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1,028 results for “modelling & simulation”
Data from: Long term impacts of selective logging on two Amazonian tree species with contrasting ecological and reproductive characteristics: inferences from Eco-gene model simulations
The impact of logging and subsequent recovery after logging is predicted to vary depending on specific life history traits of the logged species. The Eco-gene simulation model was used to evaluate the long-term impacts of selective logging over 300 years on two contrasting Brazilian Amazon tree species, Dipteryx odorata and Jacaranda copaia. D. odorata (Leguminosae), a slow growing climax tree, occurs at very low densities, whereas J. copaia (Bignoniaceae) is a fast growing pioneer tree that occurs at high densities. Microsatellite multilocus genotypes of the pre-logging populations were used as data inputs for the Eco-gene model and post-logging genetic data was used to verify the output from the simulations. Overall, under current Brazilian forest management regulations, there were neither short nor long-term impacts on J. copaia. By contrast, D. odorata cannot be sustainably logged under current regulations, a sustainable scenario was achieved by increasing the minimum cutting diameter at breast height from 50 to 100 cm over 30-year logging cycles. Genetic parameters were only slightly affected by selective logging, with reductions in the numbers of alleles and single genotypes. In the short term, the loss of alleles seen in J. copaia simulations was the same as in real data, whereas fewer alleles were lost in D. odorata simulations than in the field. The different impacts and periods of recovery for each species support the idea that ecological and genetic information are essential at species, ecological guild or reproductive group levels to help derive sustainable management scenarios for tropical forests.
Data from: Successful by chance? the power of mixed models and neutral simulations for the detection of individual fixed heterogeneity in fitness components
Heterogeneity in fitness components consists of fixed heterogeneity due to latent differences fixed throughout life (e.g. genetic variation), and dynamic heterogeneity generated by stochastic variation. Their relative magnitude is crucial for evolutionary processes, as only the former may allow for adaptation. However, the importance of fixed heterogeneity in small populations has recently been questioned. Using neutral simulations (NS), several studies failed to detect fixed heterogeneity, thus challenging previous results from mixed models (MM). To understand the causes of this discrepancy, we estimate the statistical power and false positive rate of both methods, and apply them to empirical data from a wild rodent population. While MM show high false positive rates if confounding factors are not accounted for, they have high statistical power to detect real fixed heterogeneity. In contrast, NS are also subject to high false positive rates, but have always low power. Indeed, MM analyses of the rodent population data show significant fixed heterogeneity in reproductive success, whereas NS analyses do not. We suggest that fixed heterogeneity may be more common than is suggested by NS, and that NS are useful only if more powerful methods are not applicable and if they are complemented by a power analysis.
Data from: Understanding age-specific dispersal in fishes through hydrodynamic modelling, genetic simulations and microsatellite DNA analysis
Many marine species have vastly different capacities for dispersal during larval, juvenile and adult life stages, and this has the potential to complicate the identification of population boundaries and the implementation of effective management strategies such as marine protected areas. Genetic studies of population structure and dispersal rarely disentangle these differences and usually provide only lifetime-averaged information that can be considered by managers. We address this limitation by combining age-specific autocorrelation analysis of microsatellite genotypes, hydrodynamic modelling and genetic simulations to reveal changes in the extent of dispersal during the lifetime of a marine fish. We focus on an exploited coral reef species, Lethrinus nebulosus, which has a circum-tropical distribution and is a key component of a multispecies fishery in northwestern Australia. Conventional population genetic analyses revealed extensive gene flow in this species over vast distances (up to 1500 km). Yet, when realistic adult dispersal behaviours were modelled, they could not account for these observations, implying adult dispersal does not dominate gene flow. Instead, hydrodynamic modelling showed that larval L. nebulosus are likely to be transported hundreds of kilometres, easily accounting for the observed gene flow. Despite the vast scale of larval transport, juvenile L. nebulosus exhibited fine-scale genetic autocorrelation, which declined with age. This implies both larval cohesion and extremely limited juvenile dispersal prior to maturity. The multidisciplinary approach adopted in this study provides a uniquely comprehensive insight into spatial processes in this marine fish.
Simulation data for WRF-GC (v2.0): online two-way coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.7.2) for modeling regional atmospheric chemistry–meteorology interactions
<p>This repository provides the test simulation data for "WRF-GC (v2.0): online two-way coupling of WRF (v3.9.1.1) and GEOS-Chem (v12.7.2) for modeling regional atmospheric chemistry–meteorology interactions" published in Geoscientific Model Development. The configurations for sensitivity experiments are described in this paper. Please contact the corresponding author Tzung-May Fu (fuzm@sustech.edu.cn) for more details.</p>
MD simulation trajectory and related files for POPC bilayer with 340mM NaCl (Berger model delivered by Tieleman, ffgmx ions, Gromacs 4.5)
<p>Equilibrated POPC lipid bilayer simulation ran with Gromacs 4.5, Berger force field delivered by Peter Tieleman (http://wcm.ucalgary.ca/tieleman/downloads), double bonds updated in http://dx.doi.org/10.1021/jp065424f, ffgmx parameters for ions, 50ns, T=298K, 128 POPC molecules, 7202 water molecules, 44 Na molecules, 44 Cl molecules. This data is used in the NMRLipids II project (nmrlipids.blospot.fi).</p>
MD simulation trajectory and related files for POPC bilayer with 340mM CaCl_2 (Berger model delivered by Tieleman, ffgmx ions, Gromacs 4.5)
<p>Equilibrated POPC lipid bilayer simulation ran with Gromacs 4.5, Berger force field delivered by Peter Tieleman (http://wcm.ucalgary.ca/tieleman/downloads) double bonds updated in http://dx.doi.org/10.1021/jp065424f, ffgmx parameters for ions, 50ns, T=298K, 128 POPC molecules, 7157 water molecules, 44 Na molecules, 88 Cl molecules. This data is used in the NMRLipids II project (nmrlipids.blospot.fi).</p>
Simulated data for "Spot-On: robust model-based analysis of single-particle tracking experiments" (MATLAB format)
<p>See 10.5281/zenodo.834787 for a more complete description.</p>
Water depth time series derived by FLOW-R2D model simulating the Tous dam break
<p>In this dataset, the water depth time series in 21 gauges of Sumacarcel town are derived by the FLOW-R2D model,<br> assuming 240 different combinations of three input paramaters:</p> <p>a) input flow to the computatioal domain (upstream boundaries) (Q)<br> b) the Manning coefficient of the computational domain (n)<br> c) effective slope (required at the upstream boundaries) (S)</p> <p>The sampling for the three parameters is made by Latin Hypercube technique, assuming for each parameter the following interval:</p> <p>a) 10000-20000 m^3/s<br> b) 0.03-0.21 s/m^(1/3)<br> c) 0.0001 - 0.02</p> <p>The dataset consists of the following:<br> <br> 1) input_data.csv file, in which the 240 combinations of the three input parameters is provided (Scenario 100 - 399)<br> 2) runs_tous folder, in which files 100.cv-399.csv. Water depth time series are recorded in 21 gauges.<br> The first column is time (in seconds), and the water dpeths are in meters.<br> <br> Papers relative to this dataset:</p> <p>1) Description of the case study:<br> Alcrudo F and Mulet J (2007). Description of the Tous Dam break case study (Spain).<br> Journal of Hydraulic Research, 45, 45-57.</p> <p>2) Simulation of tous dam break with FLOW-R2D:<br> Bellos V and Tsakiris G (2015). 2D flood modelling: the case of Tous dam break.<br> Proceedings of 36th World Congress of IAHR, the Hague, the Netherlands. (e-proceedings).</p> <p>3) Calibration of the case study of the FLOW-R2D paramteters:<br> Christelis V, Bellos, V, Tsakiris, G (2016). Employing surrogate modelling for the calibration of a 2D flood simulation model.<br> Proceedings of 4th European Congress of IAHR, “Sustainable Hydraulics in the era of global change”,<br> edited by S. Erpicum, B. Dewals, P. Archambeau, M. Pirotton, Liege, Belgium: 727-732.</p> <p>4) Presentation of the FLOW-R2D model:<br> Tsakiris G and Bellos, V (2014) A numerical model for two-dimensional flood routing in complex terrains.<br> Water Resources Management 28(5):1277-1291</p>
Accompanying simulated data for "Go multivariate: recommendations on multilevel hidden Markov models with categorical data of varying complexity"
<p>The multilevel hidden Markov model (MHMM) is a promising vehicle to investigate latent dynamics over time in social and behavioral processes. By including continuous individual random effects, the model accommodates variability between individuals, providing individual-specific trajectories and facilitating the study of individual differences. However, the performance of the MHMM has not been sufficiently explored. Currently, there are no practical guidelines on the sample size needed to obtain reliable estimates related to categorical data characteristics We performed an extensive simulation to assess the effect of the number of dependent variables (1-4), the number of individuals (5-90), and the number of observations per individual (100-1600) on the estimation performance of group-level parameters and between-individual variability on a Bayesian MHMM with categorical data of various levels of complexity. We found that using multivariate data generally alleviates the sample size needed and improves the stability of the results. Regarding the estimation of group-level parameters, the number of individuals and observations largely compensate for each other. Meanwhile, only the former drives the estimation of between-individual variability. We conclude with guidelines on the sample size necessary based on the complexity of the data and the study objectives of the practitioners.</p> <p>This repository contains data generated for the manuscript: "Go multivariate: recommendations on multilevel hidden Markov models with categorical data of varying complexity". It comprehends: (1) model outputs (maximum a posteriori estimates) for each repetition (n=100) of each scenario (n=324) of the main simulation, (2) complete model outputs (including estimates for 4000 MCMC iterations) for two chains of each repetition (n=3) of each scenario (n=324). Please note that the empirical data used in the manuscript is not available as part of this repository. A subsample of the data used in the empirical example are openly available as an example data set in the R package <a href="https://cran.r-project.org/web/packages/mHMMbayes/index.html">mHMMbayes on CRAN</a>. The full data set is available on request from the authors.</p>
Simulations Supporting 'Representing Intra-Hillslope Lateral Subsurface Flow in the Community Land Model', JAMES, 2019
<p>CLM5 Simulations used for the paper "Representing Intra-Hillslope Lateral Subsurface Flow in the Community Land Model". Files are in NetCDF format.</p> <p>Filenames beginning with 'RME_' contain single point simulations for the Reynolds Mountain East catchment. Filenames beginning with 'Global_' contains global simulations used in the sensitivity study. Filenames beginning with 'surfdata_' contain input surface data files used in CLM5 Hillslope simulations.</p>
Supplementary material for High-Performance Earthquake Simulations with Advanced Material Models
<p>This repository contains the setup files to run the simulations, analyse the results and create the plots, used in my PhD thesis "High-Performance Earthquake Simulations with Advanced Material Models". Martin and David have contributed by providing reference solutions for the poroelasticity studies.</p>
Datasets for the paper "Advancing Scanning Probe Microscopy Simulations: A Decade of Development in Probe-Particle Models"
<p>Input data for probe-particle (PP) atomic force microscopy (AFM), scanning tunnelling microscopy (STM), and Kelvin probe microscopy (KPFM) simulations in the paper "Advancing Scanning Probe Microscopy Simulations: A Decade of Development in Probe-Particle Models".</p> <p>Included files:</p> <ul> <li>CO_tip_densities.tar.gz: the total and delta electron densities for a CO tip.</li> <li>hartree-density.tar.gz: the hartree potential (LOCPOT.xsf), electron density (CHGCAR.xsf), and xyz geometry with point charges (mol.xyz) for 6 example molecules: C60 fullerene, formic acid dimer (FAD), 4-(4-(2,3,4,5,6- pentafluorophenylethynyl)-2,3,5,6- tetrafluorophenylethynyl) phenylethynylbenzene (FFPB), pentacene, phtalocyanine, perylene carboxylic anhydride (PTCDA).</li> <li>dft-afm.tar.gz: DFT-calculated forces for the CO-tip for all of the example molecules. Saved as numpy npz files that containt the force arrays under the key 'force' and the physical extent of the region in Ångströms under the key 'scan_window'.</li> <li>Fig_4_STM_data.tar.gz: Hartree potential (cube_001_hartree_potential.cube) and eigenvectors (KS_eigenvectors.band_1.kpt_1.out) of PTCDA for PPSTM simulations.</li> <li>KPFM_Hartree.tar.gz: Hartree potentials of FFPB with a bias voltage for KPFM simulations.</li> </ul> <p>Reproducing the figures in the manuscript:</p> <ul> <li>Fig. 2 (comparison of force-field models): Install ppafm with the [opencl] option, and run the example script in the ppafm repository at examples/paper_figure/run_simulation.py. The image is saved into the same folder with the script.</li> <li>Fig. 3 (KPFM LCPD map): Install ppafm, navigate to examples/FFPB_KPFM, and run the script ./run.sh. After running the script, the LCPD map of Fig. 3(a) can be found in LCPD_atoms_cbar_018.png and the AFM map of Fig. 3(b) in Q-0.10K0.25V0.00/Amp0.50/df_atoms_cbar_018.png.</li> <li>Fig. 4 (STM dI/dV map): Download and compile the PPSTM code (<a href="https://github.com/Probe-Particle/PPSTM">https://github.com/Probe-Particle/PPSTM</a>). After that navigate to tests/PTCDA_mol and use the run_all.sh script.</li> <li>Fig. 5 (IETS): Install ppafm, navigate to examples/FePc_Au-IETS, and run the script run_ppafm-iets.sh. The IETS image can be found in the Q0.00K0.24 folder.</li> </ul> <p>The ppafm version 0.3.1, and PPSTM version 1.0.2 (<a href="https://doi.org/10.5281/zenodo.10669867" rel="nofollow">https://doi.org/10.5281/zenodo.10669867</a>) was used when producing the images for the manuscript.</p>
A Dataset of Reconstructed Carotid Bifurcation Lumen and Plaque Models with Centerline Tree and Simulated Hemodynamics
<p><code>carotid_bifurcation_database.zip</code> contains 79 cases of left and right-side carotid bifurcations (152 inner wall models). For each case, inner wall (lumen) and plaque models were extracted from computed tomography angiography (CTA) scans. The models were segmented, reconstructed, and a centerline tree was created for each geometry using the <a href="https://github.com/PepeEulzer/CarotidAnalyzer">CarotidAnalyzer</a> pipeline. The geometries include varying degrees of internal carotid stenosis. Bifurcations with 100% stenosis were omitted, as the vessel is not discernible in the scan.</p> <p><code>carotid_flow_database.zip</code> contains hemodynamic flow simulations of the above models. Fluid data (velocity, pressure) and surface data (wall shear stress) are given in seperate files for each case. For each field, a systolic and diastolic time step are provided.</p> <p><strong>Further information regarding the extraction pipeline and flow simulations can be obtained from the following publications:<br></strong>P. Eulzer, F. von Deylen, W.-C. Hsu, R. Wickenhöfer, C. M. Klingner, and K. Lawonn (2023), A Fully Integrated Pipeline for Visual Carotid Morphology Analysis. Computer Graphics Forum, 42(3): 25-37. <a href="https://doi.org/10.1111/cgf.14808">https://doi.org/10.1111/cgf.14808</a></p> <p>Kevin Richter, Tristan Probst, Anna Hundertmark, Pepe Eulzer, and Kai Lawonn (2024), Longitudinal wall shear stress evaluation using centerline projection approach in the numerical simulations of the patient-based carotid artery. Computer Methods in Biomechanics and Biomedical Engineering, 27(3): 347-364. <a href="https://doi.org/10.1080/10255842.2023.2185478">https://doi.org/10.1080/10255842.2023.2185478</a></p> <p>P. Eulzer, K. Richter, A. Hundertmark, R. Wickenhöfer, C. M. Klingner, and K. Lawonn (2024), Instantaneous Visual Analysis of Blood Flow in Stenoses Using Morphological Similarity. Computer Graphics Forum 43(3): in print. <a href="https://doi.org/10.1111/cgf.15081">https://doi.org/10.1111/cgf.15081</a></p>
Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP2-RCP4.5)
<p>As global emissions and temperatures continue to rise, global climate models offer projections as to how the climate will change in years to come. These model projections can be used for a variety of end-uses to better understand how current systems will be affected by the changing climate. While climate models predict every individual year, using a single year may not be representative as there may be outlier years. It can also be useful to represent a multi-year period with a single year of data. Both items are currently addressed when working with past weather data by a using Typical Meteorological Year (TMY)methodology. This methodology works by statistically selecting representative months from a number of years and appending these months to achieve a single representative year for a given period. In this analysis, the TMY methodology is used to develop Future Typical Meteorological Year (fTMY) using climate model projections. The resulting set of fTMY data is then formatted into EnergyPlus weather (epw) fi les that can be used for building simulation to estimate the impact of climate scenarios on the built environment.</p> <p>This dataset contains the cross-climate-model version fTMY files for 3281 US Counties in the continental United States. The data for each county is derived from six different global climate models (GCMs) from the 6th Phase of Coupled Models Intercomparison Project CMIP6-ACCESSCM2, BCC-CSM2-MR, CNRM-ESM2-1, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-MM. The six climate models were statistically downscaled for 1980–2014 in the historical period and 2015–2100 in the future period under the SSP585 scenario using the methodology described in Rastogi et al. (2022). Additionally, hourly data was derived from the daily downscaled output using the Mountain Microclimate Simulation Model (MTCLIM; Thornton and Running, 1999). The shared socioeconomic pathway (SSP) used for this analysis was SSP 2 and the representative concentration pathway (RCP) used was RCP 4.5. More information about SSP and RCP can be referred to O'Neill et al. (2020).</p> <p>Please be aware that in cases where a location contains multiple .EPW files, it indicates that there are multiple weather data collection points within that location.</p> <p>More information about the six selected CMIP6 GCMs:</p> <p>ACCESS-CM2 -<br>http://dx.doi.org/10.1071/ES19040<br>BCC-CSM2-MR -<br>https://doi.org/10.5194/gmd-14-2977-2021<br>CNRM-ESM2-1-<br>https://doi.org/10.1029/2019MS001791<br>MPI-ESM1-2-HR -<br>https://doi.org/10.5194/gmd-12-3241-2019<br>MRI-ESM2-0 -<br>https://doi.org/10.2151/jmsj.2019-051<br>NorESM2-MM -<br>https://doi.org/10.5194/gmd-13-6165-2020</p> <p>Additional references:<br>O'Neill, B. C., Carter, T. R., Ebi, K. et al. (2020). Achievements and Needs for the Climate Change Scenario Framework.<br>Nat. Clim. Chang. 10, 1074–1084 (2020). https://doi.org/10.1038/s41558-020-00952-0<br>Rastogi, D., Kao, S.-C., and Ashfaq, M. (2022). How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections? Earth's Future, 10, e2022EF002734. https://doi.org/10.1029/2022EF002734Thornton, P. E. and Running, S. W. (1999). An Improved Algorithm for Estimating Incident Daily Solar Radiation from Measurements of Temperature, Humidity and Precipitation, Agricultural and Forest Meteorology, 93, 211-228.</p> <p><strong>Please cite the following if this data is used in any research or project:</strong></p> <p><em><strong>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New (2023). “Multi-Model Future Typical Meteorological (fTMY) Weather Files for nearly every US County.” The 3rd ACM International Workshop on Big Data and Machine Learning for Smart Buildings and Cities and BuildSys '23: The 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Istanbul, Turkey, November 15-16, 2023. DOI: <a href="http://dx.doi.org/10.1145/3600100.3626637" target="_blank" rel="noreferrer noopener">10.1145/3600100.3626637</a></strong></em></p> <p> </p> <p><strong>Cross-Model Version:</strong></p> <div> <div> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10719204, Feb 2024. [<a href="10719204" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10719178, Feb 2024. [<a href="../records/10719178" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10698921, Feb 2024. [<a href="../records/10698921" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (Cross-Model version-SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10420668, Dec 2023. [<a href="../records/10420668" target="_blank" rel="noopener">Data</a>] </p> <p> </p> <p><strong>Model-specific Version:</strong></p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729277, Feb 2024. [<a href="../records/10729277" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729279, Feb 2024. [<a href="../records/10729279" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729223, Feb 2024. [<a href="../records/10729223" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729201, Feb 2024. [<a href="../records/10729201" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729157, Feb 2024. [<a href="../records/10729157" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729199, Feb 2024. [<a href="../records/10729199" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (East and South – SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.8335814, Sept 2023. [<a href="../records/8335814" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (West and Midwest – SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.8338548, Sept 2023. [<a href="../records/8338548" target="_blank" rel="noopener">Data</a>] </p> </div> </div> <p> </p> <p><strong>Representative Cities Version:</strong></p> <p>Bass, Brett, New, Joshua R., Rastogi, Deeksha and Kao, Shih-Chieh (2022). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation (1.0) [Data set]." Zenodo, doi.org/10.5281/zenodo.6939750, Aug. 2022. [<a href="https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fzenodo.org%2Frecord%2F6939750%23.YwYzp3bMKUk&data=05%7C01%7Clif2%40ornl.gov%7C26cbed91b56e40d4014708dbc0976975%7Cdb3dbd434c4b45449f8a0553f9f5f25e%7C1%7C0%7C638315528798318118%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=S8Z0mjWDMqelFJkp2mfNBVqaiDCdM3AXjQ7PDPEBIu4%3D&reserved=0">Data</a>]</p>
Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP5-RCP8.5)
<p>As global emissions and temperatures continue to rise, global climate models offer projections as to how the climate will change in years to come. These model projections can be used for a variety of end-uses to better understand how current systems will be affected by the changing climate. While climate models predict every individual year, using a single year may not be representative as there may be outlier years. It can also be useful to represent a multi-year period with a single year of data. Both items are currently addressed when working with past weather data by a using Typical Meteorological Year (TMY)methodology. This methodology works by statistically selecting representative months from a number of years and appending these months to achieve a single representative year for a given period. In this analysis, the TMY methodology is used to develop Future Typical Meteorological Year (fTMY) using climate model projections. The resulting set of fTMY data is then formatted into EnergyPlus weather (epw) fi les that can be used for building simulation to estimate the impact of climate scenarios on the built environment.</p> <p>This dataset contains the cross-climate-model version fTMY files for 3281 US Counties in the continental United States. The data for each county is derived from six different global climate models (GCMs) from the 6th Phase of Coupled Models Intercomparison Project CMIP6-ACCESSCM2, BCC-CSM2-MR, CNRM-ESM2-1, MPI-ESM1-2-HR, MRI-ESM2-0, NorESM2-MM. The six climate models were statistically downscaled for 1980–2014 in the historical period and 2015–2100 in the future period under the SSP585 scenario using the methodology described in Rastogi et al. (2022). Additionally, hourly data was derived from the daily downscaled output using the Mountain Microclimate Simulation Model (MTCLIM; Thornton and Running, 1999). The shared socioeconomic pathway (SSP) used for this analysis was SSP 5 and the representative concentration pathway (RCP) used was RCP 8.5. More information about SSP and RCP can be referred to O'Neill et al. (2020).</p> <p>Please be aware that in cases where a location contains multiple .EPW files, it indicates that there are multiple weather data collection points within that location.</p> <p>More information about the six selected CMIP6 GCMs:</p> <p>ACCESS-CM2 -<br>http://dx.doi.org/10.1071/ES19040<br>BCC-CSM2-MR -<br>https://doi.org/10.5194/gmd-14-2977-2021<br>CNRM-ESM2-1-<br>https://doi.org/10.1029/2019MS001791<br>MPI-ESM1-2-HR -<br>https://doi.org/10.5194/gmd-12-3241-2019<br>MRI-ESM2-0 -<br>https://doi.org/10.2151/jmsj.2019-051<br>NorESM2-MM -<br>https://doi.org/10.5194/gmd-13-6165-2020</p> <p>Additional references:<br>O'Neill, B. C., Carter, T. R., Ebi, K. et al. (2020). Achievements and Needs for the Climate Change Scenario Framework.<br>Nat. Clim. Chang. 10, 1074–1084 (2020). https://doi.org/10.1038/s41558-020-00952-0<br>Rastogi, D., Kao, S.-C., and Ashfaq, M. (2022). How May the Choice of Downscaling Techniques and Meteorological Reference Observations Affect Future Hydroclimate Projections? Earth's Future, 10, e2022EF002734. https://doi.org/10.1029/2022EF002734Thornton, P. E. and Running, S. W. (1999). An Improved Algorithm for Estimating Incident Daily Solar Radiation from Measurements of Temperature, Humidity and Precipitation, Agricultural and Forest Meteorology, 93, 211-228.</p> <p><strong>Please cite the following if this data is used in any research or project:</strong></p> <p><em><strong>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New (2023). “Multi-Model Future Typical Meteorological (fTMY) Weather Files for nearly every US County.” The 3rd ACM International Workshop on Big Data and Machine Learning for Smart Buildings and Cities and BuildSys '23: The 10th ACM International Conference on Systems for Energy-Efficient Buildings, Cities, and Transportation, Istanbul, Turkey, November 15-16, 2023. DOI: <a href="http://dx.doi.org/10.1145/3600100.3626637" target="_blank" rel="noreferrer noopener">10.1145/3600100.3626637</a></strong></em></p> <p> </p> <p><strong>Cross-Model Version:</strong></p> <div> <div> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10719204, Feb 2024. [<a href="10719204" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10719178, Feb 2024. [<a href="../records/10719178" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). " Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (Cross-Model Version-SSP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10698921, Feb 2024. [<a href="../records/10698921" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (Cross-Model version-SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10420668, Dec 2023. [<a href="../records/10420668" target="_blank" rel="noopener">Data</a>] </p> <p> </p> <p><strong>Model-specific Version:</strong></p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729277, Feb 2024. [<a href="../records/10729277" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP1-RCP2.6)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729279, Feb 2024. [<a href="../records/10729279" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729223, Feb 2024. [<a href="../records/10729223" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP2-RCP4.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729201, Feb 2024. [<a href="../records/10729201" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (West and Midwest - SP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729157, Feb 2024. [<a href="../records/10729157" target="_blank" rel="noopener">Data</a>] </p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2024). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County in CONUS (East and South - SSP3-RCP7.0)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.10729199, Feb 2024. [<a href="../records/10729199" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (East and South – SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.8335814, Sept 2023. [<a href="../records/8335814" target="_blank" rel="noopener">Data</a>]</p> <p>Shovan Chowdhury, Fengqi Li, Avery Stubbings, Joshua R. New, Deeksha Rastogi, and Shih-Chieh Kao (2023). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation for every US County (West and Midwest – SSP5-RCP8.5)." ORNL internal Scientific and Technical Information (STI) report, doi: 10.5281/zenodo.8338548, Sept 2023. [<a href="../records/8338548" target="_blank" rel="noopener">Data</a>] </p> </div> </div> <p> </p> <p><strong>Representative Cities Version:</strong></p> <p>Bass, Brett, New, Joshua R., Rastogi, Deeksha and Kao, Shih-Chieh (2022). "Future Typical Meteorological Year (fTMY) US Weather Files for Building Simulation (1.0) [Data set]." Zenodo, doi.org/10.5281/zenodo.6939750, Aug. 2022. [<a href="https://gcc02.safelinks.protection.outlook.com/?url=https%3A%2F%2Fzenodo.org%2Frecord%2F6939750%23.YwYzp3bMKUk&data=05%7C01%7Clif2%40ornl.gov%7C26cbed91b56e40d4014708dbc0976975%7Cdb3dbd434c4b45449f8a0553f9f5f25e%7C1%7C0%7C638315528798318118%7CUnknown%7CTWFpbGZsb3d8eyJWIjoiMC4wLjAwMDAiLCJQIjoiV2luMzIiLCJBTiI6Ik1haWwiLCJXVCI6Mn0%3D%7C3000%7C%7C%7C&sdata=S8Z0mjWDMqelFJkp2mfNBVqaiDCdM3AXjQ7PDPEBIu4%3D&reserved=0">Data</a>]</p>
Code and Data for Atmospheric River Induced Precipitation in California as Simulated by the Regionally Refined Simple Convective Resolving E3SM Atmosphere Model Version 0
<p>Includes the code used for all simulations and grid configurations for the paper entitled "Atmospheric River Induced Precipitation in California as Simulated by the Regionally Refined Simple Convective Resolving E3SM Atmosphere Model Version 0" submitted to Geoscientific Model Development. Also included are the model output files for all cases and grid configurations used to generate the analysis and figures in the paper. </p>
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CMIP6 models rarely simulate Antarctic winter sea-ice anomalies as large as observed in 2023
<p><span>Contains processed data to create all figures and tables from the publication Diamond et al. (2024) </span><span>CMIP6 models rarely simulate Antarctic winter sea-ice anomalies as large as observed in 2023; Geophysical Research Letters </span><span>[Full reference available on publication]</span></p> <p><span> </span></p> <p><span>Directory structure and associated contents listed below. Folders contain all data required to make corresponding figure, apart from where indicated. Full description of analysis in Diamond et al. (2024). Unless otherwise indicated, 'SIE' refers to August Antarctic SIE.</span></p> <p><span> </span></p> <p><span>F1/</span></p> <p><span>f1a_obs_ts.csv: Timeseries of 1979-2023 SIE </span></p> <p><span>f1b_all_instances.csv: All instances (labelled by model, simulation, and simulation year) where an anomaly of magnitude SIE_2023 was detected. SIE timeseries from years -20 to +20 about each anomaly.</span></p> <p><span>f1b_quartiles.csv: Upper and lower quartiles, mean, and median, computed over all instances.</span></p> <p><span> </span></p> <p><span>F2/</span></p> <p><span>F2_a.csv: 5--95% range of SIE variability dataset, for all model simulations, and observations.</span></p> <p><span>F2_b.csv: T_aug23, computed using ECDFs, for all model simulations, and multi-model ensemble.</span></p> <p><span>F2_c.csv: T_aug23, computed using GEVs, for all model simulations, and multi-model ensemble.</span></p> <p><span> </span></p> <p><span>F3/</span></p> <p><span>Data for all figures is already provided elsewhere:</span></p> <p><span>F3a: for observations in SF3/SF3_obs_dataset.csv, and for simulations in SF3/SF3_simulations.csv.</span></p> <p><span>F3b: for multi-model ensemble T_aug23 with associated errors in SF6/SF6_all.csv</span></p> <p><span>F3c: for GEV_estimated T_aug23 for all simulations and models in SF6/SF6_all.csv; and<span> </span>5--95% ranges of SIE variability datasets in F2/F2_a.csv</span></p> <p><span> </span></p> <p><span>~</span></p> <p><span>SF1/</span></p> <p><span>SF1_all.csv: SIE timeseries for all model simulations, and associated moving averages.</span></p> <p><span>SF2/</span></p> <p><span>sf2_all.csv: SIE decadal means and ranges for all model simulations.</span></p> <p><span>sf2_quartiles.csv: taken over all simulations above - mean decadal mean in each bin; and lower quartile, median and upper quartile of the decadal ranges in each bin.</span></p> <p><span>SF3/</span></p> <p><span>SF3_obs_dataset.csv: SIE variability dataset for 1979-2023 observations.</span></p> <p><span>SF3_simulations.csv: SIE variability datasets for all model simulations</span></p> <p><span>SF4/</span></p> <p><span>F4_a.csv: p_aug23, computed using ECDFs, for all model simulations, and multi-model ensemble.</span></p> <p><span>F4_b.csv: p_aug22, computed using ECDFs, for all model simulations, and multi-model ensemble.</span></p> <p><span>F4_c.csv: T_aug23, computed using GEVs, for all model simulations, and multi-model ensemble.</span></p> <p><span>F4_d.csv: for all model simulations: p-value that simulation SIE variability dataset is drawn from same probability distribution as observations. Computed using Kolmogorov-Smirnoff 2-sample test.</span></p> <p><span>SF5/</span></p> <p><span>Data for both SF5a and b are already provided in F1/f1b_all_instances.csv.</span></p> <p><span>SF6/</span></p> <p><span>SF6_all.csv: for all model simulations, and multi-model ensemble, p_aug23 and T_aug23, with lower and upper errors (computed from 5--95% confidence interval using bootstrapping)</span></p> <p><span>SF7/</span></p> <p><span>Data for SF7a is already provided elsewhere: for observations in SF3/SF3_obs_dataset.csv, and for simulations in SF3/SF3_simulations.csv.</span></p> <p><span>SF7_all.csv: as for SF6_all.csv, but instead using subset of models.</span></p>
Simulation data from the three-dimensional multifluid MHD model of Najib et al. (2011)
<p>Simulation data from the three-dimensional multifluid MHD model of Najib et al. (2011).</p> <p>We use the data to study the ion escape at Mars.</p> <p> </p>
Model results for Miocene simulations
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