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695 results for “model output”
Fig. 6 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 6: Temperature (A) and salinity (B) average profiles with the associated STD for model (red) and Argo (blue), calculated from the available profiles in the southern Ionian region. Hovmöller diagrams of the differences between Argo and model associated profiles over time for temperature (C) and salinity (D) in the southern Ionian.
Fig. 5 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 5: Temperature (A) and salinity (B) average profiles with the associated STD for model (red) and Argo (blue), calculated from the available profiles in the northern Ionian region. Hovmöller diagrams of the differences between Argo and model associated profiles over time for temperature (C) and salinity (D) in the northern Ionian.
Fig. 3 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 3: Temperature (A) and salinity (B) average profiles with the associated STD for model (red) and Argo (blue), calculated from the available profiles in the southern Adriatic region. Hovmöller diagrams of the differences between Argo and model associated profiles over time for temperature (C) and salinity (D) in the south Adriatic.
Fig. 4 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 4: Temperature (A) and salinity (B) average profiles with the associated STD for model (red) and Argo (blue), calculated from the available profiles in the Otranto Strait. Hovmöller diagrams of the differences between Argo and model associated profiles over time for temperature (C) and salinity (D) in the Otranto Strait.
Fig. 1 in Comparison of high resolution hydrodynamic model outputs with in-situ Argo profiles in the Ionian Sea Abstract
Fig. 1: SANI model bathymetry (A). The geographical area covered by SANI model (red rectangular) and the divided sub-regions SA (Southern Adiatic - yellow), OS (Otranto Strait - green), NI (Northern Ionian – brown) and SI (Southern Ionian – blue). All the available (966) Argo profiles for the period 2008-2012 from 21 individual floats denoted with different colours according to their WMO number (B).
Model outputs from "Holocene thinning in central Greenland controlled by the Northeast Greenland Ice Stream"
<p>Two-dimensional outputs of the best Yelmo model simulation within the ensemble presented in the manuscript by Tabone et al., 2024, titled "Holocene Thinning in Central Greenland Controlled by the Northeast Greenland Ice Stream," published in Nature Communications. For more information, please contact the authors and read the paper. </p>
Model Output for CCKW analyses
<p>These files are the output of WRF tropical channel simulation. Three cumulus parameterizations were analyzed, but one is uploaded as a representative (Grell Freitas Scheme). The brief description of each file are as follows :</p> <ol> <li> Q_cu_GF.nc : Diabatic heating anomaly data</li> <li>PREC_filt_cu_GF.nc : Kelvin Wave filtered precipitation anomaly data</li> <li>T_cu_GF.nc : Temperature anomaly data</li> <li>EOF_cu_GF.nc : EOF decomposition data (2 modes)</li> </ol>
DALROMS-NWA12 v1.0, a coupled circulation-sea ice-biogeochemistry model for the northwest North Atlantic: output files (2 of 2)
<p>DALROMS-NWA12 v1.0 is a coupled circulation-sea ice-biogeochemistry modelling system based on ROMS, CICE, and MCT. The model domain covers the North Atlantic Ocean from ~81 deg W to ~39 deg W and ~33.5 deg N to 76 deg N. This record includes daily-mean output of all (ocean circulation, sea ice, and biogeochemistry) modules for September 2015. Similar files for September 2013 are available at <a href="https://doi.org/10.5281/zenodo.12744506" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12744506</a>.</p> <p>Model codes, scripts for compiling the model, and sample CPP header files and runtime parameter files (namelists) for physics-only simulations are available at <a href="https://doi.org/10.5281/zenodo.12752091" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12752091</a>. CPP header and runtime parameter files for the biogeochemistry module are available upon request.</p> <p>Sample input files for physics-only simulations are available at <a href="https://doi.org/10.5281/zenodo.12752190" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12752190</a>, <a href="https://doi.org/10.5281/zenodo.12734049" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12734049</a>, and <a href="https://doi.org/10.5281/zenodo.12735153" target="_blank" rel="noopener">https://doi.org/10.5281/zenodo.12735153</a>. Input files for the biogeochemistry module are available upon request.</p>
ISIMIP 3b, Land Use Models outputs intercomparison
<p>These data sets contain the information used to compare the land use models' outputs submitted as human forcings for ISIMIP3b and utilized for different impact analyses and teams under various scenarios (SSP1-RCP2.6, SSP3-RCP7.0, SSP5-RCP8.5). </p>
WRF Model Output for the 3 km grid of the Hurricane Nature Run, days 08-01-2005 to 08-03-2005
<p>This is WRF model output for the Hurricane Nature Run presented in Nolan et al. (2013). The tar file contains gzipped netcdf files each with 6 model outputs at 30 minute intervals.</p>
WRF Model Output for the 3 km grid of the Hurricane Nature Run, days 08-01-2005 to 08-03-2005
<p>This is WRF model output for the Hurricane Nature Run presented in Nolan et al. (2013). The tar file contains gzipped netcdf files each with 6 model outputs at 30 minute intervals.</p>
Figure 1. Inputs and output parameters for TDNN models-Time-Delay Artificial Neural Network Computing Models for Predicting Shelf Life of Processed Cheese
<p>The data consisted of 36 samples, which were divided into two subsets, i.e., 30 used for<br> training the network and 6 for testing the TDNN models. Soluble nitrogen, pH, standard plate<br> count, yeast & mould count, and spore count were taken as input parameters, and sensory score as<br> output parameter for developing TDNN single and multilayer models (Fig.1).</p>
Ocean-only simulation outputs based on the IPSL-CM5A-LR and IPSL-CM5A-MR models
<p><br> Name of the simulations<br> piControl2 : COUPLED-LR<br> piControlMR3 : COUPLED-MR<br> OR2L2E : CLIM-LR<br> OR2L2G : CLIM-MR<br> OR2L2M : TOTAL-LR member 1<br> OR2L2M2 : TOTAL-LR member 2<br> OR2L2M3 : TOTAL-LR member 3<br> OR2L2M4 : TOTAL-LR member 4<br> OR2L2M5 : TOTAL-LR member 5<br> OR2L2R : TOTAL-MR member 1<br> OR2L2R2 : TOTAL-MR member 2<br> OR2L2R3 : TOTAL-MR member 3<br> OR2L2R4 : TOTAL-MR member 4<br> OR2L2R5 : TOTAL-MR member 5<br> OR2L2Q : RANDOM-LR<br> OR2L2S : RANDOM-MR<br> piControl2_perm : like COUPLED-LR but with random permutation identical to those used to generate RANDOM-LR<br> piControlMR3_perm : like COUPLED-MR but with random permutation identical to those used to generate RANDOM-MR</p> <p>Variables :<br> zomsfatl : Atlantic meridional overturning streamfunction<br> thetao_700 : averaged potential temperature between 0m and 700m<br> dens : 3D seawater density<br> EOF-NA_SLP : PC1 of the North Atlantic monthly SLP</p> <p> </p>
EMAC-L90MA-SD output used in "Stratospheric Injection of Brominated Very Short-Lived Substances: Aircraft Observations in the Western Pacific and Representation in Global Models"
<p>Output of halocarbons, inorganic bromine, and tropopause pressure from EMAC-L90MA-SD used in:</p> <p>Wales et al., Stratospheric Injection of Brominated Very Short-Lived Substances: Aircraft Observations in the Western Pacific and Representation in Global Models." <em>Journal of Geophysical Research: Atmospheres,</em> (2018).</p> <p>The EMAC-L90MA-SD simulation uses ERA-Interim meteorology and was prepared according to: </p> <p>Jöckel, P., Tost, H., Pozzer, A., Kunze, M., Kirner, O., Brenninkmeijer, C. A. M., Brinkop, S., Cai, D. S., Dyroff, C., Eckstein, J., Frank, F., Garny, H., Gottschaldt, K.-D., Graf, P., Grewe, V., Kerkweg, A., Kern, B., Matthes, S., Mertens, M., Meul, S., Neumaier, M., Nützel, M., Oberländer-Hayn, S., Ruhnke, R., Runde, T., Sander, R., Scharffe, D., & Zahn, A.: Earth System Chemistry integrated Modelling (ESCiMo) with the Modular Earth Submodel System (MESSy) version 2.51, <em>Geoscientific Model Development</em>, 9, 1153–1200, doi: 10.5194/gmd-9-1153-2016, URL <a href="http://www.geosci-model-dev.net/9/1153/2016/">http://www.geosci-model-dev.net/9/1153/2016/</a> (2016)</p> <p>For further details, please contact Patrick Joeckel (Patrick.Joeckel@dlr.de) and Phoebe Graf (Phoebe.Graf@dlr.de)</p>
Historical climate model output of ECHAM5-wiso from 1871-2011 at T106 resolution
<p>Historical climate model simulation of the isotope-enabled ECHAM5-wiso model from the years 1871 to 2011 at T106 (1 degree) resolution. The model code was provided by Martin Werner of AWI. The simulations were designed and run by Nathan Steiger on the Yellowstone supercomputer. The boundary conditions were interpolated HadISST fields. The simulations also included updated fractionation factors (an option within the ECHAM5-wiso Fortran code). All variables here are at monthly resolution in netcdf format. Please <a href="http://www.ldeo.columbia.edu/~nsteiger/contact.html">contact</a> Nathan Steiger if you have any questions about the simulation. In addition to the data citation, please also cite the following reference for where the data were first published: Steiger, N.J., E.J. Steig, S.G. Dee, G.H. Roe, and G.J. Hakim, (2017): <em>Climate reconstruction using data assimilation of water-isotope ratios from ice cores.</em> Journal of Geophysical Research: Atmospheres, doi:10.1002/2016JD026011.</p> <p>Standard variables include: ECHAM5 T106 orography, 2 m temperature, surface pressure, mean sea level pressure, vertically integrated water vapor, total precipitation, evaporation, soil moisture, relative humidity, specific humidity, atmospheric stream function at 200 hPa, geopotential height at 500 hPa, windspeed at 10 m, and u-velocity wind at 200 hPa. Moisture variables are given at the surface (lowest atmospheric level).</p> <p>Isotope variables include: d18O and dD of total precipitation, d18O and dD of evaporation, d18O and dD of snow fall, d18O and dD of seasonal snow cover, d18O and dD of snow on glaciers, d18O and dD of soil moisture, and specific humidity of water isotopes.</p>
Model output from "A model intercomparison of CCN-limited tenuous clouds in the high Arctic"
<p>Model output from "A model intercomparison of CCN-limited tenuous clouds in the high Arctic", accepted for publication in Atmospheric Chemistry and Physics, 2018, same authors. The intercomparison includes output from three large-eddy simulation models (UCLALES-SALSA, MIMICA, and COSMO-LES) and three numerical weather prediction models (COSMO-NWP, WRF, and UM-CASIM) for a case study of high-Arctic tenuous cloud based on observations from the 2008 Arctic Summer Cloud Ocean Study (ASCOS) campaign. See publication for details. The discussion preprint for peer review can be found at https://doi.org/10.5194/acp-2017-1128.</p>
Secondary ice production parameterization output - COSMO model
<p>Secondary ice production via processes like rime splintering, frozen droplet shattering, and breakup upon ice hydrometeor collision have been proposed to explain discrepancies between in-cloud ice crystal and ice-nucleating particle numbers. To understand the impact of this kind of additional ice number generation on surface precipitation, we present one of the first studies to implement frozen droplet shattering and ice-ice collisional breakup parameterizations in a larger-scale model. We simulate a cold frontal rainband from the Aerosol Properties, PRocesses, And InfluenceS on the Earth's Climate campaign and investigate the impact of the new parameterizations on the simulated ice crystal number concentrations (ICNC) and precipitation. Near the convective regions of the rainband, contributions to ICNC can be as large from secondary production as from primary nucleation, but ICNCs greater than 50 L-1 remain underestimated by the model. Addition of the secondary production parameterizations also clearly intensifies the differences in both accumulated precipitation and precipitation rate between the convective towers and non-convective gap regions. We suggest, then, that secondary ice production parameterizations be included in large-scale models on the basis of large hydrometeor concentration and convective activity criteria.</p>
Annex B to the technical report on the raw primary commodity (RPC) model - Summary statistics of the output data
<p><strong>The raw primary commodity model</strong>:</p> <p>Dietary exposure is typically calculated by combining food consumption data with occurrence data. EFSA’s food consumption data are stored in the Comprehensive European Food Consumption Database (Comprehensive Database). Some of these data, however, cannot be used in exposure assessments when the occurrence data are reported for the raw primary commodities (RPCs). The RPC model aims to bridge this gap by transforming the Comprehensive Database into RPC consumption data. Using the RPC model, EFSA successfully developed a new RPC Consumption Database, which contains 51 dietary surveys from 23 different countries. These surveys cover a total of 94,532 subjects and 26,573,088 RPC consumption records. The consumption data generated by the RPC model were manually checked and validated by means of case studies. These case studies demonstrated that the RPC consumption data are suitable for assessing dietary exposure to chemicals where the occurrence data are predominantly available for RPCs.</p> <p><strong>Annex B to the technical report on the raw primary commodity model:</strong></p> <p>Annex B is an excel file which presents summary statistics of the output data generated by the RPC model. The following tables are included in Annex B:</p> <p>Table B.1 :Summary statistics of chronic RPC consumption expressed in g/kg bw per day (total population)</p> <p>Table B.2 :Summary statistics of chronic RPC consumption expressed in g/day (total population)</p> <p>Table B.3 :Summary statistics of acute RPC consumption expressed in g/kg bw (consumers only)</p> <p>Table B.4 :Summary statistics of acute RPC consumption expressed in g (consumers only)</p> <p>Table B.5 :Comparison of the RPC consumption data with RPC consumption data used in EFSA's Pesticides Residues Intake Model (PRIMo)</p> <p>Table B.6 :Contribution of processed products to the average chronic RPC consumption</p>
Results of TIMES model & inputs and outputs of the multicriteria and portfolio analysis
<p>These datasets contain the underlying data for the following publication: <strong>Energy efficiency promotion in Greece in light of risk: Evaluating policies as portfolio assets, Energy, https://doi.org/10.1016/j.energy.2018.12.180</strong></p>
Bahamas National Hazard Analysis. Data Inputs and Outputs for the InVEST Coastal Vulnerability Model.
<p>The following folders contain the model inputs and outputs for the InVEST Coastal Vulnerability model that were used in the analysis discussed in:</p> <p>Silver JM, Arkema KK, Griffin RM, Lashley B, Lemay M, Maldonado S,<br> Moultrie SH, Ruckelshaus M, Schill S, Thomas A, Wyatt K and Verutes G<br> (2019) Advancing Coastal Risk Reduction Science and Implementation by<br> Accounting for Climate, Ecosystems, and People. Front. Mar. Sci. 6:556.<br> doi: 10.3389/fmars.2019.00556</p> <p>The readme.txt file contains information about data layers.</p>
ScienceDex guides
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These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.
Allen Brain Atlas
Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.
Annotated Behaviour and Observability Dataset (ABODe)
ABODe is a University of Edinburgh DataShare dataset for behavior classification in group-housed mice using home-cage video, identities, bounding boxes, ground-plate positions, and annotator labels.
DANDI Archive for NWB datasets
DANDI is a BRAIN Initiative archive for publishing and sharing neurophysiology data, including electrophysiology, optophysiology, and behavioral data packaged as NWB and related standards.
International Brain Laboratory public data
The International Brain Laboratory public data releases expose standardized mouse decision-making experiments, including Neuropixels recordings, widefield calcium imaging, behavior, and session metadata accessed through the ONE API.
OpenNeuro
OpenNeuro is a free, open platform for sharing neuroimaging datasets, with public search, dataset pages, and download paths for web, S3, DataLad, and the OpenNeuro CLI.