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15 results for “regional atmospheric model”

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zenodo48/100

Atmospheric climate model output of the COSMO-CLM2 regional climate model hindcast run over Antarctica (1987-2016)

<p>The dataset contains monthly output of a&nbsp;COSMO-CLM&sup2; (COSMO-CLM coupled to the Community Land Model) atmospheric hindcast simulation over Antarctica which is described&nbsp;and evaluated in the following paper:&nbsp;</p> <p>Souverijns, N., Gossart, A., Demuzere, M., Lenaerts, J.T.M., Medley, B., Gorodetskaya, I.V., Vanden Broucke, S., van Lipzig, N.P.M., 2019. A new Regional Climate Model for POLAR-CORDEX: Evaluation of a 30-year Hindcast with COSMO-CLM&sup2; over Antarctica. Journal of Geophysical Research: Atmospheres, 124, 1405-1427. (doi:10.1029/2018JD028862)</p> <p>Details of the model simulation:<br> - COSMO-CLM version&nbsp;5.0_clm6<br> - Community Land Model version 4.5<br> - Horizontal resolution: 0.25&deg;x0.25&deg;<br> - Vertical resolution: 40 levels<br> - Time period: 1987-2016 (excluding&nbsp;4 years of spin-up)<br> - Driving model: ERA-Interim<br> &nbsp;</p> <p>The data provided here has a monthly time resolution and contains the monthly average of all variables except denoted otherwise below. As such, each file consists of 360 time steps.<br> - AEVAP_S: Surface evaporation [kg m-2] (summed value for each month)<br> - ALB: Surface albedo [-] (only for austral summer months)<br> - ALHFL_S: Surface latent heat flux [W m-2]<br> - ALWD_S: Downward longwave radiation at the surface [W m-2]<br> - ALWU_S: Upward longwave radiation at the surface [W m-2]<br> - ASHFL_S: Surface sensible heat flux [W m-2]<br> - ASOB_S: Surface net downward shortwave radiation [W m-2]<br> - ASWDIFD_S: Diffuse downward shortwave radiation at the surface [W m-2]<br> - ASWDIFU_S: Diffuse upward shortwave radiation at the surface [W m-2]<br> - ASWDIR_S: Direct downward shortwave radiation at the surface [W m-2]<br> - ATHB_S: Surface net downward longwave radiation at the surface [W m-2]<br> - P: Pressure at 40 vertical levels [Pa]<br> - QV: Specific humidity at 40 vertical levels [kg kg-1]<br> - RH2M: Relative humidity at 2 meter [%]<br> - SNOW_GSP: Surface snowfall amount [kg m-2]&nbsp;(summed value for each month)<br> - T2M: Temperature at 2 meter [K]<br> - T: Temperature at 40 vertical levels [K]<br> - WS10M: Wind speed at 10 meter [m s-1]<br> - WS: Wind speed at 40 vertical levels [m s-1]</p>

opencc-by-4.0Jan 2019View details →
zenodo44/100

The third Met Office Unified Model-JULES Regional Atmosphere and Land configuration, RAL3

<p>Supporting data for figures in GMD draft paper: The third Met Office Unified Model-JULES Regional Atmosphere and Land configuration, RAL3</p>

opencc-by-4.0Oct 2024View details →
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

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 →
zenodo32/100

Regional Atmospheric Climate Model 2 (RACMO2), version 2.3p2

<p>In the 1990s the KNMI developed in cooperation with the Danish Meteorological Institute the research model RACMO based on the High Resolution Limited Area Model (HIRLAM) numerical weather prediction model. In 1993 UU/IMAU started to modify the model such that it better represented the extreme conditions over glacier surfaces. This first version of RACMO, RACMO1, combined the dynamical core of the HIRLAM model with ECHAM4 physics. The polar modified version of RACMO1 was mainly applied to the Antarctic Ice Sheet.</p> <p>The second version, RACMO2, combines the dynamical core of the HIRLAM model with the European Centre for Medium-range Weather Forecasts (ECMWF) Integrated Forecast System (ISF) physics. RACMO versions 2.0 and 2.1 included HIRLAM version 5.0.6 and ISF cycle CY23r4, while version 2.3 includes HIRLAM version 6.3.7 and cycle CY33r1. Due to the rapid increase in computer capacity over the years, these versions of RACMO have not only been applied to the Greenland and Antarctic Ice Sheets, but also at higher resolution to smaller areas such as Dronning Maud Land and Patagonia.</p> <p>For the RACMO model in general the grids are defined over the equator and then rotated to the area of interest. Grid distance is defined in fraction of degrees, which results in near equidistant grid points as long as the domain is small enough. Note that the domain is thus not on a (polar) stereographic projection plane. In the vertical, the model adopts a system of hybrid sigma levels, which evolve from terrain-following sigma levels close to the surface to pure pressure levels at higher elevation. The actual number of horizontal grid points varies per model run; in most simulations, 40 vertical layers were used.</p> <p>Since RACMO is a regional model, it needs external information at the lateral boundaries and sea surface. At the lateral boundary zone of the model, the temperature, specific humidity, zonal and meridional wind components, and the surface pressure are relaxed towards the fields of a global model every 6 model hours, as are the sea surface temperature and sea ice concentration. RACMO is not forced at the model top. The interior of the model is not nudged towards observations and allowed to evolve freely.</p>

opencc-by-4.0Feb 2020View details →
zenodo32/100

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&nbsp;repository provides&nbsp;the test simulation data for &quot;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&ndash;meteorology interactions&quot; 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>

opencc-by-4.0Jun 2021View details →
zenodo32/100

Electron concentration profiles calculated using eight-component model of the ionospheric D-region and two different set of input atmospheric data

<p>The files contain electron concentration <i>Ne</i> profiles during solar X-ray flares&nbsp;that occurred on 9-11&nbsp;June 2014. The altitude range is 50-90 km.</p><p>Values of&nbsp;electron concentration were calculated using eight-component model of the ionospheric D-region and two different set of input atmospheric data (MSIS neutral atmosphere model and Aura satellite measurements). Results are obtained&nbsp;for ten VLF paths: from European transmitters ICV, TBB, GQD, GBZ, and DHO to Mikhnevo geophysical observatory (55°N 38°E) and A118 SID station (43°N 1°E).</p><p>The data is presented as MATLAB files. Each .mat file&nbsp;contains data and&nbsp;variable "description" with data's structure information.</p>

opencc-by-4.0Oct 2023View details →
zenodo32/100

Dataset for "Enhanced Regional Ocean Ensemble Data Assimilation Through Atmospheric Coupling in the SKRIPS Model"

Open the record for dataset details and reuse information.

opencc-by-4.0Dec 2023View details →
zenodo32/100

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. &nbsp;Also included are the model output files for all cases and grid configurations used to generate the analysis and figures in the paper.&nbsp;</p>

opencc-by-4.0Mar 2024View details →
zenodo32/100

Kilometre-scale regional climate model simulations of two atmospheric river case studies in West Antarctica

<p>Regional climate model simulations produced using the MetUM, HCLIM and Polar-WRF models at 1 km horizontal grid spacing. The data span two case studies in which an atmospheric river made landfall over the Amundsen Sea Embayment and Thwaites / Pine Island ice shelves. The first is a winter case (23-30 June 2020) and the second a summer case (3-9 February 2020).&nbsp;</p> <p>Data are gridded, in native model coordinates, and saved as netcdf.</p> <p>Data produced by:</p> <p>HCLIM: Jos&eacute; Abraham Torres</p> <p>MetUM: Ella Gilbert</p> <p>Polar-WRF: Denys Pishniak</p> <p>Data were produced to support the analysis presented in Gilbert et al. (2024) [preprint] . The research was funded by the PolarRES project, which is funded under the EU's Horizon 2020 programme call H2020-LC-CLA-2018-2019-2020 under grant agreement 101003590. MetUM simulations were performed on the ARCHER2 UK National Supercomputer.&nbsp;</p>

opencc-by-4.0Jul 2024View details →
zenodo32/100

Model output for analyses in Qian et al. (2023) "Region and cloud regime dependences of parametric sensitivity in E3SM Atmosphere Model".

<p>This archive contains&nbsp;post-processed data for analyses in Qian et al. (2023) &quot;<strong>Region and cloud regime dependences of parametric sensitivity in E3SM Atmosphere Model</strong>&quot;.&nbsp;&nbsp;These data are model output from the&nbsp;Perturbed Parameters Ensemble (PPE) simulations conducted with DOE&#39;s E3SM Atmosphere Model&nbsp;Version 1 (EAMv1).&nbsp; There are&nbsp;256x12 PPE simulations with each simulation run for 5 days&nbsp;following the approach of Cloud Associated Parameterization Testbed (CAPT) and transpose Atmosphere Model Intercomparison Project (AMIP) paradigm. The diagnostic variables associated with&nbsp;the parameterizations of turbulence and shallow convection, deep convection, cloud microphysics, and gravity wave drag at day 5 are saved and used for analyses in this paper.&nbsp;</p>

opencc-by-4.0Feb 2023View details →
zenodo32/100

Dataset for JGR Atmospheres manuscript " An Observational Constraint of VOC Emissions for Air Quality Modeling Study in the Pearl River Delta Region"

<p>The file&nbsp;includes&nbsp;hourly observations of ground-level ozone (O<sub>3</sub>) and nitrogen dioxide (NO<sub>2</sub>) concentrations at 56 environmental monitoring stations in the Pearl River Delta (PRD) region in China during June 2018. The units are in&nbsp;&mu;g/m<sup>3</sup>.</p>

opencc-by-4.0May 2023View details →
zenodo28/100

Data supporting "Modeling and evaluating the effects of irrigation on land-atmosphere interaction in southwestern Europe with the regional climate model REMO2020-iMOVE using a newly developed parameterization"

<p>This data supports the analysis of the manuscript Asmus et al. 2023 "Modeling and evaluating the effects of irrigation on land-atmosphere interaction in southwestern Europe with the regional climate model REMO2020-iMOVE using a newly developed parameterization".</p><p><strong>Simulation data</strong></p><p>The simulation data is created with REMO2020-iMOVE using the new irrigation parameterization. The results are saved as NetCDF files with monthly mean values and/or time series (hourly) of single variables for the analysis period. A list of the simulations can be found below.</p><p><strong>Observation data&nbsp;</strong></p><p>The observation data is published with the kind permission of ISPRA which hosts the SCIA database (www.scia.isprambiente.it). If you use this data, please make sure to include the following data source:<br>SCIA by ISPRA - Area Climatologia operativa - Via V. Brancati 48 00144 Roma.&nbsp;<br>We downloaded monthly mean values for the variables T2Max, T2Min and T2Mean from&nbsp;<a href="http://193.206.192.214/servertsutm/serietemporali100.php">http://193.206.192.214/servertsutm/serietemporali100.php </a>(last accessed on 14/10/2022) to verify the model results with and without irrigation parameterization.&nbsp;</p><p>By untarring the tarballs, the data structure is created that is necessary to execute the analysis scripts.</p><p>For more information or additional data please contact the author.</p><p>&nbsp;</p><p><strong>tarball &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; | exp_number | description&nbsp;</strong></p><p>067015.tar.gz &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| &nbsp;067015 &nbsp; &nbsp; &nbsp; &nbsp; | not irrigated</p><p>067016.tar.gz &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| 067016 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| irrigated with "adaptive water application scheme"</p><p>067017.tar.gz &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| 067017 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| irrigated with "adaptive water application scheme"</p><p>067019.tar.gz &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| 067019 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| irrigated with "flexible time water application scheme"</p><p>067020.tar.gz &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| 067020 &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| irrigated with "prescribed water application scheme"</p><p>observation_scia.tar &nbsp;| - &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp; &nbsp;| observation data from SCIA&nbsp;</p><p>&nbsp;</p><p><strong>Data structure for simulation data</strong></p><p>\&lt;exp_number&gt;<br>&nbsp;&nbsp; \monthly<br>&nbsp;&nbsp; \hourly<br>&nbsp;&nbsp;&nbsp; &nbsp; \var_series<br>&nbsp;&nbsp; &nbsp; &nbsp;&nbsp;&nbsp; \&lt;variable&gt;<br><br>Note:<br>\067015 includes static variables<br>&nbsp;&nbsp; \irrifrac (irrigated fraction)<br>&nbsp;&nbsp; \bla (land-sea-mask)</p>

opencc-by-4.0Oct 2023View details →
zenodo24/100

The second Met Office Unified Model-JULES Regional Atmosphere and Land configuration, RAL2

<p>Supporting data for figures in GMD draft paper:&nbsp;The second Met Office Unified Model-JULES Regional Atmosphere and Land configuration, RAL2</p> <p>&nbsp;</p> <p>&nbsp;</p>

opencc-by-4.0Mar 2023View details →

ScienceDex guides

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

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Allen Brain Atlas

Allen Brain Atlas is an Allen Institute collection of brain map atlases, datasets, APIs, and analysis tools covering mouse, human, and non-human primate brain resources.

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