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zenodo52/100

Global Extra-tropical Circulation Database based on the Jenkinson-Collison Classification calculated with 6-hourly mean sea-level pressure fields from various reanalysis datasets

<h1>Dataset Description</h1> <p>Global Extra-tropical Circulation Database based on the Jenkinson-Collison Classification calculated with 6-hourly mean sea-level pressure fields from several reanalysis datasets. This dataset is the result of an extension of the Jenkinson-Collison circulation type classification to the entire globe, including a modification of its original formulation for the southern hemisphere.</p> <p>A modified version of the IPCC-AR6 Reference Regions that excludes the intertropical range where the method is not applicable is also included, as used in the reference paper for global assessment.</p> <p>Further details in <a href="https://doi.org/10.1007/s00382-022-06658-7" target="_blank" rel="noopener">https://doi.org/10.1007/s00382-022-06658-7&nbsp;</a></p> <h2>Note for version 1.1.0</h2> <p>This version corrects an issue in the previous release, which was incorrectly labeled as <em>version 0.1</em>. That version was incomplete due to the omission of previously existing files, and should be considered <strong>incomplete</strong>. Version 1.1.0 restores all original files alongside the newly added one, ensuring the dataset is now complete and consistent. We apologize for any inconvenience this may have caused and appreciate your understanding.</p>

opencc-by-4.0Dec 2021View details →
zenodo52/100

A vertically-resolved atmospheric dust reanalysis for Mars Years 28-29 using Analysis Correction

<p>This is a dataset of meteorological variables for the atmosphere of Mars, obtained by assimilating measurements (retrievals) of atmospheric temperature and dust opacity into a 3-dimensional, time-dependent numerical model of the Martian atmospheric circulation (known as a &ldquo;reanalysis&rdquo;).</p> <p>The observations come from two spacecraft - the Mars Climate Sounder (MCS) instrument on board NASA&rsquo;s Mars Reconnaissance Orbiter (e.g. Kleinboehl et al. 2009) and the Thermal Emission Imaging Spectrometer (THEMIS) on board NASA&rsquo;s Mars Odyssey spacecraft, and cover the period from 21 September 2006 until&nbsp; 5 November 2009 (Mars Years 28:Ls=109.98 - 30:Ls=4.78). MCS observations include profiles of temperature and dust opacity from near the surface up to altitudes of around 80 km obtained from infrared limb-sounding (MCS version 3 retrievals, based on opacities at around 21.6 micron wavelengths), while THEMIS measurements are of column dust opacity in the infrared (centred around 9.3 micron wavelength). Further details can be found on the websites</p> <p>https://pds-geosciences.wustl.edu/missions/odyssey/themis.html,<br> https://atmos.nmsu.edu/data and services/atmospheres data/MARS/aerosols.html</p> <p>The model into which the observations are assimilated is the UK version of Laboratoire de M&eacute;t&eacute;orologie Dynamique Mars Global Circulation Model (LMDMGCM), a 3-dimensional, time-dependent numerical circulation model of the Martian atmosphere and near-surface environment, simulating the changing winds, temperature, pressure and dust content of the atmosphere across the whole planet. The model solves the equations of motion, mass and energy conservation using a spherical harmonic representation in the horizontal and finite difference formulation in the vertical direction, but outputs the data here on a regular longitude-latitude grid with 72 points in longitude, 36 points in latitude and 25 terrain-following sigma levels in the vertical direction (where sigma = pressure/surface pressure) on a stretched vertical grid that extends from the surface to an altitude of approximately 100 km. More details can be found in publications by Forget et al. (1999), Newman et al. (2001), Mulholland et al. (2013).</p> <p>The observations and model are linked by an assimilation scheme, based on the Analysis Correction (AC) algorithm developed by Lorenc et al. (1991) and adapted for Mars by Lewis et al. (2007). Previous reanalyses of Mars observations using this scheme include the MACDA dataset (Montabone et al. 2014) and OPENMars (Holmes et al. 2020). This new dataset, however, makes use of an extension of the AC scheme to enable assimilation of both column integrated dust opacity measurements and dust opacity profiles in the vertical direction (see Ruan et al. 2021). This new dataset therefore provides a more realistic representation of the distribution of dust loading in the Martian atmosphere than previous work, which may also result in improved representation of other meteorological variables, notably temperature.</p> <p>Data are provided as 2D and 3D fields of variables in netCDF format as generated by the numerical model on the (longitude, latitude, sigma) grid at 2-hourly intervals. Each file contains 360 time steps covering 30 Martian days or sols. The variables contained in each file are as follows:</p> <p>&nbsp;Variables and attributes<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; lon:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72) = FLOAT(lon)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; long_name: longitude<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: degrees_east<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; lat:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(36) = FLOAT(lat)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; long_name: latitude<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: degrees_north<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 2&nbsp; sigma:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(25) = FLOAT(sigma)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; long_name: sigma<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: sigma_level = p/ps<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 3&nbsp; soil:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(18) = FLOAT(soil)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; long_name: soil levels (i.e. levels below the surface to represent thermal variations)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: none<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 4&nbsp; time:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(360) = FLOAT(time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; long_name: model time<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: days since 00:00:00 (the beginning of the file)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 5&nbsp; controle:&nbsp;&nbsp;&nbsp; FLOAT(100) = FLOAT(lentable)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; long_name: Table of run parameters<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; description:&nbsp; MGCM run&nbsp;&nbsp;&nbsp; 5.000<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 6&nbsp; Ls:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(360) = FLOAT(time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: Solar longitude (such that Ls=0 is northern Spring equinox)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: deg<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 7&nbsp; tsurf:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: Surface temperature<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: K<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 8&nbsp; ps:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: surface pressure<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: Pa<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 9&nbsp; co2ice:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: co2 ice thickness (column mass density)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: kg.m-2<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 10&nbsp; fluxsurf_lw: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: fluxsurf_lw (surface infrared radiative flux)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: W.m-2<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 11&nbsp; fluxsurf_sw: FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: fluxsurf_sw (surface visible radiative flux)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: W.m-2<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 12&nbsp; temp:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: temperature<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: K<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 13&nbsp; u:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: Zonal (east-west) wind<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: m.s-1<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 14&nbsp; v:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: Meridional (north-south) wind<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: m.s-1<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 15&nbsp; rho:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: density<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: kg.m-3<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 16&nbsp; udrag:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: Drag velocity<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: m/s<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 17&nbsp; udragt:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: Threshold velocity for dust lifting<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: m/s<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 18&nbsp; aerosol:&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: dust opacity considering layer thickness<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: SI (opacity/m)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 19&nbsp; taudustvis:&nbsp; FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: Dust optical depth<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: SI<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 20&nbsp; q01:&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; FLOAT(72,36,25,360) = FLOAT(lon,lat,sigma,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: mix. ratio<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: kg/kg<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 21&nbsp; dqsdevtot:&nbsp;&nbsp; FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: dust devil lift rate<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: kg.m-2.s-1<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 22&nbsp; dqsstrtot:&nbsp;&nbsp; FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: near surface wind stress dust lifting rate<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: kg.m-2.s-1<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 23&nbsp; dqssedtot:&nbsp;&nbsp; FLOAT(72,36,360) = FLOAT(lon,lat,time)<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 0&nbsp; Physics_diagnostic: dust sedimentation rate<br> &nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp;&nbsp; 1&nbsp; units: kg.m-2.s-1</p> <p>&nbsp;</p>

opencc-by-4.0Oct 2021View details →
edi52/100

Genetic assignments for Spring Evolutionary Significant Unit reanalysis, Central Valley Chinook Salmon populations, CA, 2011-2024

Central Valley Chinook Salmon populations differ in their Endangered Species Act listing status. It is difficult to visually distinguish individuals from the different Evolutionarily Significant Units (ESU). As such, many of the salmon monitoring and evaluation efforts in the Central Valley and San Francisco Bay-Delta are hampered by uncertainty about population (stock) identification and proportional effects of management actions (Dekar et al. 2013; IEP 2019). Studies have identified that the current identification method (length-at-date models) of juvenile Chinook salmon (Fisher 1992) captured in the watershed vary in their accuracy, particularly for spring-run (NMFS 2013; Harvey et al. 2014; Merz et al. 2014). The inaccuracy of the size-based methods is likely due to differences in fish distribution during early rearing, habitat-specific growth rates, and inter-annual variability in temperatures and food availability that lead to overlap in size ranges among stocks. The primary objective of this project was the genetic classification (to genetic lineage; Evolutionary Significant Unit) of Chinook Salmon captured from State Water Project and Central Valley Project fish protection facilities and Interagency Ecological Program compliance monitoring programs. The genetic lineage was determined for sampled fish by comparing their genotypes to reference genetic baselines. Genetic methods, having less statistical uncertainty that size-based models for population identification, were intended to directly target (and reduce) one source of uncertainty in the estimation of loss (take) from water diversions (operations) and develop the information necessary for understanding stock-specific distribution, habitat utilization, abundance, and life history variation. This project supports recommendations from the Interagency Ecological Program’s Salmon and Sturgeon Assessment of Indicators by Life Stage and Interagency Ecological Program Science Agenda efforts to improve Central V

openCC (other)Oct 2025View details →
zenodo48/100

ERA-5 reanalysis results interpolated onto the five-minute average cruise track of the Antarctic Circumnavigation Expedition (ACE) during the austral summer of 2016/2017.

<p><strong>Dataset abstract</strong></p> <p>ERA-5 fields at 1-hour temporal and grid size of 0.25&deg; x 0.25&deg; (0.5&deg; x 0.5&deg; for wave variables) have been downloaded from <a href="https://cds.climate.copernicus.eu/api/v2/resources/reanalysis-era5-single-levels">https://cds.climate.copernicus.eu/api/v2/resources/reanalysis-era5-single-levels</a>.</p> <p>The data are interpolated using two methods:</p> <p>&#39;nearest&#39;: the value of the nearest ERA-5 grid cell is use;</p> <p>&#39;linear&#39;: the values from the nearest grid cells in space and time are linearly interpolated to the [date_time, latitude, longitude] coordinate of the ship</p> <p>providing a number of atmospheric, land and oceanic climate variables interpolated along the five-minute cruise track.</p> <p>The data repository can be checked out at: <a href="https://renkulab.io/gitlab/ACE-ASAID/ecmwf-interpolation-to-cruise-track">https://renkulab.io/gitlab/ACE-ASAID/ecmwf-interpolation-to-cruise-track</a></p> <p><strong>Dataset contents</strong></p> <ul> <li>era5-on-cruise-track-5min-legs0-4-linear.csv, data file, comma-separated values</li> <li>era5-on-cruise-track-5min-legs0-4-nearest.csv, data file, comma-separated values</li> <li>interpolate-to-shiptrack.py, processing script, text/x-python</li> <li>download-ecmwf.ipynb, processing script, application/x-ipynb+json</li> <li>ecwmf_interpolate.zip, processing scripts, zip file</li> <li>data_file_header, metadata, text format</li> <li>README.txt, metadata, text format</li> </ul> <p><strong>Dataset license</strong></p> <p>This interpolation of the ERA-5 reanalysis output to the five-minute averaged cruise track and velocity dataset is made available under the Creative Commons Attribution 4.0 International License (CC BY 4.0) whose full text can be found at <a href="https://creativecommons.org/licenses/by/4.0/">https://creativecommons.org/licenses/by/4.0/</a></p>

opencc-by-4.0May 2020View details →
zenodo48/100

Data from 'Local Regions Associated With Interdecadal Global Temperature Variability in the Last Millennium Reanalysis and CMIP5 Models'

<p><strong>Abstract from &#39;<em>Local Regions Associated With Interdecadal Global Temperature Variability in the Last Millennium Reanalysis and CMIP5 Models</em>&#39;:</strong></p> <p>Despite the importance of interdecadal climate variability, we have a limited understanding of which geographic regions are associated with global temperature variability at these timescales. The instrumental record tends to be too short to develop sample statistics to study interdecadal climate variability, and Coupled Model Intercomparison Project, Phase 5 (CMIP5) climate models tend to disagree about which locations most strongly influence global mean interdecadal temperature variability. Here we use a new paleoclimate data assimilation product, the Last Millennium Reanalysis (LMR), to examine where local variability is associated with global mean temperature variability at interdecadal timescales. The LMR framework uses an ensemble Kalman filter data assimilation approach to combine the latest paleoclimate data and state-of-the-art model data to generate annually resolved field reconstructions of surface temperature, which allow us to explore the timing and dynamics of preinstrumental climate variability in new ways. The LMR consistently shows that the middle- to high-latitude north Pacific and the high-latitude North Atlantic tend to lead global temperature variability on interdecadal timescales. These findings have important implications for understanding the dynamics of low-frequency climate variability in the preindustrial era.</p>

opencc-by-4.0Aug 2019View details →
zenodo48/100

SPHERA High Resolution Reanalysis over Italy - Hourly surface air temperature (2-meter height) 2013-2020

<p>SPHERA (High Resolution REAnalysis over Italy) &nbsp;is a convection-permitting regional reanalysis developed by ARPAE-Emilia Romagna and publicly available. The SPHERA domain covers Italy and the surrounding seas with a horizontal resolution of 2.2km. The temporal coverage corresponds to the period 1995-2020 and the dataset is available at hourly frequency. SPHERA reanalysis was developed using the Numerical Weather Prediction model COSMO (www.cosmo-model.org) nested in the global reanalysis ERA5 produced by ECMWF. Moreover, upper-air and surface observations were assimilated at the convection-permitting scale by the COSMO nudging scheme.</p> <p>This record reports the hourly surface air temperature at 2-meter height for the period 2013-2020. The full extension of the dataset over 1995-2020 is available over three different records due to space constraints:</p> <ul> <li>1995-2003: <a href="../records/12567563">https://zenodo.org/records/12567563</a></li> <li>2004-2012: <a href="../records/12582246">https://zenodo.org/records/12582246</a></li> <li>2013-2020: <a href="../records/12582797">https://zenodo.org/records/12582797</a></li> </ul> <p>Update (2024-06-28): inconsistencies were noted in a subset of grib messages contained the first version of the repository (slightly different spatial domain size and missing messages at 00-hour timesteps) which have been corrected in the current version v2.</p> <p>Other fields currently available on Zenodo are the surface relative humidity at 2-meter height (also over three different records due to space constraints):</p> <ul> <li>1995-2003: <a href="../records/12724026">https://zenodo.org/uploads/12724026</a></li> <li>2004-2012: <a href="../records/12724104">https://zenodo.org/uploads/12724104</a></li> <li>2013-2020: <a href="../records/12724140">https://zenodo.org/uploads/12724140</a></li> </ul> <p>and hourly accumulated total precipitation: <a href="https://zenodo.org/records/14617083">https://zenodo.org/records/14617083</a></p> <p>Details on the SPHERA dataset production, as well as data verification against surface observations are reported in peer-reviewed publications. See References.</p>

opencc-by-4.0Feb 2024View details →
zenodo48/100

SPHERA High Resolution Reanalysis over Italy - Hourly surface air temperature (2-meter height) 2004-2012

<p>SPHERA (High Resolution REAnalysis over Italy) &nbsp;is a convection-permitting regional reanalysis developed by ARPAE-Emilia Romagna and publicly available. The SPHERA domain covers Italy and the surrounding seas with a horizontal resolution of 2.2km. The temporal coverage corresponds to the period 1995-2020 and the dataset is available at hourly frequency. SPHERA reanalysis was developed using the Numerical Weather Prediction model COSMO (www.cosmo-model.org) nested in the global reanalysis ERA5 produced by ECMWF. Moreover, upper-air and surface observations were assimilated at the convection-permitting scale by the COSMO nudging scheme.</p> <p>This record reports the hourly surface air temperature at 2-meter height for the period 2004-2012. The full extension of the dataset over 1995-2020 is available over three different records due to space constraints:</p> <ul> <li>1995-2003: <a href="../records/12567563">https://zenodo.org/records/12567563</a></li> <li>2004-2012: <a href="../records/12582246">https://zenodo.org/records/12582246</a></li> <li>2013-2020: <a href="../records/12582797">https://zenodo.org/records/12582797</a></li> </ul> <p>Update (2024-06-28): inconsistencies were noted in a subset of grib messages contained the first version of the repository (slightly different spatial domain size and missing messages at 00-hour timesteps) which have been corrected in the current version v2.</p> <p>Other fields currently available on Zenodo are the surface relative humidity at 2-meter height (also over three different records due to space constraints):</p> <ul> <li>1995-2003: <a href="../records/12724026">https://zenodo.org/uploads/12724026</a></li> <li>2004-2012: <a href="../records/12724104">https://zenodo.org/uploads/12724104</a></li> <li>2013-2020: <a href="../records/12724140">https://zenodo.org/uploads/12724140</a></li> </ul> <p>and hourly accumulated total precipitation: <a href="https://zenodo.org/records/14617083">https://zenodo.org/records/14617083</a></p> <p>Details on the SPHERA dataset production, as well as data verification against surface observations are reported in peer-reviewed publications. See References.</p>

opencc-by-4.0Feb 2024View details →
zenodo48/100

SPHERA High Resolution Reanalysis over Italy - Hourly surface air temperature (2-meter height) 1995-2003

<p>SPHERA (High Resolution REAnalysis over Italy) &nbsp;is a convection-permitting regional reanalysis developed by ARPAE-Emilia Romagna and publicly available. The SPHERA domain covers Italy and the surrounding seas with a horizontal resolution of 2.2km. The temporal coverage corresponds to the period 1995-2020 and the dataset is available at hourly frequency. SPHERA reanalysis was developed using the Numerical Weather Prediction model COSMO (www.cosmo-model.org) nested in the global reanalysis ERA5 produced by ECMWF. Moreover, upper-air and surface observations were assimilated at the convection-permitting scale by the COSMO nudging scheme.</p> <p>This record reports the hourly surface air temperature at 2-meter height for the period 1995-2003. The full extension of the dataset over 1995-2020 is available over three different records due to space constraints:</p> <ul> <li>1995-2003: <a href="../records/12567563">https://zenodo.org/records/12567563</a></li> <li>2004-2012: <a href="../records/12582246">https://zenodo.org/records/12582246</a></li> <li>2013-2020: <a href="../records/12582797">https://zenodo.org/records/12582797</a></li> </ul> <p>Update (2024-06-28): inconsistencies were noted in a subset of grib messages contained the first version of the repository (slightly different spatial domain size and missing messages at 00-hour timesteps) which have been corrected in the current version v2.</p> <p>Other fields currently available on Zenodo are the surface relative humidity at 2-meter height (also over three different records due to space constraints):</p> <ul> <li>1995-2003: <a href="../records/12724026">https://zenodo.org/uploads/12724026</a></li> <li>2004-2012: <a href="../records/12724104">https://zenodo.org/uploads/12724104</a></li> <li>2013-2020: <a href="../records/12724140">https://zenodo.org/uploads/12724140</a></li> </ul> <p>and hourly accumulated total precipitation: <a href="https://zenodo.org/records/14617083">https://zenodo.org/records/14617083</a></p> <p>Details on the SPHERA dataset production, as well as data verification against surface observations are reported in peer-reviewed publications. See References.</p>

opencc-by-4.0Feb 2024View details →
zenodo48/100

SPHERA High Resolution Reanalysis over Italy - Hourly surface relative humidity (2-meter height) 2013-2020

<p>SPHERA (High Resolution REAnalysis over Italy) &nbsp;is a convection-permitting regional reanalysis developed by ARPAE-Emilia Romagna and publicly available. The SPHERA domain covers Italy and the surrounding seas with a horizontal resolution of 2.2km. The temporal coverage corresponds to the period 1995-2020 and the dataset is available at hourly frequency. SPHERA reanalysis was developed using the Numerical Weather Prediction model COSMO (www.cosmo-model.org) nested in the global reanalysis ERA5 produced by ECMWF. Moreover, upper-air and surface observations were assimilated at the convection-permitting scale by the COSMO nudging scheme.</p> <p>This record reports the hourly surface relative humidity at 2-meter height for the period 2013-2020. The full extension of the dataset over 1995-2020 is available over three different records due to space constraints:</p> <ul> <li>1995-2003: <a href="12724026">https://zenodo.org/uploads/12724026</a></li> <li>2004-2012: <a href="12724104">https://zenodo.org/uploads/12724104</a></li> <li>2013-2020: <a href="12724140">https://zenodo.org/uploads/12724140</a></li> </ul> <p>Other fields currently available on Zenodo are the surface air temperature at 2-meter height (also over three different records due to space constraints):</p> <ul> <li>1995-2003: <a href="../records/12567563">https://zenodo.org/records/12567563</a></li> <li>2004-2012: <a href="../records/12582246">https://zenodo.org/records/12582246</a></li> <li>2013-2020: <a href="../records/12582797">https://zenodo.org/records/12582797</a></li> </ul> <p>and hourly accumulated total precipitation: <a href="https://zenodo.org/records/14617083">https://zenodo.org/records/14617083</a></p> <p>Details on the SPHERA dataset production, as well as data verification against surface observations are reported in peer-reviewed publications. See References.</p>

opencc-by-4.0Jul 2024View details →
zenodo48/100

SPHERA High Resolution Reanalysis over Italy - Hourly surface relative humidity (2-meter height) 2004-2012

<p>SPHERA (High Resolution REAnalysis over Italy) &nbsp;is a convection-permitting regional reanalysis developed by ARPAE-Emilia Romagna and publicly available. The SPHERA domain covers Italy and the surrounding seas with a horizontal resolution of 2.2km. The temporal coverage corresponds to the period 1995-2020 and the dataset is available at hourly frequency. SPHERA reanalysis was developed using the Numerical Weather Prediction model COSMO (www.cosmo-model.org) nested in the global reanalysis ERA5 produced by ECMWF. Moreover, upper-air and surface observations were assimilated at the convection-permitting scale by the COSMO nudging scheme.</p> <p>This record reports the hourly surface relative humidity at 2-meter height for the period 2004-2012. The full extension of the dataset over 1995-2020 is available over three different records due to space constraints:</p> <ul> <li>1995-2003: <a href="12724026">https://zenodo.org/uploads/12724026</a></li> <li>2004-2012: <a href="12724104">https://zenodo.org/uploads/12724104</a></li> <li>2013-2020: <a href="12724140">https://zenodo.org/uploads/12724140</a></li> </ul> <p>Other fields currently available on Zenodo are the surface air temperature at 2-meter height (also over three different records due to space constraints):</p> <ul> <li>1995-2003: <a href="../records/12567563">https://zenodo.org/records/12567563</a></li> <li>2004-2012: <a href="../records/12582246">https://zenodo.org/records/12582246</a></li> <li>2013-2020: <a href="../records/12582797">https://zenodo.org/records/12582797</a></li> </ul> <p>and hourly accumulated total precipitation: <a href="https://zenodo.org/records/14617083">https://zenodo.org/records/14617083</a></p> <p>Details on the SPHERA dataset production, as well as data verification against surface observations are reported in peer-reviewed publications. See References.</p>

opencc-by-4.0Jul 2024View details →
zenodo48/100

SPHERA High Resolution Reanalysis over Italy - Hourly surface relative humidity (2-meter height) 1995-2003

<p>SPHERA (High Resolution REAnalysis over Italy) &nbsp;is a convection-permitting regional reanalysis developed by ARPAE-Emilia Romagna and publicly available. The SPHERA domain covers Italy and the surrounding seas with a horizontal resolution of 2.2km. The temporal coverage corresponds to the period 1995-2020 and the dataset is available at hourly frequency. SPHERA reanalysis was developed using the Numerical Weather Prediction model COSMO (www.cosmo-model.org) nested in the global reanalysis ERA5 produced by ECMWF. Moreover, upper-air and surface observations were assimilated at the convection-permitting scale by the COSMO nudging scheme.</p> <p>This record reports the hourly surface relative humidity at 2-meter height for the period 1995-2003. The full extension of the dataset over 1995-2020 is available over three different records due to space constraints:</p> <ul> <li>1995-2003: <a href="12724026">https://zenodo.org/uploads/12724026</a></li> <li>2004-2012: <a href="12724104">https://zenodo.org/uploads/12724104</a></li> <li>2013-2020: <a href="12724140">https://zenodo.org/uploads/12724140</a></li> </ul> <p>Other fields currently available on Zenodo are the surface air temperature at 2-meter height (also over three different records due to space constraints):</p> <ul> <li>1995-2003: <a href="../records/12567563">https://zenodo.org/records/12567563</a></li> <li>2004-2012: <a href="../records/12582246">https://zenodo.org/records/12582246</a></li> <li>2013-2020: <a href="../records/12582797">https://zenodo.org/records/12582797</a></li> </ul> <p>and hourly accumulated total precipitation: <a href="https://zenodo.org/records/14617083">https://zenodo.org/records/14617083</a></p> <p>Details on the SPHERA dataset production, as well as data verification against surface observations are reported in peer-reviewed publications. See References.</p>

opencc-by-4.0Jul 2024View details →
zenodo44/100

Global reanalysis of riverine water levels at the river mouth

<p>Dataset prepared for manuscript &quot;The effect of surge on riverine flood hazard and impact in deltas globally&quot; (Eilander <em>et al </em>2020)</p> <p>This dataset includes water level data and discharge at 3433 river mouth locations globally, including several components of&nbsp; nearshore still water levels based on a model framework for global compound flood simulations. We usedof runoff from tier 2 of the EartH2Observe (E2O) project (Dutra <em>et al</em> 2017, Schellekens <em>et al</em> 2017) with meteorological forcing from ERA-Interim (Dee <em>et al</em> 2011) and MSWEP v1.2 (Beck <em>et al</em> 2017), surge levels from the Global Tide and Surge Reanalysis (GTSR) based on the GTSM model (Muis <em>et al</em> 2016), and tide levels from the FES2012 model (Carrere <em>et al</em> 2012). These runoff and dynamic sea level (surge and tide) data were used to force the global river routing model CaMa-Flood (Yamazaki <em>et al</em> 2011) to simulate riverine water levels.</p> <p>The accompanying excel file provides an table explaining the data dimensions, variables and metadata.</p>

opencc-by-4.0Feb 2020View details →
zenodo44/100

On the airburst of large meteoroids in the Earth's atmosphere. The Lugo bolide: reanalysis of a case study

<p>Seismic data of the Lugo bolide of January 19, 1993.<br /> &nbsp;</p>

opencc-zeroJan 2015View details →
zenodo44/100

Global River Discharge Reanalysis dataset (GRDR)

<p>The Global River Discharge Reanalysis dataset (GRDR) is a global river discharge product that contains daily flows in ~2.9 million vectorized river reaches for 1984-2018.</p> <p>For more details about GRDR, please refer to the following publication:&nbsp; Feng D. and Gleason C.J., 2024, More flow upstream and less flow downstream: The changing form and function of global rivers, <em>Science</em></p>

opencc-by-4.0Jul 2023View details →
zenodo44/100

Remotely-sensed Arctic Discharge Reanalysis (RADR)

<p>This repository contains the dataset RADR generated from the work <em>Recent changes to Arctic river discharge (2021), Nature Communications, DOI:&nbsp;10.1038/s41467-021-27228-1</em></p> <p>The authors caution users that discrepancy in flow magnitude exists for a few small rivers between 1984-2013 and 2014-2019 due to the different climate forcings used for these two periods.</p>

opencc-by-4.0Apr 2021View details →
zenodo44/100

Data used in 'Local Wind Regime Induced by Giant Linear Dunes: Comparison of ERA5-Land Reanalysis with Surface Measurements.'

<p>This repository contains the data used in:</p> <blockquote> <p>Gadal, C., Delorme, P., Narteau, C. et al. Local Wind Regime Induced by Giant Linear Dunes: Comparison of ERA5-Land Reanalysis with Surface Measurements. Boundary-Layer Meteorol 185, 309&ndash;332 (2022). <a href="https://doi.org/10.1007/s10546-022-00733-6">https://doi.org/10.1007/s10546-022-00733-6</a></p> </blockquote> <p>where wind data measured at 4 different places in and across the Namib Sand Sea are compared to the data from the ERA5/ERA5Land climate reanalyses.</p> <p>The use this data, one should first look at the GitHub repository <a href="https://github.com/Cgadal/GiantDunes">https://github.com/Cgadal/GiantDunes</a> and at the corresponding documentation <a href="https://cgadal.github.io/GiantDunes/">https://cgadal.github.io/GiantDunes/</a>. The description sometimes refers to scripts used in <a href="https://github.com/Cgadal/GiantDunes/tree/master/Processing">https://github.com/Cgadal/GiantDunes/tree/master/Processing</a>.</p> <p>The two folders &#39;raw_data&#39; and &#39;processed_data&#39; contain the input raw_data, and the output data after processing used to make the paper figures, respectively. In each of them, &#39;.npy&#39; files contain Python dictionaries with different variables in them. They can be loaded using the Python library <code>numpy</code> as <code>data = np.load(&#39;file.npy&#39;, allow_pickle=True).item()</code>; and the different keys (variables) can be printed with <code>data.keys()</code> or <code>data[station].keys()</code> if <code>data.keys()</code> return the different stations. Unless specified otherwise below, note that all variables are given in the International System of Units (SI), and wind direction is given anticlockwise, with the 0 being a wind blowing from the West to the East.</p> <ul> <li>raw_data: <ul> <li>DEM: contains the Digital Elevation Models of the two stations from the SRTM30, downloaded from here: https://dwtkns.com/srtm30m/</li> <li>ERA5: hourly data from the ER5 climate reanalysis, on surface (_BLH) and pressure levels (_levels). Downloaded from https://cds.climate.copernicus.eu/</li> <li>ERA5Land: hourly data from the ER5Land climate reanalysis Downloaded from https://cds.climate.copernicus.eu/</li> <li>KML_points: kml points of the measurement station. It can be opened directly in GoogleEarth.</li> <li>measured_wind_data: contains the measured in situ data. The windspeed is measured using Vector Instruments A100-LK cup anemometers, the wind direction using Vector Instruments W200-P wind vane and the time using Campbell Instruments CR10X and CR1000X dataloggers.<br> &nbsp;</li> </ul> </li> <li>processed_data: <ul> <li>&#39;Data_preprocessed.npy&#39;: preprocessed_data, output of 1_data_preprocessing_plot.py</li> <li>&#39;Data_DEM.npy&#39;: properties of the processed DEM, the output of 2_DEM_analysis_plot.py</li> <li>&#39;Data_calib_roughness.npy&#39;: data from the calibration of the hydrodynamic roughnesses, the output of 3_roughness_calibration_plot.py</li> <li>&#39;Data_final.npy&#39;: file containing all computed quantities</li> <li>&#39;time_series_hydro_coeffs.npy&#39;: file containing the time series of the calculated hydrodynamic coefficients by &#39;5_norun_hydro_coeff_time_series.npy&#39;.</li> </ul> </li> </ul> <p>&nbsp; &nbsp; &nbsp; Depending on the loaded data file, main dictionary keys can be:</p> <ul> <li>&#39;lat&#39;: latitude, in degree</li> <li>&#39;lon&#39;: longitude, in degree</li> <li>&#39;time&#39;: time vector, in datetime objects (https://docs.python.org/3/library/datetime.html)</li> <li>&#39;DEM&#39;: elevation data array in [m], with dimensions matching &#39;lat&#39; and &#39;lon&#39; vectors</li> <li>&#39;z_mes&#39;, &#39;z_insitu&#39;, &#39;z_ERA5LAND&#39;: height of the corresponding velocity</li> <li>&#39;direction&#39;: measured wind direction, in [degrees]</li> <li>&#39;velocity&#39;: measured wind velocity, in [m/s]</li> <li>&#39;orientaion&#39;: dune pattern orientation, [deg]</li> <li>&#39;wavelength&#39;: dune pattern wavelength, [km]</li> <li>&#39;z0_insitu&#39;: chosen hydrodynamic roughness for the considered station.</li> <li>&#39;U_insitu&#39;, &#39;Orientation_insitu&#39;: hourly averaged measured wind velocities and direction</li> <li>&#39;U_era&#39;, &#39;Orientation_era&#39;: hourly 10m wind data from the ERA5Land data set</li> <li>&#39;Boundary layer height&#39;, &#39;blh&#39;: boundary layer height from the hourly ERA5 dataset</li> <li>&#39;Pressure levels&#39;, &#39;levels&#39;: Pressure levels from the pressure levels ERA5 dataset</li> <li>&#39;Temperature&#39;, &#39;t&#39;: Temperature from the pressure levels ERA5 dataset</li> <li>&#39;Specific humidity&#39;, &#39;q&#39;: Specific humidity from the pressure levels ERA5 dataset</li> <li>&#39;Geopotential&#39;, &#39;z&#39;: Geopotential from the pressure levels ERA5 dataset</li> <li>&#39;Virtual_potential_temperature&#39;: Virtual potential temperature calculated from the pressure levels ERA5 dataset</li> <li>&#39;Potential_temperature&#39;: Potential temperature calculated from the pressure levels ERA5 dataset</li> <li>&#39;Density&#39;: Density calculated from the pressure levels ERA5 dataset</li> <li>&#39;height&#39;: Vertical coordinates calculated from the pressure levels ERA5 dataset</li> <li>&#39;theta_ground&#39;: Averaged virtual potential temperature within the ABL.</li> <li>&#39;delta_theta&#39;: Virtual potential temperature at the ABL.</li> <li>&#39;gradient_free_atm&#39;: Virtual potential temperature gradient in the FA.</li> <li>&#39;Froude&#39;: time series of the Froude number U/((delta_theta/theta_ground)*g*BLH)</li> <li>&#39;kH&#39;: time series of the number &#39;kH&#39;</li> <li>&#39;kLB&#39;: time series of the internal Froude number kU/N</li> </ul> <p>Other keys are not relevant and are stored for verification purposes. For more details, please contact Cyril Gadal (see authors), and look at the following GitHub repository: <a href="https://github.com/Cgadal/GiantDunes">https://github.com/Cgadal/GiantDunes</a>, where all the codes are present.<br> &nbsp;</p>

opencc-by-4.0Mar 2022View details →
zenodo44/100

GloRESatE - Global Rainfall Erosivity from Reanalysis and Satellite Estimates

<p>Rainfall erosivity measures the impact of rainfall kinetic energy and intensity or its potential to cause soil erosion. The sparsely available gauge rainfall dataset limits reliable rainfall erosivity assessment globally. GloRESatE is a state-of-the-art global rainfall erosivity dataset with a high spatial resolution of 0.1&deg; &times; 0.1&deg;. It integrates satellite data (CMORPH, IMERG Final Run), reanalysis data (ERA5-Land), and observations from 6,170 gauge stations worldwide. Created using advanced Gaussian Process Regression, this dataset provides accurate and reliable rainfall erosivity information. It serves as a vital resource for hydrological research, aiding studies in soil erosion, water resource management, and climate change impact assessments on a global scale.</p> <p>&nbsp;</p> <p>Das, S., Jain, M.K., Gupta, V., McGehee, R.P., Yin, S., de Mello, C.R., Azari, M., Borrelli, P. and Panagos, P., 2024. GloRESatE: A dataset for global rainfall erosivity derived from multi-source data.&nbsp;<em>Scientific Data</em>,&nbsp;<strong>11</strong>:926. https://doi.org/10.1038/s41597-024-03756-5</p>

opencc-by-4.0Oct 2023View details →
zenodo44/100

CATCH-EyoU: Exploiting European data and testing the integrated theory of youth active EU citizenship: PIDOP subset reanalysis

<p>This is a subset of the full PIDOP dataset. The derived subset contains cross-sectional survey results from the PIDOP questionnaire survey that were collected in 9 European countries (incl. Turkey) during a period of 16-26 year old in 2011. The data set includes 9060 individual cases.&nbsp; The questionnaire used in the survey is published in Barrett, M. &amp; Zani, B. (Eds.) (2015). <em>Political and civic engagement: Multidisciplinary perspectives.</em> Hove: Routledge (p.519-534).</p>

opencc-by-nc-4.0Jul 2018View details →
zenodo44/100

MERIDA - MEteorological Reanalysis Italian DAtaset

<p>The new <strong>ME</strong>teorological <strong>R</strong>eanalysis <strong>I</strong>talian <strong>DA</strong>taset (<strong>MERIDA</strong>) has been developed to cope with the increasing weather extremes of the last 20 years, which caused several disruptions to the Italian electric system. This work has been developed following the indications emerged from the &ldquo;Resilience Working Table&rdquo; set up by the Italian Regulatory Authority for Energy, Networks and the Environment (ARERA). MERIDA is able to respond to the energy stakeholders, who need reliable meteorological data to implement effective adaptation strategies to operate the electric system safely.</p> <p>MERIDA consists of a dynamical downscaling of the ERA5 global reanalysis using the mesoscale model WRF-ARW. ERA5 data are retrieved with a 3-hourly temporal resolution to assure good temporal consistency. Temperature data from the SYNOP Air Force stations are also retrieved to be ingested in the WRF simulations at 3-hourly temporal resolution.</p> <p>The computational domain of MERIDA consists of 2 grids with horizontal resolution of 21 km and 7 km respectively, with the internal grid centered over Italy.</p> <p>The meteorological fields of MERIDA are open access and distributed in NETCDF file format on a regular lat-lon grid of 0.07&deg; resolution.</p> <p>A subset of the dataset is available here for download for the period 2000-2018. The full dataset covering the period 1990-2019, and continuoulsy updated, is available at the following website:&nbsp;<a href="http://merida.rse-web.it">http://merida.rse-web.it/</a>&nbsp;</p> <p>The following subset of meteorological fields is available here for download:</p> <ul> <li>T2 - 2m temperature (K)</li> <li>PREC - Total Precipitation (mm/h)</li> <li>U10 - 10m u-component of wind (m/s)</li> <li>V10 - 10m v-component of wind (m/s)</li> <li>PSFC - Surface pressure (Pa)</li> <li>Q2 - 2m Specific Humidity (Kg/Kg)</li> <li>SWDIR - Direct global short-wave radiation (W/m<sup>2</sup>)</li> <li>SWDIF - Diffuse global short-wave radiation (W/m<sup>2</sup>)</li> <li>MSLP - Mean Sea Level Pressure (Pa)</li> <li>SNEQV - Snow Water Equivalent (mm)</li> <li>SOIL_T - Soil Temperature - Layer 5 cm (K)</li> <li>SOIL_M - Soil Moisture - Layer 5 cm (m<sup>3</sup>/m<sup>3</sup>)</li> </ul> <p>The following variables are available under request:</p> <ul> <li>SOIL_T - Soil Temperature (K, Layers: 5,25,70,150 cm)</li> <li>SOIL_M - Soil Moisture - Layer 5 cm (m<sup>3</sup>/m<sup>3</sup>, Layers: 5,25,70,150 cm)</li> <li>TT - Temperature (K, Pressure levels: 850,700,500 hPa)</li> <li>RH - Relative Humidity (%, Pressure levels: 850,700,500 hPa)</li> <li>GHT - Geopotential Height (gpm, Pressure levels: 850,700,500 hPa)</li> <li>UU - u-component of wind (m/s, Pressure levels: 850,700,500 hPa)</li> <li>VV - v-component of wind (m/s, Pressure levels: 850,700,500 hPa)</li> <li>TG - Ground Temperature (K)</li> <li>HFX - Sensible Heat Flux (W/m<sup>2</sup>)</li> <li>LH - Latent Heat Flux (W/m<sup>2</sup>)</li> <li>GRDFLX - Ground Flux (W/m<sup>2</sup>)</li> <li>TR - Transpiration Flux (W/m<sup>2</sup>)</li> </ul> <p>All the variables not included for download may be downloaded at :&nbsp;<a href="http://merida.rse-web.it">http://merida.rse-web.it/</a></p> <p>or requested at:</p> <ul> <li>riccardo.bonanno@rse-web.it</li> <li>matteo.lacavalla@rse-web.it</li> <li>simone.sperati@rse-web.it</li> </ul> <p>&nbsp;</p>

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

Multi-study reanalysis of 2213 acute myeloid leukemia patients reveals age- and sex-dependent gene expression signatures

<p>Supplemental Information,&nbsp;Supplementary&nbsp;Tables, and Supplementary Files, as well as&nbsp;accompanying data for&nbsp;the manuscript &quot;Stratified computational meta-analysis of 2213 acute myeloid leukemia patients reveals age- and sex-dependent gene expression signatures&quot; by Raeuf Roushangar and George I. Mias.&nbsp;</p>

opencc-by-4.0Jun 2019View 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