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1,028 results for “modelling & simulation”
Modeled tritium in precipitation from Fukushima Daiichi Nuclear Power Plant accident simulations with MIROC5-iso
<p>This data set contains modeled tritium in precipitation values from different simulations of Fukushima Daiichi Nuclear Power Plant (FDNPP) accident produced with MIROC5-iso. The simulations are for the period 2011-20121 and were with different anthropogenic tritium source functions. A complete description can be found in Cauquoin, A., Gusyev, M., Bong, H., Okazaki, A., and Yoshimura, K.: Modeling tritium release to the atmosphere during the Fukushima Daiichi Nuclear Power Plant accident and application to estimating post-accident water system transit times, <em>Environ. Sci. Pollut. Res.</em>, <a href="https://doi.org/10.1007/s11356-025-35919-1" target="_blank" rel="noopener">https://doi.org/10.1007/s11356-025-35919-1</a>, 2025. </p> <p>The simulations are named fukushima_accident_{jra55, era5}_total_gas_{div100, div200, div500, div1000}, with {jra55, era5} describing a nudging to JRA-55 or ERA5 reanalyses, and with {div100, div200, div500, div1000} describing the anthropogenic tritium input function used in DatasetS1_table_tritium_release_atm_fukushima_input.csv.</p> <p>The modeled values of tritium in Hiso river water, Minamisoma spring and artesian groundwater, calculated using MIROC5-iso tritium in monthly precipitation in Fukushima, scaled Tokyo GNIP data, and tritium measurements in preciptation at Fukushima as input of the TracerLPM model, are included too. </p> <p>The model data can be downloaded as netcdf, csv or xlsx files:</p> <ul> <li>*_daymean.prcpTU.nc: daily mean tritium in precipitation over the period 2011-2021, expressed in TU;</li> <li>*_monmean.prcpTU.nc: monthly mean tritium in precipitation over the period 2011-2021, expressed in TU;</li> <li>*_daymean.prcp.nc: daily precipitation over the period 2011-2021, expressed in mm/day;</li> <li>*_monmean.prcp.nc: monthly precipitation over the period 2011-2021, expressed in mm/month;</li> <li>*_prcp_daymean.remapnn.csv: daily precitation at nearest grid cells of Tsukuba, Kashiwa, Hongo, Yokosuka, Konan, and Misasa over the period 2011-2012, expressed in mm/day;</li> <li>*_prcp_monmean.remapnn.csv: montly mean precitation at nearest grid cells of Chiba, Niigata, and Fukushima over the period 2011-2021, expressed in mm/month;</li> <li>*_prcpTU_daymean.remapnn.csv: tritium in daily precitation at nearest grid cells of Tsukuba, Kashiwa, Hongo, Yokosuka, Konan, and Misasa over the period 2011-2012, expressed in TU;</li> <li>*_prcpTU_monmean.remapnn.csv: tritium in montly precitation at nearest grid cells of Chiba, Niigata, and Fukushima over the period 2011-2021, expressed in TU;</li> <li>DatasetS1_table_tritium_release_atm_fukushima_input.csv: Table of anthropogenic tritium daily release, based on reconstructed iodine-131 total gas emissions from <a href="https://doi.org/10.5194/acp-15-1029-2015" target="_blank" rel="noopener">Katata et al. (2015)</a>, used as inputs for MIROC5-iso.</li> <li>TracerLPM_fukushima_with_peak_jra55.xlsx: Tritium input function Cin(t) and tritium concentration in Hiso river water, Minamisoma spring and artesian groundwater modeled by TracerLPM. Simulation div100 nudged to JRA-55 was used for constructing Cin(t).</li> <li>TracerLPM_fukushima_without_peak_jra55.xlsx: Tritium input function Cin(t) and tritium concentration in Hiso river water, Minamisoma spring and artesian groundwater modeled by TracerLPM. Simulation ctrl nudged to JRA-55 (without FDNPP peak) was used for constructing Cin(t).</li> <li>TracerLPM_fukushima_with_peak_era5.xlsx: Tritium input function Cin(t) and tritium concentration in Hiso river water, Minamisoma spring and artesian groundwater modeled by TracerLPM. Simulation div100 nudged to ERA5 was used for constructing Cin(t).</li> </ul>
CTAO Simulation Telescope Models for CORSIKA and sim_telarray - Prod6
<p>The <a href="https://www.cta-observatory.org/">Cherenkov Telescope Array Observatory</a> (CTAO) is the next-generation gamma-ray observatory under construction on the island of La Palma (Spain) and near Paranal (Chile). CTAO will cover a wide energy range and provide substantial improvements in sensitivity, angular resolution and energy resolution in comparison to any existing gamma-ray detector. Detailed Monte Carlo simulations enable the optimization of the instrument configuration and the estimation of observatory performance using realistic models of the telescope design. The CORSIKA air-shower simulation code and the sim_telarray code for simulation of arrays of Cherenkov telescopes are used for all CTAO Monte Carlo simulation productions.</p> <p>This repository provides access to the simulation configuration describing the site parameters and the telescope simulation models required for the generation of the CTAO Instrument Response Functions based on the Prod6 observatory model (to be published).</p> <p>In detail, these are:</p> <ul> <li>telescope simulation models for each telescope type (Prod6 includes models for large-size, medium-size, and small-size telescopes)</li> <li>CORSIKA input files defining (among others) site atmosphere, telescope positions, and interaction models;</li> <li>execution scripts required to run CORSIKA and sim_telarray.</li> </ul> <p>The CORSIKA air-shower simulation code is available from the Institute for Astroparticle Physics (KIT), see <a href="https://www.iap.kit.edu/corsika/index.php" target="_blank" rel="noopener">CORSIKA website</a>. The sim_telarray telescope simulation program, auxilliary libraries, and common configuration files are available at the <a href="https://www.mpi-hd.mpg.de/hfm/~bernlohr/sim_telarray/" target="_blank" rel="noopener">sim_telarray website</a>, with an up-to-date interface to CORSIKA at the <a href="https://www.mpi-hd.mpg.de/hfm/~bernlohr/iact-atmo/" target="_blank" rel="noopener">IACT/ATMO interface website</a>. For installation, copy all relevant tar.gz packages plus the ‘build_all’ script into the same directory and run `./build_all prod6 qgs2` (see `–help` for more options). Note that CORSIKA 7.7550 requires the corsika-77550.patch file available in bernlohr-1.68.tar.gz. Runing without it would lead to run-time errors. Placing the patch file in the top level of the directory structure is enough for the `build_all` script to pick it up.</p> <p>In cases for which the CTAO Simulation Telescope Models are used in a research project, we ask that the following acknowledgement is added in any resulting publication:</p> <p>“This research has made use of the CTAO Simulation Telescope Models provided by the CTAO Central Organisation and Consortium (version prod6 v1.0; [a]).”</p> <p>Please use the following BibTex Entry for [a] in the reference section of your publication: https://zenodo.org/records/14198379/export/bibtex</p> <p>References:</p> <p>[1] K.Bernlöhr, Simulation of imaging atmospheric Cherenkov telescopes with CORSIKA and sim_telarray, Astroparticle Physics 30 (2008) 149–158; <a href="https://arxiv.org/abs/0808.2253" target="_blank" rel="noopener">arXiv:0808.2253</a></p>
Data for: Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and multi-scale material modeling
<h2>Description</h2> <p>DATA REPOSITORY FOR</p> <p>Title: Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and <br> multi-scale material modeling<br>By: Eva Jägle, Jithender J. Timothy, Daniel Jansen, Alisa Machner<br>Accepted by: Cement and Concrete Research</p> <p>This dataset presents the data of the paper 'Deriving early hydration cement paste phase assemblage, microstructure development and elastic properties using thermodynamic simulation and multi-scale material modeling' submitted to and accepted by Cement and Concrete Research. The dataset follows the structure of the paper such that the calculations described therein can be reproduced.</p> <p>Data is available on three types of cement: Two ordinary Portland cements of different grinding fineness (CEM I 42.5 R und CEM I 52.5 R) and one limestone-containing blended cement (CEM II/A-LL 42.5 R). The data refer to the first 24 hours of hydration and temperature conditions of 20°C (for CEM I 42.5 R, CEM I 52.5 R, CEM II/A-LL 42.5 R) and 35°C (for CEM I 52.5 R). All data were retrieved for cement pastes with a water-to-cement ratio of 0.45.</p> <p>The dataset contains raw and processed data from quantitative X-ray diffraction, 5PL cement dissolution fitting, thermodynamic simulation with GEMS, multi-scale material modeling, ultrasonic testing and Vicat penetration tests. The data is mainly available in .xlsx files together with short descriptions in ReadMe.txt files.</p>
Automatic time step adjustment for shortening the runtime of the simulation of marine ecosystem models
<p><strong>Abstract:</strong></p> <p>In investigating the global carbon cycle, shortening the runtime of the simulation of marine ecosystem models is an important issue. More specifically, steady annual cycles mostly are used to assess and validate the models against<br> observational data and to identify relevant biogeochemical processes. Offline simulations based on the transport matrix method already reduce the high computational effort significantly. Furthermore, they facilitate the application<br> of larger time steps in a simple way. In this paper, we present two different methods that automatically adjust the time step during the simulation of a steady state using transport matrices. The algorithms use either an adaptive<br> step size control or decreasing time steps. Their aim is to apply always the time step as large as possible but without any manual selection. We applied the methods for a variety of ecosystem models of different complexity, using Latin<br> hypercube samples of size 100 for the model parameters of each model. We showed that both methods computed an approximation of the steady annual cycle that was of the same accuracy as solutions obtained with a fixed time step. Both algorithms lowered the runtime of the steady annual cycle computation significantly. The performance gain depended on the complexity of the models. Moreover, the adaptive method has a certain overhead that might lead to higher computational cost in special cases.</p> <p><strong>Content:</strong></p> <ul> <li>Tracer concentrations of a reference solution for all parameter vectors and biogeochemical models</li> <li>SQLite database including the results using the decreasing time steps algorithm</li> <li>Tracer concentrations of the results using the decreasing time steps algorithm</li> <li>SQLite database including the results using the step size control algorithm</li> <li>Tracer concentrations of the results using the step size control algorithm</li> </ul>
Soil organic matter and plant carbon allocated to nitrogen acquisition simulated by the FUN-BioCROP model
<p>This data package contains the model input, results, and validation data from Juice et al (citation below). The FUN-BioCROP model (Fixation and Uptake of Nitrogen- Bioenergy Carbon, Rhizosphere, Organisms, and Protection) advances the field of bioenergy modeling by integrating new empirical paradigms of the role of belowground processes in shaping coupled carbon (C) and nitrogen (N) cycles. It was developed by modifying the FUN-CORPSE model (Fixation and Uptake of Nitrogen- Carbon, Organisms, Rhizosphere, and Protection in the Soil Environment, Sulman et al. 2017 Ecology Letters) for use in bioenergy systems by including mechanistic tillage, organic matter addition, nitrogen fertilization, harvest, and feedstock-specific parameters, and to be driven by DayCent plant productivity and biomass data.</p>
Nonlinear macro-model for OpenSEES simulation of composite steel beams
<p>The dataset consist of the following:</p> <p>1- Source_Code.zip</p> <p>This folder includes the subroutines that can be used to implement the macro-model for simulating the hysteretic behavior of composite steel beams. The README.txt file provides a description of each subroutine </p> <p>2- Nonlinear_Models.zip</p> <p>This folder includes the nonlinear building models. The README.txt file explains the procedure to run nonlinear static or dynamic analysis.</p> <p>3- Incremental_Dynamic_Analysis_Results.zip</p> <p>This folder includes the IDA results for each building at three different sites. The README.txt file provides a description of the data.</p> <p>4- Ground_Motion_Sets.zip</p> <p>This folder includes the ground motion records for four different sites at two different return periods: 475 and 2475 years. The site hazard curves are also included. A description of the hazard analysis, disaggregation and record selection is provided in PSHA_Report.doc</p>
CTAO Simulation Telescope Models for CORSIKA and sim_telarray - prod3b
<p>The <a href="https://www.cta-observatory.org/">Cherenkov Telescope Array Observatory</a> (CTAO) will be the next-generation gamma-ray observatory under construction on the island of La Palma (Spain) and near Paranal (Chile). CTAO will cover a wide energy range and provide substantial improvements in sensitivity, angular resolution and energy resolution in comparison to any existing gamma-ray detector. Detailed Monte Carlo simulations allow us to optimise the instrument configuration and to estimate the observatory performance using realistic models of the telescope design. The CORSIKA air-shower simulation code and the sim_telarray code for simulation of arrays of Cherenkov telescopes are used for all CTAO Monte Carlo simulation productions.</p> <p>This repository provides access to the simulation configuration describing the site parameters and the telescope simulation models required for the generation of the <a href="https://doi.org/10.5281/zenodo.5163272">CTAO Instrument Response Functions - prod3b</a>.</p> <p>In detail, these are:</p> <ul> <li>telescope simulation models for each telescope type (prod3b includes models for large-size, medium-size, and small-size telescopes)</li> <li>CORSIKA input files defining (among others) site atmosphere, telescope positions, and interaction models;</li> <li>execution scripts required to run CORSIKA and sim_telarray.</li> </ul> <p>The CORSIKA air-shower simulation code is available from the Institute for Astroparticle Physics (KIT), see <a href="https://www.iap.kit.edu/corsika/index.php">CORSIKA website</a>. The sim_telarray telescope simulation program, auxilliary libraries, and common configuration files are available at the <a href="https://www.mpi-hd.mpg.de/hfm/~bernlohr/sim_telarray/">sim_telarray website</a>, with an up-to-date interface to CORSIKA at the <a href="https://www.mpi-hd.mpg.de/hfm/~bernlohr/iact-atmo/">IACT/ATMO interface website</a>. For installation, copy all relevant tar.gz packages plus the ‘build_all’ script into the same directory and run ‘./build_all prod3-la-palma qgs2' (see ‘--help’ for more options).</p> <p>In cases for which the CTAO Simulation Telescope Models are used in a research project, we ask that the following acknowledgement is added in any resulting publication:</p> <p>“This research has made use of the CTA Simulation Telescope Models provided by the CTA Observatory and Consortium (version prod3b v1.0; [a]).”</p> <p>Please use the following BibTex Entry for [a] in the reference section of your publication: <a href="https://zenodo.org/record/6219128/export/hx">https://zenodo.org/record/6219128/export/hx</a></p> <p>References:</p> <p>[1] K.Bernlöhr, Simulation of imaging atmospheric Cherenkov telescopes with CORSIKA and sim_telarray, Astroparticle Physics 30 (2008) 149–158; <a href="https://arxiv.org/abs/0808.2253">arXiv:0808.2253</a></p> <p>[2] A. Acharyya, for the CTA Consortium (2019), Monte Carlo studies for the optimisation of the Cherenkov Telescope Array layout, <a href="https://arxiv.org/abs/1904.01426">arXiv:1904.01426</a></p>
CTAO Simulation Telescope Models for CORSIKA and sim_telarray - prod5
<p>The <a href="https://www.cta-observatory.org/">Cherenkov Telescope Array Observatory</a> (CTAO) will be the next-generation gamma-ray observatory under construction on the island of La Palma (Spain) and near Paranal (Chile). CTAO will cover a wide energy range and provide substantial improvements in sensitivity, angular resolution and energy resolution in comparison to any existing gamma-ray detector. Detailed Monte Carlo simulations allow us to optimise the instrument configuration and to estimate the observatory performance using realistic models of the telescope design. The CORSIKA air-shower simulation code and the sim_telarray code for simulation of arrays of Cherenkov telescopes are used for all CTAO Monte Carlo simulation productions.</p> <p>This repository provides access to the simulation configuration describing the site parameters and the telescope simulation models required for the generation of the <a href="https://doi.org/10.5281/zenodo.5499839">CTAO Instrument Response Functions - prod5</a>.</p> <p>In detail, these are:</p> <ul> <li>telescope simulation models for each telescope type (prod5 includes models for large-size, medium-size, and small-size telescopes)</li> <li>CORSIKA input files defining (among others) site atmosphere, telescope positions, and interaction models;</li> <li>execution scripts required to run CORSIKA and sim_telarray.</li> </ul> <p>The CORSIKA air-shower simulation code is available from the Institute for Astroparticle Physics (KIT), see <a href="https://www.iap.kit.edu/corsika/index.php">CORSIKA website</a>. The sim_telarray telescope simulation program, auxilliary libraries, and common configuration files are available at the <a href="https://www.mpi-hd.mpg.de/hfm/~bernlohr/sim_telarray/">sim_telarray website</a>, with an up-to-date interface to CORSIKA at the <a href="https://www.mpi-hd.mpg.de/hfm/~bernlohr/iact-atmo/">IACT/ATMO interface website</a>. For installation, copy all relevant tar.gz packages plus the ‘build_all’ script into the same directory and run ‘./build_all prod5 qgs2’ (see ‘--help’ for more options).</p> <p>In cases for which the CTAO Simulation Telescope Models are used in a research project, we ask that the following acknowledgement is added in any resulting publication:</p> <p>“This research has made use of the CTA Simulation Telescope Models provided by the CTA Observatory and Consortium (version prod5 v1.0; [a]).”</p> <p>Please use the following BibTex Entry for [a] in the reference section of your publication: <a href="https://zenodo.org/record/6218687/export/hx">https://zenodo.org/record/6218687/export/hx</a></p> <p>References:</p> <p>[1] K.Bernlöhr, Simulation of imaging atmospheric Cherenkov telescopes with CORSIKA and sim_telarray, Astroparticle Physics 30 (2008) 149–158; <a href="https://arxiv.org/abs/0808.2253">arXiv:0808.2253</a></p> <p>[2] O.Gueta for the CTA Consortium and the CTA Observatory, The Cherenkov Telescope Array: layout, design and performance, Proceedings of the 37th International Cosmic Ray Conference (ICRC2021), Berlin, Germany; <a href="https://arxiv.org/abs/2108.04512">arXiv:2108.04512</a></p>
Simulation Data for "Community-Driven Code Comparisons for Three-Dimensional Dynamic Modeling of Sequences of Earthquakes and Aseismic Slip"
<p>Simulation data from Jiang et al. (2022), "Community-Driven Code Comparisons for Three-Dimensional Dynamic Modeling of Sequences of Earthquakes and Aseismic Slip," <em>Journal of Geophysical Research: Solid Earth</em><em>.</em></p> <p>The archive includes simulation data for 3D SEAS benchmarks BP4-QD and BP5-QD that are analyzed in our paper (descriptions in NOTES.txt) </p> <p><strong>BP4-QD Benchmark Simulations:</strong><br>1000 m: jiang.5, lambert.8, barbot.3, barbot.2, dliu.2, li.4<br>500 m: jiang.3, lambert.3, barbot.5, barbot.7, ozawa</p> <p><strong>BP5-QD Benchmark Simulations:</strong><br>2000 m: jiang.6, lambert.8, liu.4, cattania.5, dli.7, barbot.3, dliu.10, li.3<br>1000 m: jiang.2, lambert.7, liu.5, cattania.3, ozawa, dli.5, barbot, dliu.6, li.2<br>500 m: jiang.4, lambert.9, liu.6, cattania.4, ozawa.2, dli.6, barbot.2, dliu.8<br>250 m: lambert.10, liu.7</p> <p><strong>BP5-QD with Off-Fault Data:</strong><br>1000 m: lambert.7, dli.5, barbot, dliu.6, li.2<br>500 m: lambert.9, dli.6, barbot.2, dliu.8</p> <p>Tables 2–4 in our paper summarizes details of numerical codes and selected simulations.</p> <p>The benchmark descriptions and the full suite of simulation data are available at SEAS online platform https://strike.scec.org/cvws/seas/.</p>
ConFiRMa dataset_01: simulation of CRM characterization tests with the OOFEM code (detailed level modelling)
<p>The Dataset collects the input files developed for the simulation of characterization tests performed on Composite Reinforced Mortar samples with the free open-source code OOFEM (detailed level modelling). The description of the numerical models and the analysis and comparison of the results can be found in paper "Characterization of Textile Reinforced Mortar: state of the art and detailed modelling with a free open source finite element code" (<a href="https://doi.org/10.1061/(ASCE)CC.1943-5614.0001240">https://doi.org/10.1061/(ASCE)CC.1943-5614.0001240</a>).</p> <p>OOFEM Version 2.5 (https://doi.org/10.5281/zenodo.4339630) was used for running the analyzes.</p> <p>ReadMe file provide a description of the different input files.</p> <p> </p>
Archived Model Output for "Simulating Observations of Southern Ocean Clouds and Implications for Climate"
<p>This is an archive of CAM6 simulation output used in the paper Southern Ocean Aerosol and Ice Nucleating Particles in the Community Earth System Model Version 2, submitted to the Journal of Geophysical Research Atmospheres. </p>
ConFiRMa dataset_02: simulation of tests on CRM strengthened masonry elements with the OOFEM code (detailed level modelling)
<p>The Dataset collects the input files developed for the simulation of tests on masonry elements strengthened through Composite Reinforced Mortar with the free open-source code OOFEM (detailed level modelling). The description of the numerical models and the analysis and comparison of the results can be found in paper "Masonry elements strengthened through Textile-Reinforced Mortar:application of the detailed level modelling with a free open-source Finite-Element code".</p> <p>OOFEM Version 2.5 (https://doi.org/10.5281/zenodo.4339630) was used for running the analyzes.</p> <p>ReadMe file provide a description of the different input files.</p>
Intital simulation of Hunga-Tonga volcanic aerosol cloud with the UM-UKCA composition-climate model
<p>This dataset is from a series of “forward projection” interactive stratospheric aerosol simulations of the Jan 2022 Hunga-Tonga volcanic aerosol cloud with the UM-UKCA composition-climate model. The model experiments predict how the cloud will disperse through 2022, and apply the UM-UKCA model at GA4 (Walters et al., 2014), with GLOMAP v8.2, as applied for the “MajorVolc” datasets for Agung, El Chichon and Pinatubo (Dhomse et al., 2020), those runs aligned with the Historical Eruption SO2 emissions Assessment experiment within ISA-MIP (Timmreck et al., 2018).</p> <p>The “standard” Hunga-Tonga GA4 UM-UKCA experiment emits 0.4Tg of SO2 at 29-31km, within a 24-hour period, matching the detrainment duration specified for the ISA-MIP HErSEA experiment protocol. Following the stronger than expected mid-visible backscatter ratios (BSR) measured by CALIOP satellite-borne lidar, and from ground-based lidar from Reunion Island (very high BSR values > 200), we also ran UM-UKCA simulations with “scaled-up Hunga-Tonga SO2 emission”, at 0.8, 1.2 and 1.6 Tg of SO2 emitted.</p> <p>Unexpectedly strong stratospheric AOD observed from the OMPS satellite months after the eruption further strengthens the motivation for these simulations.</p> <p>Several hypotheses for the high AOD from Hunga-Tonga have been suggested:<br> 1) an unusual amount of (or influence from) co-emitted ultra-fine ash particles<br> 2) “in-plume oxidised sulphate” already converted from SO2 at the time of detrainment<br> (e.g. via aqueous-phase oxidation within water droplets within the eruptive plume).<br> 3) co-emitted marine aerosol (e.g. sea-salt aerosol) from seawater vaporized in the plume<br> </p> <p>There are 4 types of netcdf files, Stratospheric AOD (saod), Effective Radius (reff), Extinction (ext) and sulphate aerosol surface area density (sad).</p> <p><br> <br> For e.g. <br> saod550_HT_0pt4Tg_T2Mz-20220101-20230831.nc contains<br> Stratospheric aerosol optical depth (sAOD) at 550nm (2D-monthly dataset vs latitude and time) with 0.4 Tg SO2 injection Jan2022 to August 2023<br> Whereas other files<br> reff_HT_0pt4Tg_T2Mz_20220101-20230831.nc,<br> sad_HT_0pt4Tg_T2Mz_20220101-20230831.nc<br> ext550_HT_0pt4Tg_T2Mz-20220101-20230831.nc</p> <p>contain particle effective radius (reff), aerosol surface area density, aerosol extinction as 3D-monthly fields (altitude, latitude , time) from the same simulation.<br> Other saod and extinction files are also available at 870 and 1020 nm.</p> <p> </p> <p>Note that these are preliminary simulations, hence we do not expect good match with the observations. We plan to perform additional UM-UKCA simulations, comparing to the satellite and ground-based lidar measurements, and to in-situ balloon observations from Reunion Island rapid response campaign & upcoming high-altitude balloon sampling flights in Brazil.</p> <p> </p> <p>References :<br> Dhomse SS, Mann GW, Antuña Marrero JC, Shallcross SE, Chipperfield MP, Carslaw KS, Marshall L, Abraham NL, Johnson CE. 2020. Evaluating the simulated radiative forcings, aerosol properties, and stratospheric warmings from the 1963 Mt Agung, 1982 El Chichón, and 1991 Mt Pinatubo volcanic aerosol clouds. Atmospheric Chemistry and Physics. 20(21), pp. 13627-13654</p> <p><br> Timmreck, C., Mann, G. W., Aquila, V., Hommel, R., Lee, L. A., Schmidt, A., Brühl, C., Carn, S., Chin, M., Dhomse, S. S., Diehl, T., English, J. M., Mills, M. J., Neely, R., Sheng, J., Toohey, M., and Weisenstein, D.: The Interactive Stratospheric Aerosol Model Intercomparison Project (ISA-MIP): motivation and experimental design, Geosci. Model Dev., 11, 25812608, https://doi.org/10.5194/gmd-11-2581-2018, 2018.</p> <p> </p>
Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model : Model Ouput Data
<p>This upload includes data associated with the manuscript "Global agricultural ammonia emissions simulated with the ORCHIDEE land surface model : )" submitted to Geoscientific Model Development. The dataset includes an output file with the simulated ammonia emissions for the agricultural sector.</p> <p>The emissions (manure management and soil), manure production and soil ammonium concentrations are monthly fields from the simulation for 2007-2015.</p> <p>Additional information is given in the readme file</p>
A Practical Tool-Chain for the Development of Coordination Scenarios - Graphical Modeler, DSL, Code Generators and Automaton-Based Simulator
<p>The Peer Model is a modeling tool for coordination based on blackboard-based collaboration. </p> <p>The tool-chain consists of a modeler, translator and simulator.</p> <p>Its goal is to help developers of distributed and concurrent coordination software better understand algorithms and identify deficiencies from the beginning.</p> <p><br> </p>
Large-Eddy Simulation of Wind Turbine Flows: A New Evaluation of Actuator Disk Models - Dataset
<p>Main data used in the following paper: Revaz, T.; Porté-Agel, F. Large-Eddy Simulation of Wind Turbine Flows: A New Evaluation of Actuator Disk Models. <em>Energies</em> <strong>2021</strong>, <em>14</em>, 3745. https://doi.org/10.3390/en14133745</p>
Model codes and simulation data for "Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS's Earth system model (ModelE-BiomeE v.1.0)"
<p>ModelE-BiomeE v1.0 model codes and data This folder contains the simulation data and model codes that were used in the paper ‘Modeling demographic-driven vegetation dynamics and ecosystem biogeochemical cycling in NASA GISS’s Earth system model (ModelE-BiomeE v.1.0)’ (https://doi.org/10.5194/gmd-2022-72). We included the data simulated by ModelE-BiomeE v.1.0 with settings of full demography (folder FullDemography) and single cohort (folder SingleCohort), and initial settings of land grids and vegetation data (folder GlobalVegetation). The codes include the full ModelE 2.1, module BiomeE files in ModelE, and the standalone BiomeE. In the folder FullDemography, we have 4 netcdf files for global output and 25 files for single grids output. The files ‘FullDM_2588_JAN.nc’ and ‘FullDM_2588_JUL.nc’ are the original model output of January and July in the year 2588. The file ‘FullDM_2588_Annual.nc’ is the yearly summary of model simulations. The file ‘FullDM_Selected.nc’ is an annual summary of 588 years of model simulation only with selected variables. The csv files are for single grids output at the time steps of daily and yearly. The last digit 1~8 represents the sites of 'BNC','MNT','HF','OKR','KZ','SV','WGK','TPJ', respectively (Table 1). Table 1 Site ID and file number ['BNC', 'MNT', 'HF', 'OKR', 'KZ', 'SV', 'WGK', 'TPJ'] ['8991', '8992', '8993', '8994', '8995', '8996', '8997', '8998'] ['8971', '8972', '8973', '8974', '8975', '8976', '8977', '8978'] ['8961', '8962', '8963', '8974', '8965', '8966', '8977', '8968'] Please refer to Table 2 in the paper for the detail of these 8 sites. ‘DailyLAIGPP.csv’ is a summary of all ‘DailyEcosystem’ files with LAI and GPP data. We included the Python scripts that can be used to generate the figures in out paper (Plotting-BiomeE-MsTMIP.py, Plotting-Scatter-Comparison.py, PlottingBiomeEMaps.py, and PlottingGridOutput.py). For the convenience of readers (in reproducing our figures), we included the summary of reanalysis of the data from observations and MsTMIP in folder ‘Sum-Obs-Simu’. Please refer to the original sources listed in our paper for the detail of these data.</p>
Processed model output of the climate simulation in the study: The effects of diachronous surface uplift of the European Alps on regional climate and the isotopic composition of precipitation (δ18Op) [Boateng et al.]
<p><strong>The geodynamic evolution of the Alps suggests that the Alps did not rise monotonically due to the different post-collisional processes such as slab break-off. However, understanding such subsurface dynamics would require adequate knowledge about its surface uplift history. Stable isotope paleoaltimetry methods are widely used to infer past surface elevation using geologic archives. However, its accurate interpretation relies on attributing the extracted isotopic signal from proxies to surface uplift despite other influences such as climate. To resolve this issue, topographic sensitivity experiments across the Alps are used to investigate the impacts of the diachronous surface uplift on regional climate and δ18Op. The Atmospheric General Circulation Model ECHAM5 with water isotope tracking capabilities (ECHAM5-wiso) is used to simulate the climate with varied topographic scenarios. We present the processed (long-term means) model output of the relevant climate variables (i.e δ18Op, near-surface temperature, precipitation amount, near-surface meridional and zonal winds, mean sea level pressure, and elevation) in response to the changes in topography. The file names are representative of the topographic scenarios used for the simulations. For example, the file “W2E1.nc” is the model output produced by a topographic scenario in which the topography across the west-central Alps was set to 200% of its modern height, and the Eastern Alps were kept at 100%. The “CTL.nc” file contains model output from the control simulation that uses present-day topography. The datasets for instance can be used to select far-field sampling points for the δ-δ paleoaltimetry method that are not significantly affected by the topographic changes.</strong></p>
Atmospheric Distribution of HCN from Satellite Observations and 3-D Model Simulations - TOMCAT data
<p>This repository contains the model data from the paper "Atmospheric Distribution of HCN from Satellite<br> Observations and 3-D Model Simulations" submitted to ACP.</p> <p>The files contains the monthly mean hydrogen cyanide (HCN) mixing ratios modelled using the TOMCAT 3-D offline chemical transport model with a horizontal resolution of 2.8° × 2.8° with 60 hybrid σ-pressure levels from the surface to ~60 km.</p>
Cuzzone2024: Ice sheet model simulations reveal polythermal ice conditions existed across the NE USA during the Last Glacial Maximum
<p>Here you will find model output associated with Cuzzone et al. (2024) for simulations conducted to reconstruct the Last Glacial Maximum conditions across the Northeast United States. Model output is available as: 1) Simulated Ensemble Mean LGM Ice Thickness, 2.) Simulated Ensemble Mean LGM Velocity 3.) Simulated Ensemble Mean LGM Velocity in X and Y direction, and 4.) The simulated LGM thermal state, shown as the Model ensemble agreement for warm and cold-based ice.</p> <p>These outputs are given for 3 model domains: 1) The Northeast USA (NE Domain), 2) The Adirondack Mountains (ADK), 3) The White Mountains (White), and 4) Mount Katahdin (Kat).</p> <p>Model output is given in .tif format, and the Map Projection is ESPG: 4326 , WGS 84</p> <p>Units for model output is:</p> <p>1) Ice Thickness: meters</p> <p>2) Velocity (vel, vx, vy): meters/yr</p> <p>3) Model Thermal Agreement: -5 to 5</p> <p>-5: All ensemble members agree cold-based ice</p> <p>-4: 4/5 ensemble members agree cold-based ice</p> <p>-3: 3/5 ensemble members agree cold-based ice</p> <p>-2: 2/5 ensemble members agree cold-based ice</p> <p>-1: 1/5 ensemble members agree cold-based ice</p> <p>0: 50% ensemble members either cold or warm-based</p> <p>1: 1/5 ensemble members agree cold-based ice</p> <p>2: 2/5 ensemble members agree cold-based ice</p> <p>3: 3/5 ensemble members agree cold-based ice</p> <p>4: 4/5 ensemble members agree cold-based ice</p> <p>5: 5/5 ensemble members agree cold-based ice</p>
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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.