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2,762 results for “Heating”
Data, scripts, and figure of the article: The fasting heat production of broilers is a function of their body composition
<p>This data set contains the data, JMP scripts, and figures of the article titled "The fasting heat production of broilers is a function of their body composition" to be published in the journal Animal - Open Space.</p>
Reference Data Set: Electricity, Heat, and Gas Sector Data for Modeling the German System
<p>This reference data set representing the status quo of the German electricity, heat, and natural gas sectors was compiled within the research project ‘LKD-EU’ (Long-term planning and short-term optimization of the German electricity system within the European framework: Further development of methods and models to analyze the electricity system including the heat and gas sector).</p> <p>While the focus is on the electricity sector, the heat and natural gas sectors are covered as well. With this reference data set, we aim to increase the transparency of energy infrastructure data in Germany. Where not otherwise stated, the data included in this report is given with reference to the year 2015 for Germany. The data set is documented in DIW Data Documentation 92 (see references).</p> <p>The project is a joined effort by the German Institute for Economic Research (DIW Berlin), the Workgroup for Infrastructure Policy (WIP) at Technische Universität Berlin (TUB), the Chair of Energy Economics (EE2) at Technische Universität Dresden (TUD), and the House of Energy Markets & Finance at University of Duisburg-Essen. The project was funded by the German Federal Ministry for Economic Affairs and Energy through the grant ‘LKD-EU’, FKZ 03ET4028A-D.</p>
Physical inconsistencies in the representation of the ocean heat-carbon nexus in simple climate models (Ocean and Climate variables from Simple Climate Models)
<p>This dataset provide global ocean and climate variables from 8 Simple Climate Models used in the study "Physical inconsistencies in the representation of the ocean heat-carbon nexus in simple climate models"</p>
3D output of idealized large-eddy simulations with varying speed and surface heating to assess Doppler lidar scan patterns
<p><span>This dataset consists of nine idealized large-eddy simulations that were designed to systematically investigate the ability of different Doppler lidar scan patterns to measure the 3-dimensional wind vector at one point or in one profile. For more information, please see the documentation.</span></p>
Multifunctional Polymer Composites for Automatable Induction Heating with Subsequent Temperature Verification
<p>This data upload contains the metadata and datasets underlying the manuscript: "Multifunctional Polymer Composites for Automatable Induction Heating with Subsequent Temperature Verification".</p> <p>A description of the uploaded data is found in the README.txt.</p>
Data used in the manuscript entitled "Turbulent heat flux dynamics along the Dotson and Getz ice-shelf fronts (Amundsen Sea, Antarctica)"
<p>Data files used in the analysis in the manuscript entitled "Turbulent heat flux dynamics along the Dotson and Getz ice-shelf fronts (Amundsen Sea, Antarctica)".</p> <p>Data were collected during the RV NB Palmer NBP2202 cruise, during the 2022 TARSAN campagine in the Amundsen Sea.</p> <p>Underway data provides daily files from the underway and meteorology sensors in JGOFS format. CTD data collected from the cruise. Information about sensors and data formats is included in the data report.</p> <p>Glider data was processed through the UEA Seaglider Toolbox (https://bitbucket.org/bastienqueste/uea-seaglider-toolbox/src/toolbox/) and is provided in Matlab format.</p> <p> </p> <p>Manuscript abstract:</p> <p>In coastal polynyas, where sea–ice formation occurs, it is crucial to have accurate estimates of heat fluxes in order to predict future rates of sea–ice formation. The Amundsen Sea Polynya is the fourth largest coastal polynya around Antarctica, yet remains poorly observed because of its remoteness. Consequently, we rely on models and reanalysis that are unvalidated to study the effect of atmospheric forcing on polynya dynamics. We use summer ship-board data from the NBP22/02 cruise to understand the turbulent heat flux dynamics in the Amundsen Sea Polynya and evaluate our ability to represent these dynamics in ERA5. We show that cold and dry air outbreaks from Antarctica enhance air–sea temperature and humidity gradients, triggering episodic heat loss events. The heat loss is larger along the ice shelves, and it is also where the ERA5 turbulent heat flux exhibits the largest biases, underestimating the flux by up to 141~W~m$^{-2}$ due to its coarse resolution and misrepresentation of ice-shelf location. By reconstructing a turbulent heat flux product from ERA5 variables using a nearest neighbour approach to obtain sea surface temperature, we decrease the bias to 107 W m$^{-2}$. Using a 1D-model, we show that the mean co-located ERA5 heat loss underestimation of -28~W~m$^{-2}$ led to an overestimation of the summer evolution of sea surface temperature (heat content) by +0.76~°C (+8.2e+07~J) over 35-days. By obtaining the reconstructed flux, the reduced heat loss bias (12 W~m$^{-2}$) reduced the seasonal bias in sea surface temperature (heat content) to -0.17~°C (-3.30e+07~J) over the 35-days. This study shows that caution should be applied when retrieving ERA5 turbulent flux along the ice shelves, and that a reconstructed flux using ERA5 variables shows better accuracy.</p> <p> </p> <p> </p>
Estimation of the variation in specific discharge over large depth using Distributed Temperature Sensing (DTS) measurements of the heat pulse response
<p>The data contains measurements and derived values that are used for the manuscript "Estimation of the variation in specific discharge over large depth using Distributed Temperature Sensing (DTS) measurements of the heat pulse response, [Paper # 2018WR024171]" Currently under review at the Water Resources Research journal.</p> <p>The data is stored in netCDF files with xarray (Python), and should be readable with any other netCDF reader. </p> <ul> <li>TEMP is the measured temperature in degrees Celsius relative to the background temperature</li> <li>tempinfty is one of the calibration parameters. Represents the steady state temperature increase</li> <li>A is one of the calibration parameters. Represents the timescale in days</li> <li>b is one of the calibration parameters. Represents the scaled distance to the heat source</li> <li>err_alpha is one of the calibration parameters. Represents the autoregressive parameter</li> <li>TEMPmodel is the best fit temperature response in degrees Celsius relative to the background temperature</li> <li>Innovation is termed the noise in the article, in degrees Celsius.</li> <li>q is the estimated specific discharge in meters per day</li> <li>q_MC_XX are the confidence intervals of the estimated specific discharge calculated with Monte Carlo as presented in the article</li> <li>q_lmfit_XX are the confidence intervals of the estimated specific discharge calculated with LMFIT. Is a rough estimate for q_MC_XX calculated by lmfit (Python package).</li> </ul> <p>Time is measured in days with respect to when the heating cable is turned on.</p> <p>Additionally, a Jupyter notebook is supplemented to the article. It demonstrates the calibration routine and the calculation of the confidence interval for the temperature response at a single depth.</p>
Industry - Chemical & Pharma., Cement, Motor systems, Excess heat recovery systems
<ul> <li>This file provides techno-economic data for energy efficiency measures applicable in Swiss industrial systems.</li> <li>Generalized indicators for measure-specific potential energy savings are not developed (except for cement industry) due to the unavailability of physical production data, ex-ante and ex-post data of the implemented measures and detailed statistics on energy use by application in Swiss industry.</li> <li>When using the data please consult and refer to the publications given in the Reference section.</li> </ul>
Heat load maps at 100m resolution (Linz)
<p>Climate indices (e.g. mean annual number of summer days, hot days, tropical nights) for 30-year historical/future climate periods. The calculation method is based on the cuboid method, a statistical-dynamical downscaling procedure that combines high-resolution (100m) urban climate simulations with long-term climate information from monitoring data/regional climate projections.</p> <p><strong>Climate indices for historical/current periods:</strong> - Background climate information: monitoring data from the airport station Linz Hoersching (1961-2010) - Background climate information: historical (bias-corrected) EURO-CORDEX simulations (1971-2000)</p> <p><strong>Climate indices for future periods:</strong> - Background climate information: bias-corrected EURO-CORDEX model simulations for different representative concentration pathways (2021-2100)</p>
Historical heat wave temperature (Comune di Napoli)
<p>Hazard level of heat waves depending on different base temperatures for the city of Naples.</p>
District heating network data for the city of Flensburg from 2014-2016
<p>The data package contains flow temperatures and the overall heat load for the district heating network of Flensburg, Germany for the years 2014-2016.</p>
Supplementary material to the manuscript: Regionalised Heat Demand and Power-To-Heat Capacities in Germany - An Open Data Set for Assessing Renewable Energy Integration
<p>This is the supplementary material for the manuscript:</p> <p>"Regionalised Heat Demand and Power-To-Heat Capacities in Germany - an Open Data Set for Assessing Renewable Energy Integration"</p> <p>Article DOI: <a href="https://doi.org/10.1016/j.apenergy.2019.114161">https://doi.org/10.1016/j.apenergy.2019.114161</a></p> <p>Open access preprint: <a href="https://arxiv.org/abs/1912.03763">https://arxiv.org/abs/1912.03763</a></p> <p> </p> <p><strong>DESCRIPTION OF THE DATASET AND LICENSES:</strong></p> <p>The subdirectory "04_results" contains the regionalised heat demand an power-to-heat capacity data on administrative district level (NUTS-3) for Germany. The subdirectories "01_census_special_evaluation_data" and "02_other_input_data" contain the utilised input data. The subdirectory "03_code" contains the developed and applied source code.</p> <p>The data in this repository are provided under open source licenses. For license information and other general information on the supplementary material, refer to the LICENSE files and README files in the respective subdirectories.</p> <p>For a detailed description of the approach developed by the author, the input data used and the generated results, refer to the manuscript "Regionalised Heat Demand and Power-To-Heat Capacities in Germany - an Open Data Set for Assessing Renewable Energy Integration".</p> <p><strong>METADATA:</strong></p> <p>Sector: Residential Buildings – Space Heating and Domestic Hot Water</p> <p>Geographical scope: Germany</p> <p>Geographical resolution: Administrative districts (NUTS-3)</p> <p>Temporal scope: 2011, three scenarios for 2030</p> <p>Temporal resolution: 15min</p> <p> </p> <p><strong>UNITS:</strong></p> <p>In the final results folders (04_results/01_installed_heating_p2h_capacity; 04_results/02_daily_time_series; 04_results/03_yearly_time_series) the units of the data are indicated in the file names or the column names, e.g. by "in_MW". In case of unit indication in the file name, the unit refers to all columns in the file.</p> <p>In the intermediate results folder (04_results/00_sql_tables_exported_to_csv) all units referring to power are "kW" and all units referring to energy are "kWh".</p> <p><strong>NEWS AND CONTACT:</strong></p> <p>This dataset will be used as part of the <a href="https://wiki.openmod-initiative.org/wiki/Region4FLEX">region4FLEX model</a>. We are currently enhancing the data by temporally and spatially resolved COP time series and determining load shifting potentials. If you wish to receive news or have general questions please contact: wilko.heitkoetter@dlr.de. </p>
Measurement Dataset of Thermal Fault Emulation of a 46Ah High-Power Kokam Nano Pouch Cell via Uniform and Local Heating
<h1>Preface</h1> <p>This dataset contains experimental data that supplement the article <em>Thermal fault detection by changes in electrical behaviour in lithium-ion cells </em>(<a href="https://doi.org/10.1016/j.jpowsour.2021.229572" target="_blank" rel="noopener">10.1016/j.jpowsour.2021.229572</a>) in the Journal of Power Sources. This dataset extends the already published cell characteristics (see <a href="https://doi.org/10.17632/g443f7cn7p.2" target="_blank" rel="noopener">10.17632/g443f7cn7p.2</a>) by all measured quantities associated with the conducted study. Therefore, the dataset includes sensor readings that have not been described in the before mentioned documents due to space limitations. <em><br></em></p> <p>The published data belongs to the master thesis <em>Development of a model-based method for the early detection of safety-critical heating of lithium-ion cells (transl.), Klink</em> <em>(2020), TU Clausthal</em> that is connected to a study thankfully funded by the European Automobile Manufacturers' Association (ACEA).</p> <h1>Structure</h1> <p>The repository is subdivided in four directories (.zip) based on the content. Within these directories, the individual datasets can be found. While every dataset contains three different file types, the corresponding files can be identified based on the identical filenames. The following file types are provided:</p> <table> <tbody> <tr> <td><strong>File type</strong></td> <td><strong>Content</strong></td> <td><strong>Comment</strong></td> </tr> <tr> <td>*.png</td> <td>Simple graph of the provided data.</td> <td>Missing values are interpolated.</td> </tr> <tr> <td>*.csv</td> <td>Tabular data of the dataset.</td> <td>Columns are separated by ";", the decimal point is ".".</td> </tr> <tr> <td>*.pickle</td> <td>Pickled object of a <a href="https://pandas.pydata.org/docs/index.html" target="_blank" rel="noopener">pandas</a> dataframe (Python) of the data. Preserve index and data types.</td> <td>Pickled with pandas version 2.2.2 using the pickle protocol 5</td> </tr> </tbody> </table> <p>The index and column names of the tabular time series have the following name scheme: X_Y_Z </p> <table> <tbody> <tr> <td><strong>Placeholder</strong></td> <td><strong>Description</strong></td> <td><strong>Example</strong></td> </tr> <tr> <td>X</td> <td>Quantity symbol</td> <td>U for voltage, I for current</td> </tr> <tr> <td>Y</td> <td>[optional] Additional index</td> <td><em>meas </em>for measured quantities</td> </tr> <tr> <td>Z</td> <td>Unit</td> <td>s for seconds, V for volt</td> </tr> </tbody> </table> <h1>Content</h1> <p>The dataset contains the data of both experiments for validation and for investigation of the fault characteristics of the conducted thermal abuse test. While the electrical quantities have been recorded using a battery test stand from Keysight/Scienlab (SL60/200/12BT4C) the temperature readings have been measured by type K thermocouples and recorded with data logger from PCE instruments. For all tests, the temperature sample rate has been set to 1 Hz. Please refer to the attached schematics in <em>SensorPositions.zip</em> for the placement of the individual thermocouples. In addition, T_5 represents the surrounding and T_2 is on the backside of T_1. The sensor positions T_7 and T_8 are added only for the uniform heating where T_7 is located between heating element and cell and T_8 central at the heating plate. Within the referenced article, only T_1 has been used. </p> <p>For details on the experimental setup, please refer to the method section of the linked article. </p> <h2>1. Validation</h2> <table> <tbody> <tr> <td><strong>Description</strong></td> <td> </td> <td>The data contains the electrical load of the cell with an extended WLTC driving cycle that has been scaled to approx. 400 A as well as the corresponding temperature at T_1. The test was conducted within a climatic chamber at 20°C. This data can be used to either parameterize a model of the cell or to validate a model based on other parameter such as the linked parameter set.</td> </tr> <tr> <td><strong>Columns</strong></td> <td>t_s</td> <td>Test time in seconds</td> </tr> <tr> <td> </td> <td>I_meas_A</td> <td>Applied current for WLTC emulation</td> </tr> <tr> <td> </td> <td>U_meas_V</td> <td>Voltage response of cell</td> </tr> <tr> <td> </td> <td>T_meas_C</td> <td>Cell surface temperature</td> </tr> </tbody> </table> <h2>2. ThermalCalibration</h2> <table> <tbody> <tr> <td><strong>Description</strong></td> <td> </td> <td>For each heating setup (uniform, local) this directory contains one data set. Within this experiment, the cell was pulsed with short high current (150 A) pulses to achieve a constant thermal heating power without changing the SOC. Based on the temperature response, a thermal model can be parameterized for both heating setups. Please note, that the electrical sample rate was higher and no interpolation was conducted. </td> </tr> <tr> <td><strong>Columns</strong></td> <td>t_s</td> <td>Test time in seconds</td> </tr> <tr> <td> </td> <td>I_meas_A</td> <td>Applied current</td> </tr> <tr> <td> </td> <td>U_meas_V</td> <td>Voltage response of cell</td> </tr> <tr> <td> </td> <td>T_?_C</td> <td>Temperature reading of sensor ?. See above for description of the individual sensor positions. </td> </tr> </tbody> </table> <h2>3. UniformThermalFault</h2> <table> <tbody> <tr> <td><strong>Description</strong></td> <td> </td> <td>During cycling the cell with a continuous WLTC cycle, the thermal fault was induced by activation of the heating element. After multiple cycles, the cell went into thermal runaway during a charging procedure. Please note, that in the end, the test was disrupted multiple times due to problems induced by the high temperatures. Temperature readings of 9999°C (Upper range) due to sensor failure have been replaced by NaN. Since the heating is started delayed into the second WLTC cycle, the first cycle can be used as reference for normal operation.</td> </tr> <tr> <td><strong>Columns</strong></td> <td>t_s</td> <td>Test time in seconds</td> </tr> <tr> <td> </td> <td>I_meas_A</td> <td>Applied current</td> </tr> <tr> <td> </td> <td>U_meas_V</td> <td>Voltage response of cell</td> </tr> <tr> <td> </td> <td>T_?_C</td> <td>Temperature reading of sensor ?. See above for description of the individual sensor positions. </td> </tr> </tbody> </table> <h2>4. LocalThermalFault</h2> <table> <tbody> <tr> <td><strong>Description</strong></td> <td> </td> <td> <p>During cycling the cell with a continuous WLTC cycle, the thermal fault was induced by activation of the heating element. After multiple cycles, a charging process and observation, no thermal runaway occurred. Please note, that in the end, the test was disrupted multiple times due to problems induced by the high temperatures. It seems that the heat transfer into the cell could have been optimized, as shown by the relatively low cell temperature despite the hot heating element. Nevertheless, this experiment can be used to investigate online detection of small cell changes due to local heating - even without thermal runaway. Since the heating is started delayed into the second WLTC cycle, the first cycle can be used as reference for normal operation.</p> </td> </tr> <tr> <td><strong>Columns</strong></td> <td>t_s</td> <td>Test time in seconds</td> </tr> <tr> <td> </td> <td>I_meas_A</td> <td>Applied current</td> </tr> <tr> <td> </td> <td>U_meas_V</td> <td>Voltage response of cell</td> </tr> <tr> <td> </td> <td>T_?_C</td> <td>Temperature reading of sensor ?. See above for description of the individual sensor positions. </td> </tr> </tbody> </table>
Customizable induction heating profiles: from tailored colloidally stable nanoparticles towards multi-stage heatable supraparticles
<p>This data publication is based on the metadata and datasets underlying the manuscript: "Inductively heatable nano- and supraparticles: from colloidally stable hot nanoparticles to supraparticles with customizable multi-stage heating profiles"</p> <p>Magnetic nanoparticles (NPs) are efficient heat mediators in induction heating. Originally explored for hyperthermia, their applications have broadened to industrial processes where temperature control is crucial. By adjusting the NP composition or morphology, magnetic characteristics such as Curie temperatures can be tailored, allowing control over maximum heating thresholds. These NPs are, however, usually designed for maximum heating rates at specific magnetic fields. In this work, the synthesis is presented for colloidally stable Co and ZnCo ferrite NPs with customizable maximum heating temperatures, and their combination within micron-scaled supraparticles (SPs). Maximum induction heating temperatures of ZnCo ferrite NPs are tuned between 150 and 220 °C, while customization of Co ferrite species yields temperatures between 200 and 350 °C. These distinct magnetic properties are exploited in the selective multi-stage heating of SPs consisting of both species. Here, ZnCo ferrite components heat up to a first temperature plateau at low alternating magnetic fields (AMF), while Co ferrite NPs reach higher temperatures at increased AMF. The precise control of induction heating thresholds through the adaptability of NPs offers a high degree of customizability which makes induction heating particularly attractive for applications requiring sequential or spatial heating, such as catalysis or debonding on demand.</p>
XRDs of Materials used in the Supplementary Information file of A. Lowe et al Exploring the Heat of Water Intrusion ... ACS Appl. Mater. Interfaces 2024, 16, 5286−5293
<p>Data plots were limited to 2theta range from 5 degrees to 50 degrees. CuKa</p>
Codes and Data for 'Cost-effective Planning of Decarbonized Power-Gas Infrastructure to Meet the Challenges of Heating Electrification'
<p>The codes and data used in the followng paper</p> <p>''Khorramfar, R., Santoni-Calvin, M., Mallapragada, D., Amin, S., Botterud, A.,<br>Norfork L., (2025) Cost-effective Planning of Power-Gas Infrastructure to Meet the Challenges<br>of Heating Electrification, Cell Reports Sustainability</p> <p> </p> <p>Link (open source): https://www.cell.com/cell-reports-sustainability/fulltext/S2949-7906(25)00003-5</p> <p> </p>
Vertical cloud radiative heating from the EC-Earth3 PRIMAVERA high-resolution model part 2 (of 2)
<p>The h5 files contain variables used for the article “Vertical structure of cloud radiative heating in the tropics: confronting the EC-Earth v3.3.1/3P model with satellite observations”. The experiment was done with the EC-Earth3 model (https://portal.enes.org/models/earthsystem-models/ec-earth/ec-earth). The model run was done in atmosphere-only mode with prescribed SST for the years 2005 - 2010. Year 2005 and 2006 are considered as spin-up time and are therefore not included. The experiment is described in more detail in the article. </p> <p>The files are named as follows: model-version_resolution_year_month_variable..h5<br> The following variables (named after ECMWF Parameter database) from the model output are included in the dataset:<br> - 107: Top of the atmosphere (TOA) SW radiation (all sky)<br> - 108: TOA SW radiation (clear sky)<br> - 109: TOA SW radiation (cloudy sky)<br> - 110: TOA LW radiation (all sky)<br> - 130: Temperature<br> - 133: Specific humidity<br> - 246: Specific cloud liquid water content<br> - 247: Specific cloud ice water content<br> - 248: Fraction of cloud cover<br> - 54: Pressure</p> <p>The latlon.h5 file contains the latitude and longitude for the dataset. <br> <br> To get the complete dataset for the experiment, see:<br> DOI: 10.5281/zenodo.3958826 for the radiation part of the PRIMAVERA high-resolution data.<br> DOI: 10.5281/zenodo.3960087 for the humidity part of the PRIMAVERA high-resolution data.<br> DOI: 10.5281/zenodo.3947700 for the PRIMAVERA standard-resolution data.<br> DOI: 10.5281/zenodo.3981154 for the V3.3.1 standard-resolution data.<br> DOI: 10.5281/zenodo.4734468 for the python code.</p>
Vertical cloud radiative heating from the EC-Earth3 PRIMAVERA standard-resolution model
<p>The h5 files contain variables used for the article “Vertical structure of cloud radiative heating in the tropics: confronting the EC-Earth v3.3.1/3P model with satellite observations”. The experiment was done with the EC-Earth3 model (https://portal.enes.org/models/earthsystem-models/ec-earth/ec-earth). The model run was done in atmosphere-only mode with prescribed SST for the years 2005 - 2010. Year 2005 and 2006 are considered as spin-up time and are therefore not included. The experiment is described in more detail in the article. </p> <p>The files are named as follows: model-version_resolution_year_month_variable..h5<br> The following variables (named after ECMWF Parameter database) from the model output are included in the dataset:<br> - 107: Top of the atmosphere (TOA) SW radiation (all sky)<br> - 108: TOA SW radiation (clear sky)<br> - 109: TOA SW radiation (cloudy sky)<br> - 110: TOA LW radiation (all sky)<br> - 130: Temperature<br> - 133: Specific humidity<br> - 246: Specific cloud liquid water content<br> - 247: Specific cloud ice water content<br> - 248: Fraction of cloud cover<br> - 54: Pressure</p> <p>The latlon.h5 file contains the latitude and longitude for the dataset. <br> <br> To get the complete dataset for the experiment, see:<br> DOI: 10.5281/zenodo.3958826 for the radiation part of the PRIMAVERA high-resolution data.<br> DOI: 10.5281/zenodo.3960087 for the humidity part of the PRIMAVERA high-resolution data.<br> DOI: 10.5281/zenodo.3947700 for the PRIMAVERA standard-resolution data.<br> DOI: 10.5281/zenodo.3981154 for the V3.3.1 standard-resolution data.<br> DOI: 10.5281/zenodo.4734468 for the python code.</p>
Vertical cloud radiative heating from the EC-Earth3 PRIMAVERA high-resolution model part 1 (of 2)
<p>The h5 files contain variables used for the article “Vertical structure of cloud radiative heating in the tropics: confronting the EC-Earth v3.3.1/3P model with satellite observations”. The experiment was done with the EC-Earth3 model (https://portal.enes.org/models/earthsystem-models/ec-earth/ec-earth). The model run was done in atmosphere-only mode with prescribed SST for the years 2005 - 2010. Year 2005 and 2006 are considered as spin-up time and are therefore not included. The experiment is described in more detail in the article. </p> <p>The files are named as follows: model-version_resolution_year_month_variable..h5<br> The following variables (named after ECMWF Parameter database) from the model output are included in the dataset:<br> - 107: Top of the atmosphere (TOA) SW radiation (all sky)<br> - 108: TOA SW radiation (clear sky)<br> - 109: TOA SW radiation (cloudy sky)<br> - 110: TOA LW radiation (all sky)<br> - 130: Temperature<br> - 133: Specific humidity<br> - 246: Specific cloud liquid water content<br> - 247: Specific cloud ice water content<br> - 248: Fraction of cloud cover<br> - 54: Pressure</p> <p>The latlon.h5 file contains the latitude and longitude for the dataset. <br> <br> To get the complete dataset for the experiment, see:<br> DOI: 10.5281/zenodo.3958826 for the radiation part of the PRIMAVERA high-resolution data.<br> DOI: 10.5281/zenodo.3960087 for the humidity part of the PRIMAVERA high-resolution data.<br> DOI: 10.5281/zenodo.3947700 for the PRIMAVERA standard-resolution data.<br> DOI: 10.5281/zenodo.3981154 for the V3.3.1 standard-resolution data.<br> DOI: 10.5281/zenodo.4734468 for the python code.</p>
Vertical cloud radiative heating from the EC-Earth3 v3.3.1 model.
<p>The h5 files contain variables used for the article “Vertical structure of cloud radiative heating in the tropics: confronting the EC-Earth v3.3.1/3P model with satellite observations”. The experiment was done with the EC-Earth3 model (https://portal.enes.org/models/earthsystem-models/ec-earth/ec-earth). The model run was done in atmosphere-only mode with prescribed SST for the years 2005 - 2010. Year 2005 and 2006 are considered as spin-up time and are therefore not included. The experiment is described in more detail in the article. </p> <p>The files are named as follows: model-version_resolution_year_month_variable..h5<br> The following variables (named after ECMWF Parameter database) from the model output are included in the dataset:<br> - 107: Top of the atmosphere (TOA) SW radiation (all sky)<br> - 108: TOA SW radiation (clear sky)<br> - 109: TOA SW radiation (cloudy sky)<br> - 110: TOA LW radiation (all sky)<br> - 130: Temperature<br> - 133: Specific humidity<br> - 246: Specific cloud liquid water content<br> - 247: Specific cloud ice water content<br> - 248: Fraction of cloud cover<br> - 54: Pressure</p> <p>The latlon.h5 file contains the latitude and longitude for the dataset. <br> <br> To get the complete dataset for the experiment, see:<br> DOI: 10.5281/zenodo.3958826 for the radiation part of the PRIMAVERA high-resolution data.<br> DOI: 10.5281/zenodo.3960087 for the humidity part of the PRIMAVERA high-resolution data.<br> DOI: 10.5281/zenodo.3947700 for the PRIMAVERA standard-resolution data.<br> DOI: 10.5281/zenodo.3981154 for the V3.3.1 standard-resolution data.<br> DOI: 10.5281/zenodo.4734468 for the python code.</p>
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International Brain Laboratory public data
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OpenNeuro
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