Skip to main content
ConductScienceScienceDex
Powered by ShareScore

Find research datasets worth reusing

Search datasets from major research repositories and use ShareScore to quickly assess how well each record supports discovery, access, and reuse.

359

datasets available to search

ShareScore release 0.9.0

Reset

Dataset results

359 results for “Regional modelling”

Learn how ShareScore rates datasets ↗
zenodo40/100

A Layer-averaged Nonhydrostatic Dynamical Framework on an Unstructured Mesh for Global and Regional Atmospheric Modeling: Model Description, Baseline Evaluation and Sensitivity Exploration

<p>Selected model output data for supporting this&nbsp;paper.</p> <p>List of Files:</p> <p>2dtracer.tar.gz: correlated tracer test</p> <p>rh3d.tar.gz: 3D Rossby-Haurwitz Wave</p> <p>modon.tar.gz: Colliding Modons</p> <p>jwss.tar.gz: Jablonowski-Williamson Baroclinic Steady State</p> <p>jwbw_1d.tar.gz: 1D data output from&nbsp;Jablonowski-Williamson Baroclinic Wave</p> <p>jwbw_2d.tar.gz: 2D data output from&nbsp;Jablonowski-Williamson Baroclinic Wave</p> <p>dcmip31.tar.gz: DCMIP3-1 nonhydrostatic gravity wave</p> <p>Klemp15.tar.gz: Nonhydrostatic Mountain Waves&nbsp;in Klemp et al. 2015</p> <p>held-suarez.tar.gz: Held-Suarez dry climate (post-processed data for plotting, the raw daily data are too large to upload)</p> <p>jwbwvr.tar.gz: Variable-Resolution modeling of the&nbsp;Jablonowski-Williamson Baroclinic Wave</p> <p>&nbsp;</p> <p>see&nbsp;https://doi.org/10.5281/zenodo.3544795&nbsp;for a companion work</p> <p>References:</p> <p>Zhang, Y., J. Li, R. Yu, S. Zhang, Z. Liu, J. Huang, and Y. Zhou, 2019: A Layer-Averaged Nonhydrostatic Dynamical Framework on an Unstructured Mesh for Global and Regional Atmospheric Modeling: Model Description, Baseline Evaluation, and Sensitivity Exploration. <em>Journal of Advances in Modeling Earth Systems</em>, <strong>11,</strong> 1685-1714.</p>

opencc-by-4.0Jan 2019View details →
zenodo40/100

TERENO-preAlpine observatory and ScaleX 2016 campaign data set associated with HESS paper "High-resolution fully-coupled atmospheric–hydrological modeling: a cross-compartment regional water and energy cycle evaluation"

<p>netCDF Dataset, that holds processed hourly station observations for the period 2016-06-01 to 2016-10-31.</p> <p>dimensions:<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; time = 3672 ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; stations = 6 ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; name_strlen = 6 ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; depth = 3 ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; height = 201 ;<br> variables:<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double time(time) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; time:standard_name = &quot;time&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; time:long_name = &quot;time of measurement&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; time:units = &quot;hours since 2016-06-01 00:00:00&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; time:timezone = &quot;UTC&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; time:calendar = &quot;proleptic_gregorian&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double lat(stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; lat:standard_name = &quot;latitude&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; lat:long_name = &quot;station_latitude&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; lat:units = &quot;degrees_north&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double lon(stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; lon:standard_name = &quot;longitude&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; lon:long_name = &quot;station_longitude&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; lon:units = &quot;degrees_east&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double elev(stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; elev:standard_name = &quot;altitude&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; elev:long_name = &quot;station_altitude&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; elev:units = &quot;m ASL&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double height(height) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; height:standard_name = &quot;altitude&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; height:long_name = &quot;station_altitude&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; height:units = &quot;m ASL&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double depth(depth) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; depth:standard_name = &quot;soil_depth&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; depth:long_name = &quot;soil sensor depth&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; depth:units = &quot;cm&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; char station_name(name_strlen, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; station_name:long_name = &quot;station_name&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; station_name:cf_role = &quot;timeseries_id&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double T(time, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T:standard_name = &quot;temperature&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T:long_name = &quot;2m air temperature&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T:units = &quot;degree_Celsius&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double Q(time, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Q:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Q:standard_name = &quot;mixing_ratio&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Q:long_name = &quot;2m mixing ratio&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Q:units = &quot;g kg-1&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; Q:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double ET_i(time, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_i:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_i:standard_name = &quot;evapotranspiration_intensive&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_i:long_name = &quot;lysimeter evapotranspiration intensive management&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_i:units = &quot;g kg-1 h-1&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_i:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double ET_e(time, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_e:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_e:standard_name = &quot;evapotranspiration_extensive&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_e:long_name = &quot;lysimeter evapotranspiration extensive management&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_e:units = &quot;g kg-1 h-1&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; ET_e:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double LvE_cor(time, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LvE_cor:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LvE_cor:standard_name = &quot;latent_heat_flux&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LvE_cor:long_name = &quot;energy balance corrected flux tower latent heat flux&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LvE_cor:units = &quot;W m-2&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LvE_cor:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double HTs_cor(time, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; HTs_cor:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; HTs_cor:standard_name = &quot;sensible_heat_flux&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; HTs_cor:long_name = &quot;energy balance corrected flux tower sensible heat flux&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; HTs_cor:units = &quot;W m-2&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; HTs_cor:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double GHF(time, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; GHF:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; GHF:standard_name = &quot;ground_heat_flux&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; GHF:long_name = &quot;flux tower ground heat flux&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; GHF:units = &quot;W m-2&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; GHF:positive = &quot;up&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; GHF:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double SW(time, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; SW:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; SW:standard_name = &quot;short_wave_radiation&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; SW:long_name = &quot;downward short wave radiation&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; SW:units = &quot;W m-2&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; SW:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double LW(time, stations) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LW:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LW:standard_name = &quot;long_wave_radiation&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LW:long_name = &quot;downward long wave radiation&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LW:units = &quot;W m-2&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; LW:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double VWC_25(time, depth) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_25:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_25:standard_name = &quot;volumetric_water_content&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_25:long_name = &quot;DE-Fen SoilNet volumetric water content first quartile&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_25:units = &quot;vol. %&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_25:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double VWC_50(time, depth) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_50:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_50:standard_name = &quot;volumetric_water_content&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_50:long_name = &quot;DE-Fen SoilNet volumetric water content second quartile&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_50:units = &quot;vol. %&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_50:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double VWC_75(time, depth) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_75:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_75:standard_name = &quot;volumetric_water_content&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_75:long_name = &quot;DE-Fen SoilNet volumetric water content third quartile&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_75:units = &quot;vol. %&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; VWC_75:source = &quot;TERENO-preAlpine&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double T_prof(time, height) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T_prof:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T_prof:standard_name = &quot;temperature_profile&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T_prof:long_name = &quot;DE-Fen HATPRO spline interpolated temperature profile&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T_prof:units = &quot;K&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; T_prof:source = &quot;scaleX campaign 2016&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double A_prof(time, height) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; A_prof:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; A_prof:standard_name = &quot;humidity_profile&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; A_prof:long_name = &quot;DE-Fen HATPRO spline interpolated absolute humidity profile&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; A_prof:units = &quot;kg m-3&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; A_prof:source = &quot;scaleX campaign 2016&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; double PRW(time) ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; PRW:_FillValue = -9999. ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; PRW:standard_name = &quot;precipitable_water&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; PRW:long_name = &quot;DE-Fen HATPRO column precipitable water&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; PRW:units = &quot;kg m-2&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; PRW:source = &quot;scaleX campaign 2016&quot; ;</p> <p>// global attributes:<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; :history = &quot;2019-09-12: File created.&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; :institution = &quot;Karlsruhe Institute of Technology (KIT) - Campus Alpin, Institute for Meteorology and Climate Research&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; :Contact_person = &quot;Benjamin Fersch (benjamin.fersch@kit.edu)&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; :Author = &quot;Benjamin Fersch (benjamin.fersch@kit.edu)&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; :source = &quot;https://www.tereno.net, https://scalex.imk-ifu.kit.edu&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; :Conventions = &quot;CF-1.6&quot; ;<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; :License = &quot;Creative Commons Attribution Non Commercial Share Alike 4.0 International&quot; ;</p> <p>&nbsp;</p>

opencc-by-nc-sa-4.0Sep 2019View details →
zenodo40/100

Ensemble Learning of Catchment-Wise Optimized LSTMs Enhances Regional Rainfall-Runoff modelling - Case Study: Basque Country, Spain - Data

<div> <div>This data and results are for paper: "Ensemble Learning of Catchment-Wise Optimized LSTMs Enhances Regional Rainfall-Runoff modelling - Case Study: Basque Country, Spain" by Hosseini et al. 2024 (Preprint - Under review J.Hydro 2024) Available at SSRN: <a href="https://ssrn.com/abstract=4918782" target="_blank" rel="noopener">https://ssrn.com/abstract=4918782</a></div> <div>&nbsp;</div> </div>

opencc-by-4.0Aug 2024View details →
zenodo40/100

A model of P-wave velocity beneath the greater Alpine region from teleseismic full P-waveform inversion

<p>The dataset provides values of P-wave velocity in a 3D spherical chunk beneath the greater Alpine region as they resulted from a teleseismic full waveform inversion of AlpArray data.&nbsp;</p> <p>Please find a description of the dataset in the accompanying README file.</p>

opencc-by-4.0Aug 2024View details →
zenodo40/100

Interannual Salinity Variability on the Ross Sea Continental Shelf in a Regional Ocean-Sea Ice-Ice Shelf Model

<p>This data is only used for paper submitted to the JPO &nbsp;entitled 'Interannual Salinity Variability on the Ross Sea Continental Shelf in a Regional Ocean-Sea Ice-Ice Shelf Model'.</p>

opencc-by-4.0Sep 2024View details →
zenodo40/100

Processed data used for JGR publication "Role of Midwater Mixed Waves in the Loop Current Separation Events from A Coupled Ocean-Atmosphere Regional Model and In-Situ Observations"

<p>This is the processed dataset used in the JGR publication "Role of Midwater Mixed Waves in the Loop Current Separation Events from A Coupled Ocean-Atmosphere Regional Model and In-Situ Observations" by Xiao Ge.</p> <p>Please contact the author (gexiao@tamu.edu) for all the original/processed outputs of R-CESM, and use the following original papers as citations.</p> <p>The dataset used in this research includes:</p> <p>1. Loop Current Dynamics 2009-2011: LC_*.nc is the processed (reorganized) data for each in-situ station, * represents their station ID</p> <ul> <li>https://digital.library.unt.edu/ark:/67531/metadc955416/</li> <li>https://www.sciencedirect.com/science/article/pii/S0377026516301348?via%3Dihub</li> <li>https://search.dataone.org/view/%7BBD2513E6-3B34-4B7C-BCB9-3C4ED5E8D0FB%7D</li> </ul> <p>2. Regional Community Earth System Model, R-CESM: <a href="https://zenodo.org/api/records/13932074/draft/files/h.nc/content" target="_blank" rel="noopener noreferrer">h.nc</a> is the bathymetry data of R-CESM; cmpr_*.nc files are provided as examples of the original R-CESM outputs; pvsf_prho_*.nc are the processed (subsampled at the target region and interpolated on potential density layers, derived stream function, potential vorticity, and relative vorticity) R-CESM outputs used in this research; and&nbsp;<a href="https://zenodo.org/uploads/13932074" target="_blank" rel="noopener noreferrer">LC_pv_40hlp_2013.nc</a> is the example of organized processed R-CESM (pvsf_prho_*.nc files) containing potential vorticity and relative vorticity for figures plotting</p> <ul> <li>https://journals.ametsoc.org/view/journals/bams/102/9/BAMS-D-20-0024.1.xml?tab_body=fulltext-display</li> </ul> <p>&nbsp;</p> <p>&nbsp;</p> <p>&nbsp;</p> <p>&nbsp;</p>

opencc-by-4.0Oct 2024View details →
zenodo40/100

Data and code for FishMIP global marine ecosystem model ensemble projections summarised by countries and territories and other selected marine spatial regions.

<p>R code to extract and create data tables and summary plots of FishMIP mean ensemble projections provided are for percentage change in "exploitable fish biomass", which is a proxy for the biomass available to fisheries, consisting of marine animals spanning the size range 10 g to 100 kg: this is typically dominated by fish, but is also inclusive of other animals such as crustaceans and cephalopods.</p> <p>This release contains scripts and summary data for producing figures in Part A of the following report:</p> <p>Blanchard, J.L., Novaglio, C., eds. (2024). Climate change risks to marine ecosystems and fisheries: Future projections from the Fisheries and Marine Ecosystems Model Intercomparison Project. FAO Fisheries and Aquaculture Technical Paper No. 707. Rome, FAO.</p> <p>Please refer to the above report to cite and for more information.</p> <p>The summary data are here:</p> <p>https://github.com/Fish-MIP/FAO_Report/blob/main/data/table_stats_formatted_admin_full.csv</p> <p>Where the column 'spatial_scale' refers to the type of aggregation:</p> <p>FAO_area = High Sea areas grouped by FAO Major Fishing Areas</p> <p>countries = Exclusive Economic Zones</p> <p>countries_admin = Exclusive Economic Zones results aggregated into Administrative Countries</p> <p>Please note that these results can also be visualised and downloaded from our shiny app: https://rstudio.global-ecosystem-model.cloud.edu.au/shiny/FAO_report_shiny/</p> <p>&nbsp;</p>

openapache2.0Oct 2024View details →
zenodo40/100

Model fields supporting the publication "Integrated Assessment of the Risks to Ocean Acidification in the Northern High Latitudes: Regional Comparison of Exposure, Sensitivity and Adaptive Capacity of Pelagic Calcifiers"

<p>These are&nbsp;the&nbsp;model outputs supporting the&nbsp;described manuscript. They include&nbsp;monthly averaged output of aragonite saturation state for each year during the 10-year hindcast.&nbsp;Also included is the&nbsp;particle tracking output, for both the Bering Sea and the Gulf of Alaska,&nbsp;as described in the manuscript.</p>

opencc-by-4.0Jul 2021View details →
zenodo40/100

Identification and Regionalization of Streamflow Routing Parameters for the HLM Hydrological Model in Iowa

<p>The tables contained the metrics and the peak flows estimated using HLM under three different parameterizations.&nbsp;</p>

opencc-by-4.0Sep 2021View details →
zenodo40/100

The ocean tide model CIAM2 for the Canary Islands region (Spain)

<p>Data set of Ocean tide Model for harmonic M2, in the Canary Islands region (Spain).&nbsp;</p> <p>This ocean tide model has been used in the paper &quot;Anelastic response of the Earth&#39;s crust underneath the Canary Islands revealed from ocean tide loading observations&quot;, by Jose Arnoso, Machiel S. Bos, Maite Benavent, Nigel T. Penna, Sergio Sainz-Maza, submitted to Geophysical Journal International, 2022.&quot;<br> &nbsp;</p>

opencc-by-4.0Jan 2023View details →
zenodo40/100

Tables and Data for "Synthesis of Satellite and Surface Measurements, Model Results, and FRAPPÉ Study Findings to Assess the Impacts of Oil and Gas Emissions Reductions on Maximum Ozone in the Denver Metro and Northern Front Range Region in Colorado"

<p>These are data sets and tables used in the paper &quot;Synthesis of Satellite and Surface Measurements, Model Results, and FRAPP&Eacute; Study Findings to Assess the Impacts of Oil and Gas Emissions Reductions on Maximum Ozone in the Denver Metro and Northern Front Range Region in Colorado&quot; to be submitted to Earth and Space Science. The monitor site 2016 and 2017 counts files have gridded HYSPLIT back trajectory counts for the 4 highest ozone concentration days at each site, as described in the manuscript.</p>

opencc-by-4.0Feb 2023View details →
zenodo40/100

Regional climate model simulations (CCLM 15km) of profiles for the MOSAiC period

<p>The ship-based experiment MOSAiC 2019/2020 was carried out during a full year in the Arctic. The data set includes simulation data of profiles and derived data for the MOSAiC period (Oct. 2019-Sept.2020). The regional climate model CCLM was used in a forecast mode (nested in ERA5) for the whole Arctic with 15 km resolution and is run with different configurations of sea ice data. These include the standard sea ice concentration taken from passive microwave data (AMSR2) with around 6 km resolution, and sea ice concentration from Moderate Resolution Imaging Spectroradiometer (MODIS) thermal infrared data and MODIS sea ice lead data with 1 km resolution for the winter period (Nov. 2019-April 2020). Model output is available every 1h. In the vertical, the model extends up to 22 km with 60 vertical levels. On data below 10km are used. In addition to profiles, integrated water vapour and temperature for the lowest 2km were calculated. Values are grid-box averages at the ship position. Geostrophic wind was computed from the pressure gradient of the four surrounding grid points.</p> <p>Reference: Heinemann, G., Schefczyk, L., Willmes, S., Shupe, M., 2022: Evaluation of simulations of near-surface variables using the regional climate model CCLM for the MOSAiC winter period. Elem. Sci. Anth., 10 (1). DOI: 10.1525/elementa.2022.00033.</p> <p><strong>Project:&nbsp;</strong> Modelling the impact of sea-ice leads on the atmospheric boundary layer during MOSAiC (MISLAM)</p> <p><strong>Funding: </strong>Federal Ministry of Education and Research (BMBF), grant 03F0887A</p>

openDec 2022View details →
zenodo40/100

MESMAR v1: A new regional coupled climate model for downscaling, predictability, and data assimilation studies in the Mediterranean region. Article data

<p>Regional coupled and Earth System models are fundamental numerical tools for climate investigations, downscaling of&nbsp;predictions and projections, process-oriented understanding of regional extreme events, and many more applications. Here we&nbsp;introduce a newly developed coupled regional modeling framework for the Mediterranean region, called MESMAR&nbsp;(Mediterranean Earth System model at ISMAR) version 1, which is composed of the WRF atmospheric model, the NEMO oceanic&nbsp;15 model, and the HD hydrological discharge model, coupled via the OASIS coupler. The model is implemented at moderate&nbsp;resolution (about 1/12&deg; for the ocean and river routing, while twice coarser for the atmosphere) for long-term investigations.</p> <p>The gzipped tarball contains data files contained in the manuscript associated with the MESMARv1 description and&nbsp;submitted to Geoscientific Model Developments:</p> <p>MESMAR v1: A new regional coupled climate model for downscaling,&nbsp;predictability, and data assimilation studies in the Mediterranean region</p> <p>by&nbsp;Andrea Storto, Yassmin Hesham Essa, Vincenzo de Toma, Alessandro Anav, Gianmaria Sannino,<br> Rosalia Santoleri, Chunxue Yang</p>

opencc-by-4.0May 2023View details →
zenodo40/100

Output raster datasets from Apalachicola Regional Restoration Initiative Open Pine Ecological Condition Model (2023)

<p>Output raster datasets from the 2023&nbsp;Ecological Condition Model (ECM) for open pine ecosystems in the&nbsp;Apalachicola Regional Restoration Initiative (ARRI) area of the eastern Florida Panhandle.&nbsp;Our goal was to develop an&nbsp;ECM that would span all lands in the Apalachicola Regional Restoration Initiative (ARRI) area. As such, we used only datasets that were available throughout this region and did not rely on any corporate data layers from specific landowners. Furthermore, we sought to assess ecological condition at a high enough resolution to inform management decisions down to the level of individual forest stands. By taking this approach, we hoped to create ecological condition scores that could be used to inform restoration activities across all lands, and which could be updated through time to measure progress and to gauge the effectiveness of management activities.</p> <p>Output raster datasets include ecological condition for canopy, midstory and groundcover/shrub layers as well as overall ecological condition. Each raster contains ranked scores of estimated ecological condition: 1- Excellent, 2- Good, 3-Fair, and 4-Poor.&nbsp;</p> <p>NOTE- These outputs were created using tools stored in this repository:&nbsp;<a href="https://doi.org/10.5281/zenodo.8236853">https://doi.org/10.5281/zenodo.8236853</a>&nbsp;as well as&nbsp;several raster input layers stored in this repository: https://doi.org/10.5281/zenodo.8234220.&nbsp;</p> <p>&nbsp;</p> <p>&nbsp;</p> <p>&nbsp;</p>

opencc-by-4.0Aug 2023View details →
zenodo40/100

Input raster datasets for Apalachicola Regional Restoration Initiative Open Pine Ecological Condition Model (2023)

<p>Input raster datasets used to create an Ecological Condition Model (ECM) for open pine ecosystems in the&nbsp;Apalachicola Regional Restoration Initiative area of the eastern Florida Panhandle.&nbsp;Our goal was to develop an&nbsp;ECM that would span all lands in the Apalachicola Regional Restoration Initiative (ARRI) area. As such, we used only datasets that were available throughout this region and did not rely on any corporate data layers from specific landowners. Furthermore, we sought to assess ecological condition at a high enough resolution to inform management decisions down to the level of individual forest stands. By taking this approach, we hoped to create ecological condition scores that could be used to inform restoration activities across all lands, and which could be updated through time to measure progress and to gauge the effectiveness of management activities.</p> <p>&nbsp;</p> <p>&nbsp;</p>

opencc-by-4.0Aug 2023View details →
zenodo40/100

The Styrene, Benzene, Toluene, Ethylbenzene and Xylenes (SBTEX) hourly gridding modeled emission and concentration in the U.S. Gulf region

<p>This Data includes three parts, the emission data, concentration data, and evaluation process for Styrene, Benzene, Toluene, Ethylbenzene, and Xylenes (SBTEX). We did the imputation for the missing emissions in the 2011 National Emission Inventory (NEI) and used the SMOKE model system (<a href="https://www.cmascenter.org/smoke/">https://www.cmascenter.org/smoke/</a>) to generate the hourly gridding (12x12km) explicit emission data (2012_May_to_SEP_SBTEX_emission_12K.zip). Then, we drove the CTM model (Comprehensive Air Quality Model with Extensions, CAMx model&nbsp;<a href="https://www.camx.com/">https://www.camx.com/</a>) with flexi-nesting to (4x4km) and the reactive tracer method, which considers the chemical decay, physical transport, and dry/wet deposition processes, with the complete imputed spatiotemporal SBTEX emission data to simulate the individual hourly SBTEX concentration accurately. The concentration data (2012_May_to_SEP_SBTEX_concentration_4K.zip) is hourly gridding modeled concentration data in netCDF format for Gulf region states in the US for 2012 May to Sep. The concentration and emission data are in netCDF format and have two scenarios, original emission data (Base case) and complete imputed emission (Adj case). In the concentration data, BENZENE and BENZ are two variables to represent Benzene species; however, only &quot;BENZ&quot; in Adj case is recommended to use. The Adj case results are also recommended for other species. The file (Model_evaluation_support_document) has the codes for evaluating the model result with observation data&nbsp;<a href="https://www.epa.gov/amtic/amtic-ambient-monitoring-archive-haps">https://www.epa.gov/amtic/amtic-ambient-monitoring-archive-haps</a>. The SBTEX concentration can support any SBTEX-related human health studies in the Gulf region.In this release version (Aug. 30, 2023), we have included the complete 2012 SBTEX concentration data (Adj case) at a 4km x 4km resolution in three different formats: ioapi (2012_SBTEX_conc_ioapi.zip), netCDF-CF (2012_SBTEX_conc_NetCDF_CF.zip), and csv (2012_SBTEX_conc_Hourly_csv_data.zip). Additionally, we have provided the Readme files for both concentration and emission data: &quot;Readme_for_2012_SBTEX_conc_4km_conc.txt&quot; and &quot;Readme_for_2012_emission_12km.txt&quot;.</p>

opencc-by-4.0May 2023View details →
zenodo40/100

Graphic representation of data set for the project "IRI model performance evaluation for the Mexican region"

<p>Here, we illustrate the modeling and experimental results for vertical Total Electron Content (TEC) over Mexico during the five year period 2018-2022. The results were obtained for the UCOE GNSS receiver station (geographic coordinates: 19.6&deg;N; 101.68&deg;W ). The calculations were made each two hours during the whole period under considerations. The modeling results were obtained using the &quot;International Reference Ionosphere (IRI)&quot; model, which is an empirical climatological model based on ground and space observations of the ionosphere [Bilitza et al., 2022].</p>

opencc-by-4.0Sep 2023View details →
dryad40/100

A high-resolution regional data-assimilative ocean modeling output near Cape Hatteras in 2017

Open the record for dataset details and reuse information.

publicApr 2024View details →
dryad40/100

Climate model experiments of regional-scale tree die-off replaced by shrubs (all monthly data fields): Part 3

Open the record for dataset details and reuse information.

publicApr 2025View details →
dryad40/100

A high-resolution regional data-assimilative ocean modeling output near Cape Hatteras in 2018

Open the record for dataset details and reuse information.

publicApr 2024View details →

ScienceDex guides

Understand access before you commit

These curated guides explain access requirements, typical timelines, costs, and reuse considerations for widely used research datasets.

Compare curated 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.

allen-brain-atlas
neuroscienceopenDocumentation, web resources, and API references are available online.
Last verified 2026-04-30Open record

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.

abode-home-cage
behavioral-neuroscienceopenThe DataShare record exposes download links for annotations, documentation, license text, and the zipped per-snippet data directory.
Last verified 2026-04-30Open record

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.

dandi-nwb
electrophysiologyopenPublished Dandiset metadata and archive endpoints are available through the production DANDI API.
Last verified 2026-04-30Open record

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.

ibl
behavioral-neuroscienceopenPublic sessions can be searched and loaded from the IBL public data server through ONE.
Last verified 2026-04-29Open record

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.

openneuro
neuroscienceopenPublished datasets are available on demand over the internet.
Last verified 2026-04-29Open record