td> <td>Static files, include the model, citation, description and executable model file.</td> </tr> <tr> <td> </td> <td><strong>SHUD_model</strong></td> <td>Source code of SHUD model</td> </tr> <tr> <td> </td> <td>Citation.bib</td> <td>Citation of Data and models.</td> </tr> <tr> <td> </td> <td>ReadMe_cn.html</td> <td>The readme file in Chinese.</td> </tr> <tr> <td> </td> <td>ReadMe_en.html</td> <td>The readme file in English.</td> </tr> <tr> <td> </td> <td>shud.exe</td> <td>The executable files of SHUD model, for Windows platform only.</td> </tr> <tr> <td>UserData</td> <td>-</td> <td>The files uploaded by user</td> </tr> </tbody> </table>
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 for: Multi-campaign ship and aircraft observations of marine cloud condensation nuclei, and droplet concentrations
<p class="MsoNormal"><span>In-situ marine cloud droplet number concentrations (CDNCs), cloud condensation nuclei (CCN), and CCN proxies, based on particle sizes and optical properties, are accumulated from seven field campaigns, ACTIVATE, NAAMES, CAMP2EX, ORACLES, SOCRATES, MARCUS, and CAPRICORN2. Each campaign involves aircraft measurements, ship-based measurements, or both. Measurements are collected over the North and Central Atlantic, Indo-Pacific, and Southern Oceans, representing a range of clean to polluted conditions in various climate regimes. With the large range of environmental conditions sampled, this collection of data is ideal for testing satellite remote detection methods of CDNC and CCN in marine environment. Remote measurement methods are key to expanding the available data, in these difficult to reach regions of the Earth, and improving our understanding of aerosol-cloud interactions. Additional particle composition and continental tracers are included to identify potential contributing CCN source. Several of these campaigns, include both High Spectral Resolution Lidar and polarimetric imaging measurements that will be the basis for the next generation of space-based remote sensors and, thus, can be utilized as satellite surrogates.</span></p>
Decaying Turbulence in Molecular Clouds
<p><strong>Introduction</strong></p> <p>This data release contains a suite of 15 AREPO simulations of a spherical molecular clouds of mass <span class="math-tex">\(M=10^4\text{M}_\odot\)</span> and radius <span class="math-tex">\(R \approx 9\text{pc}\)</span> seeded with different types of decaying turbulence. The initial conditions for each simulation are different, with different turbulent modes: mixed turbulence, purely compressive, and purely solenoidal; and different Virial ratios: gravitationally underbound and overbound clouds.</p> <ul> </ul> <p><strong>Simulations</strong></p> <p>Each of the .zip archives contains a simulation. The simulation name is as follows: <em><Turbulent mode><RNG seed><Virial ratio></em> where:</p> <ul> <li>Turbulent mode can be: M = mixed, C = compressive, S = solenoidal.</li> <li>RNG seed used to generate the turbulence can be: 26, 57 and 90.</li> <li>The Virial ratio can be: <em>none</em> = Virial equilibrium (<span class="math-tex">\(\alpha_\text{vir}=1.0 \)</span>), O = overbound (<span class="math-tex">\(\alpha_\text{vir}=0.6\)</span>), U = underbound (<span class="math-tex">\(\alpha_\text{vir}=2.0\)</span>).</li> </ul> <p>Each simulation consists of:</p> <ul> <li>Four simulation snapshots, which are AREPO binary files, corresponding to the four epochs of interest: when total mass in sinks/stars is <span class="math-tex">\(250\text{M}_\odot\)</span>, <span class="math-tex">\(500\text{M}_\odot\)</span>, <span class="math-tex">\(750\text{M}_\odot\)</span>, <span class="math-tex">\(1000\text{M}_\odot\)</span>.</li> <li>Four density grids, corresponding to the four snapshots, which are ray-casted from the AREPO simulations.</li> <li>Four filament networks identified in the density grids using the tool DisPerSE.</li> </ul> <p><strong>Reading in the files</strong></p> <p>The simulations are easily read and analyzed using the code released as part of our study, called <span class="math-tex">\(\texttt{fiesta}\)</span>: <a href="http://fiesta-astro.readthedocs.io">fiesta-astro.readthedocs.io</a>.</p> <p><strong>Supplementary content</strong></p> <p>The data release also contains two animations: <a href="https://zenodo.org/api/files/8b207605-0b06-4a19-96ee-52a949578c77/2D_temporal_animation.mp4?versionId=40527056-ee88-4d31-91ca-5f0ead23557c">2D_temporal_animation.mp4</a> and <a href="https://zenodo.org/api/files/8b207605-0b06-4a19-96ee-52a949578c77/3D_sinkepoch_animation.mp4?versionId=5dbe181d-8963-4536-8667-938fda0ddcda">3D_sinkepoch_animation.mp4</a>, which are useful for visualizing the simulations.</p>
Illuminating the Hierarchical Segmentation of Faults through an Unsupervised Learning Approach applied to clouds of earthquake hypocenters [Dataset]
<p>Data repository to the preprint “Illuminating the Hierarchical Segmentation of Faults through an Unsupersived Larning Approach applied to clouds of earthquake hypocenters” by Piegari et al. (2023), including the datasets for the three analyzed earthquake catalogs.</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>
Replication Package for: Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud
<h2>Replication Package for: Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud</h2><p>This is our replication package for our study on <i>Benchmarking scalability of stream processing frameworks deployed as microservices in the cloud</i>.</p><p>All scalability experiments are performed with the scalability benchmarking framework <a href="https://www.theodolite.rocks/">Theodolite</a> at <a href="https://www.se.informatik.uni-kiel.de/en/research/software-performance-engineering-lab-spel">Kiel University's Software Performance Engineering Lab (SPEL)</a> or Google Cloud.</p><p>With this replication package, we provide:</p><ul><li><a href="https://www.theodolite.rocks/concepts/benchmarks-and-executions.html">Benchmark execution files</a> in <i>executions</i>,</li><li>our benchmark (raw) results in <i>results</i>, and</li><li>analysis script for our results in <i>analysis</i>.</li></ul><h3>Repeating Benchmark Executions</h3><p>All our Theodolite executions are tailored to either the SPEL cluster or the Google Cloud.</p><h4>Kiel University's Software Performance Engineering Lab (SPEL)</h4><p>The SPEL cluster consists of 5 nodes, named <i>kube1-1</i> to <i>kube1-5</i> and labeled with <i>env=dev</i>. To run them in your local cluster, make sure to provide the same infrastructure or rename node selectors in the execution files accordingly.</p><p>To install Theodolite, run:</p><blockquote><p>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f se-cluster-dev.yaml</p></blockquote><p>or for the vertical scalability experiment:</p><blockquote><p>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f se-cluster-dev.yaml -f se-cluster-dev-vertical.yaml</p></blockquote><p>See <a href="https://www.theodolite.rocks">Theodolite's documentation</a> for further usage instructions.</p><h4>Google Cloud</h4><p>In the public cloud baseline experiments, the cluster consists of 5 e2-standard-32 nodes.</p><p>To install Theodolite, run:</p><blockquote><p>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f gcp-cluster-dev.yaml</p></blockquote><p>For the experiments testing higher load intensities, the cluster consists of 4 e2-standard-16 nodes labeled with <i>type=infra</i> and 4 or 8 e2-standard-16 nodes with label <i>type=sut</i>. To install Theodolite in this cluster, run:</p><blockquote><p>helm install theodolite theodolite/theodolite --version 0.8.6 -f https://raw.githubusercontent.com/cau-se/theodolite/main/helm/preconfigs/extended-metrics.yaml -f gcp-cluster-stress.yaml</p></blockquote><p>In both cases, change the maximum load generated per load generator instance:</p><blockquote><p># Generate max. 100000 rec/sec per load generator instance export MAX_RECORDS_PER_INSTANCE=100000 kubectl patch benchmarks uc1-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc1-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc2-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc3-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-beam-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-beam-samza --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-flink --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-hazelcastjet --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]" kubectl patch benchmarks uc4-kstreams --type json --patch "[{op: replace, path: /spec/loadTypes/0/patchers/1/properties/loadGenMaxRecords, value: $MAX_RECORDS_PER_INSTANCE}]"</p></blockquote><p>See <a href="https://www.theodolite.rocks">Theodolite's documentation</a> for further usage instructions.</p><h3>Repeating Results Analysis</h3><p>To inspect, repeat, or extend our results analysis, see <i>results</i> or run the corresponding notebooks in <i>analysis</i>.</p><p>For analyzing and visualizing benchmark results, either Docker or a Jupyter installation with Python 3.7 or 3.8 is required (e.g., in a virtual environment). Moreover, we require some Python libraries, which can be installed by:</p><blockquote><p>python3.8 -m venv .venv # source .venv/bin/activate pip install -r analysis/requirements.txt</p></blockquote><p> </p>
Cloud Feedbacks in Radiative-Convective Equilibrium
<p>Derived data and scripts used in Stauffer and Wing (2023).</p> <p>The standardized RCEMIP output, including time and domain mean profiles, is hosted by the German Climate Computing Center (DKRZ) and is publicly available at <a href="http://hdl.handle.net/21.14101/d4beee8e-6996-453e-bbd1-ff53b6874c0e">http://hdl.handle.net/21.14101/d4beee8e-6996-453e-bbd1-ff53b6874c0e</a>.</p> <p>Descriptions of each of the data files and the variables contained within them are discussed in a README file. The jupyter notebook contains the scripts used to plot the figures used in Stauffer and Wing (2023).</p>
Reduction in the Tropical High Cloud Fraction in Response to an Indirect Weakening of the Hadley Cell
<p><strong>NetCDF files for figures in "Reduction in the Tropical High Cloud Fraction in Response to an Indirect Weakening of the Hadley Cell" submitted to Journal of Advances in Modeling Earth Systems.</strong></p> <p><strong>Figure 1: Prescribed SST for control and perturbed simulations: </strong></p> <ul> <li>sst_ctrl_annualzonalmeans.nc</li> <li>sst_pert_annualzonalmeans.nc</li> </ul> <p><strong>Figure 2: Mass meridional stream function data for control and perturbed simulations:</strong></p> <p><strong><em>Figure 2a: PC2 cloud scheme + Gregory-Rowntree mass-flux convection scheme</em></strong></p> <ul> <li>mmsf_pc2gr_ctrl_annualmeans.nc</li> <li>mmsf_pc2gr_pert_annualmeans.nc</li> </ul> <p><strong><em>Figure 2b: Smith cloud scheme + Gregory-Rowntree mass-flux convection scheme</em></strong></p> <ul> <li>mmsf_smithgr_ctrl_annualmeans.nc</li> <li>mmsf_smithgr_pert_annualmeans.nc</li> </ul> <p><strong>Figure 3a: Ice cloud fraction data for control and perturbed simulations with PC2 cloud scheme (zonal and time-mean over 15 simulation years)</strong></p> <ul> <li>Cf_pc2gr_ctrl_annualzonalmeans.nc</li> <li>Cf_pc2gr_pert_annualzonalmeans.nc</li> </ul> <p><strong>Figure 3b: Ice cloud fraction data for control and perturbed simulations with Smith cloud scheme (zonal and time-mean over 15 simulation years)</strong></p> <ul> <li>Cf_smithgr_ctrl_annualzonalmeans.nc</li> <li>Cf_smithgr_pert_annualzonalmeans.nc</li> </ul> <p><strong>Figure 3c: Relative humidity data for control and perturbed simulations with PC2 cloud scheme (zonal and time-mean over 15 simulation years)</strong></p> <ul> <li>RH_pc2gr_ctrl_annualzonalmeans.nc</li> <li>RH_pc2gr_pert_annualzonalmeans.nc</li> </ul> <p><strong>Figure 3d: Relative humidity for control and perturbed simulations with Smith cloud scheme (zonal and time-mean over 15 simulation years)</strong></p> <ul> <li>RH_smithgr_ctrl_annualzonalmeans.nc</li> <li>RH_smithgr_pert_annualzonalmeans.nc</li> </ul> <p><strong>Figure 3e: Specific humidity data for control and perturbed simulations with PC2 cloud scheme (zonal and time-mean over 15 simulation years)</strong></p> <ul> <li>q_pc2gr_ctrl_annualzonalmeans.nc</li> <li>q_pc2gr_pert_annualzonalmeans.nc</li> </ul> <p><strong>Figure 3f: Specific humidity data for control and perturbed simulations with Smith cloud scheme (zonal and time-mean over 15 simulation years)</strong></p> <ul> <li>q_smithgr_ctrl_annualzonalmeans.nc</li> <li>q_smithgr_pert_annualzonalmeans.nc</li> </ul> <p><strong>Figure 3g: Temperature data for control and perturbed simulations with PC2 cloud scheme (zonal and time-mean over 15 simulation years)</strong></p> <ul> <li>T_pc2gr_ctrl_annualzonalmeans.nc</li> <li>T_pc2gr_pert_annualzonalmeans.nc</li> </ul> <p><strong>Figure 3h: Temperature data for control and perturbed simulations with Smith cloud scheme (zonal and time-mean over 15 simulation years)</strong></p> <ul> <li>T_smithgr_ctrl_annualzonalmeans.nc</li> <li>T_smithgr_pert_annualzonalmeans.nc</li> </ul> <p><strong> Figure 5: Control and perturbed simulation data for ice cloud fraction and mass fraction of ice cloud condensates meaned over the tropical ascent region for PC2 scheme</strong></p> <ul> <li>Cf_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>Cf_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>qcf_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>qcf_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> </ul> <p><strong> Figure 6a,b: Monthly-mean data of convective mass flux for control and perturbed simulations over the tropics between <span class="math-tex">\(\pm\)</span> 31 <span class="math-tex">\(^\circ\)</span>N for PC2 cloud scheme</strong></p> <ul> <li>convmassflux_pc2gr_ctrl_monthlymean_15yrs_tropics.nc</li> <li>convmassflux_pc2gr_pert_monthlymean_15yrs_tropics.nc</li> </ul> <p><strong> Figure 6c,d: Monthly-mean data of net convective detrainment for control and perturbed simulations over the tropics between <span class="math-tex">\(\pm\)</span> 31 <span class="math-tex">\(^\circ\)</span>N for PC2 cloud scheme</strong></p> <ul> <li>netconvdetrain_pc2gr_ctrl_monthlymean_15yrs_tropics.nc</li> <li>netconvdetrain_pc2gr_pert_monthlymean_15yrs_tropics.nc</li> </ul> <p><strong> Figure 6e,f: Monthly-mean data of tendency of ice cloud condensates due to convection for control and perturbed simulations over the tropics between <span class="math-tex">\(\pm\)</span> 31 <span class="math-tex">\(^\circ\)</span>N for PC2 cloud scheme</strong></p> <ul> <li>dqcfdtconv_pc2gr_ctrl_monthlymean_15yrs_tropics.nc</li> <li>dqcfdtconv_pc2gr_pert_monthlymean_15yrs_tropics.nc</li> </ul> <p><strong> Figure 7: Annual and zonal-mean data of net convective detrainment for control and perturbed simulations over the tropical ascent regoin between <span class="math-tex">\(\pm\)</span> 7 <span class="math-tex">\(^\circ\)</span>N for PC2 cloud scheme</strong></p> <ul> <li>netconvdetrain_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>netconvdetrain_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> </ul> <p><strong> Figure 8: Annual and zonal-mean data of net convective detrainment and ice cloud fraction for control and perturbed simulations over the tropical ascent regoin between <span class="math-tex">\(\pm\)</span> 7 <span class="math-tex">\(^\circ\)</span>N for PC2 cloud scheme</strong></p> <ul> <li>netconvdetrain_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>netconvdetrain_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>Cf_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>Cf_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> </ul> <p><strong> Figure 9: Annual and zonal-mean data of convective tendency of mass fraction of ice cloud condensates for control and perturbed simulations over the tropical ascent regoin between <span class="math-tex">\(\pm\)</span> 7 <span class="math-tex">\(^\circ\)</span>N for PC2 cloud scheme</strong></p> <ul> <li>dqcfdtconv_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>dqcfdtconv_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> </ul> <p><strong> Figure 10: Annual and zonal-mean data of net depositional tendency of mass fraction of ice cloud condensates for control and perturbed simulations over the tropical ascent regoin between <span class="math-tex">\(\pm\)</span> 7 <span class="math-tex">\(^\circ\)</span>N for PC2 cloud scheme</strong></p> <ul> <li>dqcfdtnetdep_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>dqcfdtnetdep_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> </ul> <p><strong> Figure 11: Monthly-mean data of net depositional tendency of mass fraction of ice cloud condensates for control and perturbed simulations over the tropics between <span class="math-tex">\(\pm\)</span> 31<span class="math-tex">\(^\circ\)</span>N for PC2 cloud scheme</strong></p> <ul> <li>dqcfdtnetdep_pc2gr_ctrl_monthlymean_15yrs_tropics.nc</li> <li>dqcfdtnetdep_pc2gr_pert_monthlymean_15yrs_tropics.nc</li> </ul> <p><strong> Figure 12: Annual and zonal-mean data of specific humidity and advective tendency for specific humidity for control and perturbed simulations over the tropical ascent regoin between <span class="math-tex">\(\pm\)</span> 7 <span class="math-tex">\(^\circ\)</span>N for PC2 cloud scheme</strong></p> <ul> <li>q_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>q_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>dqdtadv_pc2gr_ctrl_annualzonalmeans_15yrs_tropicalascent.nc</li> <li>dqdtadv_pc2gr_pert_annualzonalmeans_15yrs_tropicalascent.nc</li> </ul> <p><strong>Figure 13: Control and perturbed simulation data for mean vertical advection of moisture, mean vertical moisture convergence, total advection of moisture, and total moisture convergence (meaned over the tropical ascent region for PC2 scheme for 15 simulation years)</strong></p> <ul> <li>MeanVerticalAdvectionOfMoisture_pc2gr_ctrl_15yrmean_tropicalascent_vertprof.nc</li> <li>MeanVerticalAdvectionOfMoisture_pc2gr_pert_15yrmean_tropicalascent_vertprof.nc</li> <li>MeanVerticalMoistureConvergence_pc2gr_ctrl_15yrmean_tropicalascent_vertprof.nc</li> <li>MeanVerticalMoistureConvergence_pc2gr_pert_15yrmean_tropicalascent_vertprof.nc</li> <li>TotalMoistureAdvection_pc2gr_ctrl_15yrmean_tropicalascent_vertprof.nc</li> <li>TotalMoistureAdvection_pc2gr_pert_15yrmean_tropicalascent_vertprof.nc</li> <li>TotalMoistureConvergence_pc2gr_ctrl_15yrmean_tropicalascent_vertprof.nc</li> <li>TotalMoistureConvergence_pc2gr_pert_15yrmean_tropicalascent_vertprof.nc</li> </ul>
UAV-based multispectral image data of a tree nursery, Eberswalde, Brandenburg, 2023 (Orthomosaics, DEMs, point clouds)
<p>This data set contains three multispectral surveys conducted with an unoccupied aerial vehicle (UAV DJI M300 RTK) of an oak tree nursery experiment</p> <ul> <li>Date of acquisition: 16.08.2023</li> <li>Location: Tree nursery, Eberswalde, Brandenburg, Germany</li> <li>UAV: DJI M300 RTK with active SAPOS connection</li> <li>Flight altitude above ground level: 30 m, 40 m, 60 m</li> <li>Image Overlap forward/side: 80 % / 80 %</li> <li>Camera: MicaSense Altum multispectral</li> <li>EPSG: 32632</li> </ul> <p>Data products: </p> <ul> <li>Orthomosaic multispectral (Resolution: 1.35 cm, 1.79 cm, 2.65 cm)</li> <li>Orthomosaic RGB</li> <li>Orthomosaic thermal LWIR in pseudo °C according to <a href="https://support.micasense.com/hc/en-us/articles/360022446473-Converting-Altum-Thermal-to-degrees-C-after-processing-in-Agisoft-or-Pix4D">Micasense</a> empirical formula</li> <li>DEM</li> <li>Dense point cloud RGB</li> <li>Spectral indices calculated: NDVI, NDRE</li> <li>Agisoft Report</li> </ul> <p><strong>Acknowledgment:</strong></p> <p><strong>Frank Becker</strong></p> <p>Landesbetrieb Forst Brandenburg</p> <p>Landeskompetenzzentrum Forst Eberswalde (LFE)</p>
Data for The importance of cloud phase when assessing surface melting in an offline coupled firn model over Ross Ice shelf, West Antarctica
<p>This is the data used in the paper "The importance of cloud phase when assessing surface melting in an offline coupled firn model over Ross Ice shelf, West Antarctica"</p>
Replication data for: Effect of Regional Marine Cloud Brightening Interventions on Climate Tipping Points
<p>Data for reproduction of Hirasawa, H., Hingmire, D., Singh, H., Rasch, P. J., & Mitra, P. (2023). Effect of regional marine cloud brightening interventions on climate tipping elements. Geophysical Research Letters, 50, e2023GL104314. https://doi.org/10.1029/2023GL104314</p> <p>Includes:</p> <ul> <li>Raw monthly 2m temperature (TREFHT) and precipitation (PRECT) data from CESM2 MCB simulations.</li> <li>Ensemble mean data from CESM2 Large Ensemble</li> <li>Tipping point metric timeseries from CESM2 LE Historical and SSP2-4.5 and CESM2 MCB simulations.</li> <li>Jupyter notebook displaying plotting script</li> <li>Scripts showing procedure for computing tipping point metrics</li> <li>CAM6 SourceMod changes to apply cloud droplet number concentration perturbations</li> <li>Top of atmosphere long and shortwave anomalies from fixed sea surface temperature simulations for computing effective radiative forcing</li> </ul>
CCClim - A machine-learning powered cloud class climatology
<p>CCClim is based on cloud property retrievals from the European Space Agency's (ESA) Cloud\_cci dataset, adding relative occurrences of eight major cloud types as defined by the World Meteorological Organization (WMO) at 1° resolution. </p><p>The cloud types are predicted using a two stage machine learning framework, in which a 1 km pixel-level classifier is followed up with a grid-box scale Random Forest regression model.</p><p>CCClim's global coverage being almost gapless from 1982 to 2016 allows for performing process-oriented analyses of clouds on a climatological time scale. Similarly, the moderate spatial and temporal resolutions make it a lightweight dataset while enabling straightforward comparison to climate models.</p><p>The compressed tarball contains 35 netCDF files, each covering one calendar year. Each file provides daily averages of nine cloud-related variables and the nine classes (eight cloud types+undetermined) as per 1° grid box fractional amounts.</p><p>Cloud-related variables:</p><ul><li>cloud water path</li><li>ice water path</li><li>liquid water path</li><li>cloud optical depth</li><li>effective liquid droplet radius at cloud top</li><li>effective ice particle radius at cloud top</li><li>cloud top pressure</li><li>surface temperature</li><li>cloud area fraction</li></ul><p>Cloud types:</p><ul><li>Ci: Cirrus/Cirrostratus</li><li>As: Altostratus</li><li>Ac: Altocumulus</li><li>St: Stratus</li><li>Sc: Stratocumulus</li><li>Cu: Cumulus</li><li>Ns: Nimbostratus</li><li>Dc: Deep convective</li></ul>
MODIS Daily Cloud-gap-filled Fractional Snow Cover Dataset of the Asian Water Tower Region (2000-2022)
<p>The Asia Water Tower region, with the Qinghai-Tibet Plateau at its core, is the most widespread region of snow cover on Earth, except for the North and South Poles. The topographic heterogeneity of the Asian Water Tower region is so great that the snow cover is thin and patchy, resulting in a highly time-varying snow cover in the region, and therefore daily-scale fractional snow cover data are urgently needed. This dataset is based on the MODIS surface reflectance product MO/YD09GA product, and the MODIS daily cloud-free fractional snow cover dataset for the Asian Water Tower region from 2000 to 2022 was produced using the MESMA-AGE algorithm and the MSTI algorithm. The high spatial resolution Landsat-8 image was taken as the "ground truth", the RMSE was 0.16, and the MAE was 0.10. This dataset has a time series from 26 February 2000 to 31 December 2022 with a spatial resolution of 0.005°, which can provide quantitative snow cover information on the spatial distribution of snow for mountain hydrological models, land surface models, numerical weather forecasts, etc.</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.