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695 results for “model output”
SURFEX-MEGAN model outputs
<p>The provided data represent the output of the different simulations performed in the submitted GMD paper "Evaluation of isoprene emissions from the coupled model SURFEX-MEGAN2.1"</p>
Model output for "Geometric amplification and suppression of ice-shelf basal melt in West Antarctica"
<h2><strong>Content description<br></strong></h2> <p>This data repository contains Úa-MITgcm model output in support of the manuscript 'Geometric amplification and suppression of ice-shelf basal melt in West Antarctica' (De Rydt and Naughten, 2024).</p> <p>In the paper, results from 4 experiments with the coupled ice-ocean model Úa-MITgcm are presented. The experiments are <em>ref_melt</em>, <em>hi_melt</em>, <em>av_melt</em> and <em>var_melt</em> (see Table 1 in De Rydt and Naughten, 2024). Original MITgcm and Úa output files at the start, middle and end of each experiment are available in the .zip-files with corresponding experiment name. Data for other timestamps are available upon request from the authors.</p> <p>The structure of each file is as follows:</p> <table> <tbody> <tr> <td>exp_name.zip/</td> <td>yyyymm/</td> <td> MITgcm/</td> <td> bathymetry.shice</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>data.diagnostics</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>output.nc</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>pload.mdjwf</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>shelfice_topo.bin</td> </tr> <tr> <td> </td> <td> </td> <td>Ua/</td> <td>DataForMIT.mat</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>NewMeltrate.mat</td> </tr> <tr> <td> </td> <td> </td> <td> </td> <td>UaDefaultRun_yyyymm_dd.mat</td> </tr> </tbody> </table> <p><br><code>yyyymm</code> refers to the start month of the coupling timestep. Note that while the initial state of the ice sheet has been optimized to represent its present-day configuration, the future evolution of the ocean and ice sheet is driven by idealized forcings. The results should not be treated as projections, and the timestamps correspond to model time (in years), not real time.</p> <p><strong>MITgcm diagnostics</strong> (temperature, salinity, velocities and ice-shelf freshwater flux) are stored in <code>output.nc</code> and provided as an average value over the 1-month coupling time interval. The <code>bathymetry.shice</code>, <code>data</code>, <code>data.diagnostics</code>, <code>pload.mdjwf </code>and <code>shelfice_topo.bin</code> files are MITgcm input files for ocean bathymetry, general model parameters, diagnostic model parameters, intial pressure loading under the ice shelf and ice-shelf draft for the corresponding coupling timestep, respectively.</p> <p><strong>Úa data</strong> is provided in <code>UaDefaultRun_yyyymm_dd.mat</code> as a snapshot at the end of the coupling interval (<code>yyyymm_dd</code>). The other files, <code>DataForMIT.mat </code>and <code>NewMeltrate.mat</code> are ice-shelf geometry and MITgcm melt rates at the start of the coupling interval, respectively.</p> <h2><strong>Other resources</strong></h2> <p>The Úa-MITgcm source code and custom Úa-MITgcm configuration files for the experiments are available <a href="https://github.com/knaughten/UaMITgcm/tree/archer2">here</a> and <a href="https://github.com/knaughten/UaMITgcm/tree/archer2/example/ASE_999">here.</a> Scripts to analyse and plot the data are available <a href="https://github.com/janderydt/uamitgcm_ASE">here</a>.</p> <h2><strong>References</strong></h2> <p>De Rydt, J. and Naughten, K.: Geometric amplification and suppression of ice-shelf basal melt in West Antarctica, The Cryosphere, 18, 1863–1888, https://doi.org/10.5194/tc-18-1863-2024, 2024.</p>
Model output and parameters from Deutloff et al. (2025)
<p>This dataset contains the model output of the coupled and the uncoupled ensemble used in Deutloff et al. (2025) (Folder: model output). It also stores all the parameters needed to reproduce the model runs (Folder: Params, Calibration) and the input data for the model (RCMIP_simplified). </p>
AWESOME CGE scenarios and modelling outputs
<p>The primary goal of this analysis was to study the impacts of climate change on food security given the potential supply of alternative water sources: desalination and reused (treated) water within the WEFE nexus in the Mediterranean Sea Basin. Accordingly, the first step of the analysis is to explicitly introduce desalination and treated water into the global CGE model and database. Using the standard GTAP model (Corong, Hertel, McDougall, Tsigas, & van der Mensbrugghe, 2017) and GTAP10A dataset (Aguiar, Chepeliev, Corong, McDougall, & & van der Mensbrugghe, 2019), the water sector was divided into three main categories using the SplitCom application (Horridge, 2008) and including (a) natural water that refers to distribution of water, (b) desalinated water that refers mostly to seawater desalination and (c) treated water that refers primarily to wastewater and brackish water treatment. The resulting modelling framework is used as the basis of the second step, that assesses the externalities associated with the different water sources incorporated in the model.</p> <p>To introduce alternative water sources in the CGE, several assumptions were made. First, desalinated water is produced from the abundant resource – seawater and therefore the scarcity value that is attributed to depletable natural resources is not applicable. Accordingly, desalinated water production can be treated as a sector disaggregated from the existing water sector in the GTAP database. For example, the economic activities of the desalination sector were reported as part of the water sector in input-output tables (United Nations, 2008).</p>
1-km high resolution model outputs using the WRF and WRF-Hydro model Raw data from the manuscipt "Process-based Atmosphere-Hydrology-Malaria Modeling: Performance for Spatio-temporal Malaria Transmission Dynamics in Sub-Saharan Africa "
<p>Here we provide the model outputs from the numerical climate model WRF (Weather Research and Forecasting) and its hydrological coupled model WRF-Hydro for the Health and Demographic Surveillance Systems (HDSS) site regions of Nouna in Burkina Faso. Model results are used for investigating the influence of surface hydrology representation, environmental and climate-sensitive driver factors on malaria incidence.<br>The experiments use the following model configuration: 1km horizontal resolution with 200*200 grid points, WSM6 microphysics, ACM2 PBL, and RRTM & Dudhia radiation scheme. WRF uses the Noah LSM, and WRF-Hydro uses the Noah LSM with enhanced lateral hydrological description (https://ral.ucar.edu/projects/wrf_hydro/overview). These simulations were conducted in the Karlsruhe Steinbuch Centre for Computing (SCC) Horeka.</p> <p>Model outputs are provided in daily step (originally derived from the hourly output). Filename with "wrf-hydro_pr_2000-2020_d02-1km.nc" provides Precipitation,<br>n mm/day"wrf-hydro_tas_2000-2020_d02-1km.nc" provides mean temperature in Celsius, "wrf-hydro_tasmax_2000-2020_d02-1km.nc" provides maximum temperature in Celsius, "wrf-hydro_tasmin_2000-2020_d02-1km.nc" provides minmum temperature in Celsius, "wrf-hydro_dtr_2000-2020_d02-1km.nc" provides diurnal temperature ranges in Celius, "wrf-hydro_rh_2000-2020_d02-1km.nc" provides relative humudity in % and "wrf-hydro_sw_2000-2020_d02-1km.nc" provides the surface hydrology.</p>
Interim model output of estimated bare peat/erosion density in Aberdeenshire and Angus at 1m resolution.
<p>This dataset contains the estimated unit density of peat erosion accross Aberdeenshire and Angus at 1m resolution. These outputs are derived from high resolution aerial imagery using the PeatNet pipeline for predicting the location and extent of peatland degradation features. This output was created to estimate the particulate organic carbon (POC) emissions from eroding peat based on multiple scenarios.</p>
Model output from 'Modelling the circular economy potential of micromobility: A Finnish case study for e-scooters and e-bikes'
<p>These two files include the results for e-scooters and e-bikes. The dynamics of the BAU scenario is shown in the first few columns, then follow the sensitivity analyses. The column names should be self-explanatory, subject to reading the paper.</p>
Conservation outcomes of dietary transitions across different values of nature - model outputs
<p>Archive of output reports produced for the MAgPIE v4.8.2 paper <strong>Conservation outcomes of dietary transitions across different values of nature</strong>.</p> <p><strong>Data</strong></p> <p>The data contain the MAgPIE model and post-processing outputs for all assessed scenarios, including the sensitivity test across the SSP1 and SSP3 scenarios.</p> <ul> <li>The file <em>rev9_healthyLscps_AOH_full_report.Rds </em>contains the outputs of our Area of Habitat (AOH) assessment for all assessed species and across all scenarios. The summary statistics shown in Figure 2 and Figure 3 have been derived from this data set. The data has also been used to create Extended Data Figures<span lang="EN-GB"> 5-</span>7<span lang="EN-GB"> and is shown in the Supplementary Information. </span>However, for Extended Data Figure<span lang="EN-GB"> 7</span> the data was <span lang="EN-GB">used in combination</span> with <span lang="EN-GB">rasterized </span>range polygons obtained from the <span lang="EN-GB">IUCN Red List Database (IUCN 2020).</span></li> <li><span lang="EN-GB">The file <em>rev9_healthyLscps_pollSuff_report_all.Rds </em>reports the global and regional cropland area (Mha) that is subject to insufficient and sufficient pollination supply. This is specified in the column ‘poll_class’, where 1 is insufficient and 2 is sufficient pollination supply. The data is shown in Figure 4, Extended Data Figure 8 and the Supplementary Information.</span></li> <li><span lang="EN-GB">The file <em>rev9_healthyLscps_RUSLE_report_all.Rds</em> reports estimated global and regional soil loss in Pg per year across all modelled scenarios. The data is displayed in Figure 4, Extended Data Figure 9 and the Supplementary Information.</span></li> <li><span lang="EN-GB">The file <em>rev9_healthyLscps_allSSP_report.rds </em>contains regional and global output variables from the MAgPIE model used in this study. The data is shown in Figure 1 and 5, as well as in Extended Data Figures 2-4 and the Supplementary Information.</span></li> <li><span lang="EN-GB">The file <em>rev9_healthyLscps_validation.rds </em>contains validation data for the MAgPIE model output variables.</span></li> </ul> <p> </p> <p><strong><span lang="EN-GB">Model code</span></strong></p> <p>The model code of the MAgPIE and SEALS models can be accessed via:</p> <p><em>MAgPIE model code</em><em>:</em></p> <ul> <li><a href="https://doi.org/10.5281/zenodo.13833444">https://doi.org/10.5281/zenodo.13833444</a> and <a href="https://github.com/magpiemodel/magpie">https://github.com/magpiemodel/magpie</a></li> </ul> <p><em>MAgPIE model documentation</em><em>:</em></p> <ul> <li><a href="https://rse.pik-potsdam.de/doc/magpie/4.3.5/">https://rse.pik-potsdam.de/doc/magpie/4.8.2/</a></li> </ul> <p><em>SEALS model code</em><em>:</em></p> <ul> <li><a href="https://github.com/jandrewjohnson/seals_dev/releases/tag/v1.0.0">https://github.com/jandrewjohnson/seals_dev/releases/tag/v1.0.0</a></li> </ul> <p><em>SEALS mode documentation:</em></p> <ul> <li><a href="https://justinandrewjohnson.com/earth_economy_devstack/seals_overview.html">https://justinandrewjohnson.com/earth_economy_devstack/seals_overview.html</a></li> </ul> <p> </p> <p><strong><span lang="EN-GB">Cited references</span></strong></p> <p><span>IUCN. (2020). <em>The IUCN Red List of Threatened Species. Version 2020-2</em>. https://www.iucnredlist.org.</span><em><span> </span></em><span>Downloaded on 25 November 2020</span></p>
Two Source Energy Balance Model Inputs and Outputs from Drone Surveys at Majadas de Tietar in May 2021
<p><strong>MONSOON PROJECT SURVEY DATA OUTPUTS: Majadas de Tietar Tree-Grass Savanna Ecosystem 05/05/2021-20/05/2021</strong></p> <p>Here we make available high resolution (0.82 cm) energy and water flux maps from unmanned aerial system (UAS) data collected using a Micasense Altum in May 2021. We use the Two Source Energy Balance Model (via pyTSEB) and include model inputs and outputs. We use the Priestley Taylor (TSEB hereafter) and Dual Time Difference (DTD hereafter) methods in pyTSEB, the details of which can be found here pyTSEB https://pytseb.readthedocs.io/en/latest/index.html. The data collection method largely follows https://www.mdpi.com/2072-4292/13/7/1286, however a new paper detailing these surveys in Majadas is under review (as of November 2021). </p> <p>This upload includes the following gridded datasets:</p> <p><strong>Model inputs</strong></p> <p>Zipfiles are named according to their collection date (<strong>DDMMYYYY.7z</strong>). Within each zipfile are the datasets corresponding to different flight times UTC +2 (<strong>hhmm_DDMMYY</strong>). Within each survey folder are rasters with descriptive filenames using the following format:</p> <p><em>Product type_Resolution_survey area_date_flight time.tif</em></p> <p>The following prefixes denote the Product types:</p> <ul> <li>CHM_... = Canopy Height Model (m)</li> <li>GFrac2_... = Green Fraction (0-1)</li> <li>MSpec_... = Raw multispectral dataset from Altum (Blue, Green, Red, NIR, Rededge, LWIR)</li> <li>TEmpK_... = Radiometric Surface Temperature (empirical calibration, K)</li> <li>TRawK_... = Radiometric Surface Temperature (no calibration, K)</li> <li>LST2_... = Radiometric Surface Temperature (calibrated using methods outlined here https://www.mdpi.com/2072-4292/12/7/1075, K)</li> <li>Grass_... = grass vegetation mask</li> <li>Tree_... = tree vegetation mask</li> </ul> <p>We also supply the config files used to generate TSEB and DTD. To run these you will need to edit the filepaths according to your own system. </p> <p><strong>Model Outputs</strong></p> <p><strong>Majadas_TSEB_EMP_outputs.7z</strong> = Two Source Energy Balance (pyTSEB) model outputs (using the Priestley-Taylor method), using radiometric temperature datasets calibrated empirically. </p> <p><strong>Majadas_DTD_EMP_outputs.7z</strong> = TSEB Dual Time Difference model outputs (from pyTSEB) using radiometric temperature datasets calibrated empirically. </p> <p><strong>DTD_ET.7z</strong> = Evapotranspiration rasters (calculated using DTD latent heat data) in g m<sup>-2</sup> s<sup>-1</sup></p> <p><strong>File names are descriptive</strong>:</p> <p><em>Model type_radiometric temperature method_vegetation type_survey area_date_flighttime.tif</em></p> <p>Model type = DTD or TSEB</p> <ul> <li>Radiometric temperature method = always empirical calibration here</li> <li>vegetation type = grass, tree, or merge (which is both tree and grass)</li> <li>Survey area = N (north, or Nitrogen fertiliser treatment), C (central, or Control fertiliser treatment), S (south, or Nitrogen and Phosphorus fertiliser treatment)</li> <li>date = in DDMMYY format</li> <li>flight time = takeoff time for the drone (hhmm) (UTC+2)</li> </ul> <p>To find the exact local time of survey times, please see the table in flight_data3.csv</p>
NOAA-GFDL GFDL-ESM4 CMIP6 ScenarioMIP additional model output
<p>This repository contains annual 3D sea water age since surface contact files from the ScenarioMIP ssp585 simulation conducted with GFDL's ESM4.1 climate model (Dunne et al., 2020) that was contributed to the 6th Coupled Model Intercomparison Project (CMIP6).</p> <p>Both the ocean model native grid and the regridded output can be found in this repository. For the regridded output, the files have been regridded from MOM6 native grid to standard World Ocean Atlas 1x1 horizontal grid, and they have been remapped from the lagrangian vertical coordinate used by GFDL's MOM6 ocean model (Adcroft et al., 2019) to standard World Ocean Atlas depths.</p> <p>Files have undergone QA/QC. All data files are NetCDF.</p> <p>Enquiries should be directed to Jasmin.John (Jasmin.John@noaa.gov) or John Dunne (John.Dunne@noaa.gov)</p> <p>Anyone using these data should cite Dunne et al. (2020) and Adcroft et al. (2019). See references below.</p> <p>Other variables associated with these simulations, along with assigned DOI's, are available through the ESGF CMIP6 portal:</p> <p>https://esgf-node.llnl.gov/projects/cmip6/ </p> <p>______________________________________________________________________</p> <p>The organization of this repository is as follows:</p> <p>Experiments:</p> <p>The tar files in the repository follow this naming convention: <model>_<activity>_<MIP>_<experiment>_<component_and_grid>_<variable>.tar.gz</p> <p> </p> <p>E.g. </p> <p> </p> <p>GFDL_ESM4_CMIP6_ScenarioMIP_ssp585_ocean_annual_z_1x1deg_agessc.tar.gz (regridded)</p> <p>GFDL_ESM4_CMIP6_ScenarioMIP_ssp585_ocean_annual_z_agessc.tar.gz (native grid)</p> <p>The organization of above repository is as follows:</p> <p>Experiment: ssp585</p> <p>NOAA-GFDL/CMIP6/ScenarioMIP/GFDL-ESM4/ESM4_ssp585_D1/</p> <p>For each experiment, 3D annual sea water age data can be found in the sub-directory:</p> <p>/ocean_annual_z_1x1deg (for regridded output)</p> <p>/ocean_annual_z (for native grid output)</p> <p>_________________________________________________________________________</p> <p>NOTES:</p> <p>Provenance:</p> <p>The data provided here do not have metadata that can be used for provenance and traceability back to NOAA/GFDL. The data DOI assigned should be used when sharing and citing these data.</p> <p>_________________________________________________________________________</p>
PACT-1D model version including polar halogen emissions - output files
<p>This dataset includes all output files created from the PACT-1D model (v1.0) developed with Arctic chlorine and bromine emission parameterizations. The PACT-1D source code used to create these output files is available at: https://doi.org/10.5281/zenodo.5654589.</p> <p> </p>
Planetary Atmosphere-Interior Model Outputs from Time-Evolution Movie
<p>This array contains the outputs from the movie version of Fig. 2 in Krissansen-Totton et al. (2021, Nature Astronomy). The numpy array can be loaded as follows</p> <p>Outputs = np.load("Output_arrays.npy",allow_pickle=True)</p> <p>The array has format Outputs[i][k][j]</p> <p>where i = 0, ... 5 (total_time, pO2, pCO2, pH2O, Tsurf, and AU)</p> <p>k = 0 , ..., 1437 is the iteration number</p> <p>j = 0 , ... , 2000 is the time index</p> <p>(AU is just a scalar providing orbital separation of that iteration)</p>
Climate model output for "The Unexpected Oceanic Peak in Energy Input to the Atmosphere and its Consequences for Monsoon Rainfall"
<p>Climate model output associated with the manuscript "The Unexpected Oceanic Peak in Energy Input to the Atmosphere and its Consequences for Monsoon Rainfall"</p>
Model output data and figures' code for Fujimori & Wu et al., Land-based climate change mitigation measures can affect agricultural markets and food security
<p>Model output data and figures' code for "Fujimori & Wu et al., Land-based climate change mitigation measures can affect agricultural markets and food security" in Nature Food (DOI: 10.1038/s43016-022-00464-4)</p>
Model input and output data of the FlexMex model comparison
<p>This data collection includes the input and output data of the FlexMex model experiment (grant number: 03ET4077A-H) funded by the German Federal Ministry for Economic Affairs and Energy (BMWi). The aim of the FlexMex project is to better understand the interrelationships of modelling approaches and model results in the mapping and analysis of technical-structural flexibilities in future electricity systems.</p> <p>The data are separated in the two subfolders InputData and OutputData. For the input data, a distinction is made between scalar and time series data.</p> <p>Please find additional information on the models, test cases and analysis in the ReadMe and the publications cited there.</p>
Model output, Khaled et al., Iron distribution in the aqueous phase (short title)
<p>Data for figures (Khaled et al., The number fraction of iron-containing particles affects OH, HO2 and H2O2 budgets in the atmospheric aqueous phase, Atmos. Chem. Phys.)</p>
Model output for "Numerically consistent budgets of potential temperature, momentum, and moisture in Cartesian coordinates: application to the WRF model"
<p>These data were produced with WRFlux v1.2.1 (https://github.com/matzegoebel/WRFlux/) from a numerical simulation with the community model WRF. Simulations represent the evolution of a convective boundary layer in the atmosphere over an idealized 2D mountain ridge. The data are published in connection with the article "Numerically consistent budgets of potential temperature, momentum and moisture in Cartesian coordinates: Application to the WRF model" in "Geoscientific Model Development" (https://doi.org/10.5194/gmd-15-669-2022).</p> <p>Three-dimensional (x, z, t) fields of five prognostic variables are provided: Potential temperature (T), water vapor mixing ratio (Q), cross-mountain (U), along-mountain (V), and vertical windspeed (W). All fields are averaged in time (30 min averaging interval) and in the along-mountain direction y.</p> <p>The repository contains the following files:</p> <p>grid.nc : variables related to the WRF numerical grid, air density<br> [U,W,T,Q]_flux.nc : resolved and subgrid-scale fluxes<br> [U,W,T,Q]_tendency.nc : resolved and subgrid-scale tendency components<br> UVWT_MEAN.nc : averaged values of the variables themselves<br> plotting.py : python script to approximately reproduce the figures of the paper. Requires the python packages matplotlib, xarray, and netcdf4.</p> <p>Figure 6 in the paper cannot be accurately reproduced with these data since the original figure uses 4D (x, y, z, t) output.</p> <p>For details on the simulation, refer to the article.</p>
Modeling output for "The dynamic atmospheric and aeolian environment of Jezero crater, Mars"
<p>This dataset contains meso- and microscale numerical modeling output supporting the findings presented in the paper, "Newman et al., The dynamic atmospheric and aeolian environment of Jezero crater, Mars, Science Advances"</p>
Codes and model output supporting Analysis of the Evolution of Parametric Drivers of High-End Sea-Level Hazards
<p>Codes and model output supporting Analysis of the Evolution of Parametric Drivers of High-End Sea-Level Hazards (Advances in Statistical Climatology, Meteorology and Oceanography, May 2022)</p>
Modeling output for "Orbital and In-Situ Investigation of Periodic Bedrock Ridges in Glen Torridon, Gale Crater, Mars"
<p><strong>General information:</strong></p> <p>Please contact Claire Newman (claire@aeolisresearch.com) if you have questions about this dataset.</p> <p><em><strong>Title of Dataset: </strong></em>Modeling output for "Orbital and In-Situ Investigation of Periodic Bedrock Ridges in Glen Torridon, Gale Crater, Mars"</p> <p><em><strong>Author Information:</strong></em><br> Name: Claire E. Newman<br> Institution: Aeolis Research<br> Email: claire@aeolisresearch.com</p> <p>Recommended citation for this dataset: Newman C.E. (2022) "Modeling output for Orbital and In-Situ Investigation of Periodic Bedrock Ridges in Glen Torridon, Gale Crater, Mars", Dataset.</p> <p><strong>Summary taken from the paper "Orbital and In-Situ Investigation of Periodic Bedrock Ridges in Glen Torridon, Gale Crater, Mars" by Kathryn M. Stack et al., accepted by JGR Planets for publication in 2022:</strong></p> <p>The orientation of Glen Torridon ridges was compared against outputs from the Mars Weather Research and Forecasting (MarsWRF) model. The MarsWRF model has been used to simulate the atmospheric circulation inside Gale crater to compare with MSL Rover Environmental Monitoring Station (REMS) wind measurements (Newman et al., 2017) and to assist in interpreting observations of dust devils (Newman et al., 2019) and aeolian changes (Baker et al., 2018, 2022). The output used in this work comes from the ‘vertical grid B’ simulations described in Newman et al. (2017), which provide the best match to observed winds and aeolian features. For Gale crater modeling, MarsWRF is run as a global model with nested higher-resolution domains that gradually increase the model’s horizontal resolution over smaller and smaller areas, finally providing output at ~400m grid spacing over the NW quadrant of the crater. The version of MarsWRF used here includes the treatment of radiative transfer in Mars’s dusty CO2 atmosphere, the seasonal CO2 cycle, subsurface-surface-atmosphere exchange of heat and momentum, and vertical mixing of heat and momentum, with surface properties (topography, roughness, albedo, etc.) based on orbital datasets and the seasonally-evolving, non-dust-storm “Mars Climate Database” dust distribution imposed (see Richardson et al., 2007 for more details).</p> <p>The model outputs minute-by-minute predictions of surface friction velocity, u*, and atmospheric density at 1.5m, ρ, for 7 sols at each of 12 periods, which are equally spaced in planetocentric solar longitude (Ls) through the martian year. For each output, the surface wind stress, τ, is found from τ=ρu_*^2. We then adjusted the contribution of each period to account for the varying number of sols Mars spends around each period over its orbit, before producing wind stress roses, which thus represent the total wind stress and direction over a non-dust-storm Mars year.</p> <p><strong>Specifically, contained in the "outputsfullcorr.nc" netCDF data file are:</strong></p> <p><strong><em>Variables:</em></strong></p> <p>PSFC: surface pressure (Pa)</p> <p>T1_5: temperature at 1.5m height above the surface (K)</p> <p>U1_5: zonal (west-to-east) wind at 1.5m height above the surface (m/s)</p> <p>V1_5: meridional (south-to-north) wind at 1.5m height above the surface (m/s)</p> <p>UST: surface friction speed (m/s)</p> <p><strong><em>Output times:</em></strong></p> <p>These variables are output from MarsWRF every minute starting at 15:10 Local True Solar Time in the first sol, with 1440 outputs per martian sol. There are 68 sols of data in total, with between 5 and 7 sols of data from each 30° Ls range, with the relative number chosen to reflect the fraction of a Mars year spent in that Ls range. In detail, the sols of the martian year used (where Ls=0 would be at the start of sol 1 and Ls=360 at the end of sol 669) are:</p> <p>Ls~0°: sols 668-669 & 01-4</p> <p>Ls~30°: sols 60-65</p> <p>Ls~60°: sols 124-130</p> <p>Ls~90°: sols 194-200</p> <p>Ls~120°: sols 254-259</p> <p>Ls~150°: sols 314-319</p> <p>Ls~180°: sols 374-378</p> <p>Ls~210°: sols 424-428</p> <p>Ls~240°: sols 474-478</p> <p>Ls~270°: sols 514-518</p> <p>Ls~300°: sols 564-568</p> <p>Ls~330°: sols 614-618</p> <p><strong><em>Output longitudes and latitudes are in file "static.nc":</em></strong></p> <p>The output is provided for a uniform grid of 19 longitudes by 15 latitudes. The longitudes and latitudes (variables XLONG and XLAT) in static.nc match those in outputsfullcorr.nc, but below are listed the longitudes and latitudes for convenience:</p> <p>Longitudes (19): 137.321, 137.3292, 137.3374, 137.3457, 137.3539, 137.3621, 137.3704, <br> 137.3786, 137.3868, 137.3951, 137.4033, 137.4115, 137.4198, 137.428, <br> 137.4362, 137.4444, 137.4527, 137.4609, 137.4691 </p> <p>Latitudes (15): -4.786012, -4.777781, -4.769551, -4.761321, -4.75309, -4.74486, -4.736629, -4.728399, -4.720168, -4.711937, -4.703707, -4.695477, -4.687246, -4.679016, -4.670785</p> <p>static.nc also provides the local topographic height used by the model at each location (variable name HGT).</p> <p><strong><em>Output data format:</em></strong></p> <p>NetCDF (Network Common Data Form) is a set of software libraries and machine-independent data formats that support the creation, access, and sharing of array-oriented scientific data. It is also a community standard for sharing scientific data. The Unidata Program Center supports and maintains netCDF programming interfaces for <a href="https://docs.unidata.ucar.edu/netcdf-c/current/">C</a>, <a href="https://docs.unidata.ucar.edu/netcdf-cxx/current/">C++</a>, <a href="https://www.unidata.ucar.edu/software/netcdf-java/">Java</a>, and <a href="https://docs.unidata.ucar.edu/netcdf-fortran/current/">Fortran</a>. Programming interfaces are also available for Python, IDL, MATLAB, R, Ruby, and Perl.</p> <p>See: <a href="https://www.unidata.ucar.edu/software/netcdf/">https://www.unidata.ucar.edu/software/netcdf/</a></p> <p><strong>References:</strong></p> <p>Baker, M.M., Lapotre, M.G.A., Minitti, M.E., Newman, C.E., Sullivan, R., Weitz, C.M., Rubin, D.R., Vasavada, A.R., Bridges, N.T., & Lewis, K.W. (2018). The Bagnold Dunes in Southern Summer: Active Sediment Transport on Mars Observed by the Curiosity Rover. <em>Geophysical Research Letters, 45</em>(17), 8853-8863. <a href="https://doi.org/10.1029/2018GL079040">https://doi.org/10.1029/2018GL079040</a>.</p> <p>Baker, M.M., Newman, C.E., Lapotre, M.G.A., Sullivan, R., Bridges, N.T., & Lewis, K.W. (2018). Coarse Sediment Transport in the Modern Martian Environment. <em>Journal of Geophysical Research- Planets, 123</em>(6), 1380-1394. <a href="https://doi.org/10.1002/2017JE005513">https://doi.org/10.1002/2017JE005513</a>.</p> <p>Baker, M.M., Newman, C.E., Sullivan, R., Minitti, M.E., Edgett, K.S., Fey, D., Ellison, D., & Lewis, K.W. (2022). Diurnal variability in aeolian sediment transport at Gale crater, Mars, J. Geophys. Res. (Plan.), 127, e2020JE006734, https://doi.org/10.1029/2020JE006734.</p> <p>Newman, C., Gómez‐Elvira, J. G., Marín, M., Navarro, S., Torres, J., Richardson, M. I., et al. (2017). Winds measured by the Rover Environmental Monitoring Station (REMS) during the Mars Science Laboratory (MSL) rover's Bagnold Dunes Campaign and com- parison with numerical modeling using MarsWRF. <em>Icarus, 291</em>, 203–231. https://doi.org/10.1016/j.icarus.2016.12.016</p> <p>Newman, C. E., Kahanpää, H., Richardson, M. I., Martinez, G. M., Vicente‐Retortillo, A., & Lemmon, M. (2019). Convective vortex and dust devil predictions for Gale crater over 3 mars years and comparison with MSL‐REMS observations, <em>J. Geophys. Res. (Plan.</em>), 124, 3442– 3468, https://doi.org/10.1029/2019JE006082.</p> <p>Richardson, M.I., Toigo, A.D., & Newman, C.E. (2007). PlanetWRF: A general purpose, local to global numerical model for planetary atmospheric and climate dynamics<em>, J. Geophys. Res. (Plan.)</em>, 112, E09001, <a href="https://doi.org/10.1029/2006JE002825">https://doi.org/10.1029/2006JE002825</a>.</p>
ScienceDex guides
Understand access before you commit
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.