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ERA-NUTS: time-series based on C3S ERA5 for European regions

M. De Felice; K. Kavvadias

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  "publisher": "Zenodo", 
  "DOI": "10.5281/zenodo.2650191", 
  "title": "ERA-NUTS: time-series based on C3S ERA5 for European regions", 
  "issued": {
    "date-parts": [
  "abstract": "<p><strong># ERA-NUTS (1980-2018)</strong></p>\n\n<p>This dataset contains a set of time-series of meteorological variables based on <a href=\"\">Copernicus Climate Change Service (C3S) ERA5 reanalysis</a>. The data files can be downloaded from here while notebooks and other files can be found on the <a href=\"\">associated Github repository</a>.</p>\n\n<p>This data has been generated with the aim of providing hourly time-series of the <strong>meteorological variables</strong> commonly used for power system modelling and, more in general, studies on energy systems.</p>\n\n<p>An example of the analysis that can be performed with ERA-NUTS is shown <a href=\"\">in this video</a>.</p>\n\n<p><strong>Important</strong>: <em>this dataset is still a work-in-progress, we will add more analysis and variables in the near-future. If you spot an error or something strange in the data please tell us <a href=\"\">sending an email</a> or opening an Issue in the <a href=\"\">associated Github repository</a>.</em></p>\n\n<p><strong>## Data</strong><br>\nThe time-series have hourly/daily/monthly frequency and are aggregated following the <a href=\"\">NUTS&nbsp; 2016 classification</a>. NUTS (Nomenclature of Territorial Units for Statistics) is a European Union standard for referencing the subdivisions of countries (member states, candidate countries and EFTA countries).</p>\n\n<p>This dataset contains NUTS0/1/2 time-series for the following variables obtained from the <strong>ERA5 reanalysis data</strong> (in brackets the name of the variable on the Copernicus Data Store and its unit measure):</p>\n\n<p>&nbsp; - <strong>t2m</strong>: 2-meter temperature (`2m_temperature`, Celsius degrees)<br>\n&nbsp; - <strong>ssrd</strong>: Surface solar radiation (`surface_solar_radiation_downwards`, Watt per square meter)<br>\n&nbsp; - <strong>ssrdc</strong>: Surface solar radiation clear-sky (`surface_solar_radiation_downward_clear_sky`, Watt per square meter)<br>\n&nbsp; - <strong>ro</strong>: Runoff (`runoff`, millimeters)<br>\n&nbsp;<br>\nThere are also a set of derived variables:<br>\n&nbsp; - <strong>ws10</strong>: Wind speed at 10 meters (derived by `10m_u_component_of_wind` and `10m_v_component_of_wind`, meters per second)<br>\n&nbsp; - <strong>ws100</strong>: Wind speed at 100 meters (derived by `100m_u_component_of_wind` and `100m_v_component_of_wind`, meters per second)<br>\n&nbsp; - <strong>CS</strong>: Clear-Sky index (the ratio between the solar radiation and the solar radiation clear-sky)<br>\n&nbsp; - <strong>HDD</strong>/<strong>CDD</strong>: Heating/Cooling Degree days (derived by 2-meter temperature the <a href=\"\">EUROSTAT definition</a>.</p>\n\n<p>For each variable we have <strong>350 599 hourly samples</strong> (from 01-01-1980 00:00:00 to 31-12-2019 23:00:00) for <strong>34/115/309 regions</strong> (NUTS 0/1/2).<br>\n&nbsp;<br>\nThe data is provided in two formats:</p>\n\n<p>&nbsp; - NetCDF version 4 (all the variables hourly and CDD/HDD daily). NOTE: the variables are stored as `int16` type using a `scale_factor` of 0.01 to minimise the size of the files.<br>\n&nbsp; - Comma Separated Value (&quot;single index&quot; format for all the variables and the time frequencies and &quot;stacked&quot; only for daily and monthly)<br>\n&nbsp;<br>\nAll the CSV files are stored in a zipped file for each variable.</p>\n\n<p><strong>## Methodology</strong></p>\n\n<p>The time-series have been generated using the following workflow:</p>\n\n<p>&nbsp; 1. The NetCDF files are downloaded from the Copernicus Data Store from the <a href=\"!/dataset/reanalysis-era5-single-levels?tab=form\">ERA5 hourly data on single levels from 1979 to present</a> dataset<br>\n&nbsp; 2. The data is read in R with the <a href=\"\">climate4r</a> packages and aggregated using the function `/get_ts_from_shp` from <a href=\"\">panas</a>. All the variables are aggregated at the NUTS boundaries using the average except for the runoff, which consists of the sum of all the grid points within the regional/national borders.<br>\n&nbsp; 3. The derived variables (wind speed, CDD/HDD, clear-sky) are computed and all the CSV files are generated using R<br>\n&nbsp; 4. The NetCDF are created using `xarray` in Python 3.7.</p>\n\n<p><strong>NOTE</strong>: air temperature, solar radiation, runoff and wind speed hourly data have been rounded with two decimal digits.</p>\n\n<p><strong>## Example notebooks</strong></p>\n\n<p>In the folder `notebooks` on the <a href=\"\">associated Github repository</a> there are two Jupyter notebooks which shows how to deal effectively with the NetCDF data in `xarray` and how to visualise them in several ways by using matplotlib or the <a href=\"\">enlopy</a> package.</p>\n\n<p>There are currently two notebooks:</p>\n\n<p>&nbsp; - <strong>exploring-ERA-NUTS</strong>: it shows how to open the NetCDF files (with Dask), how to manipulate and visualise them.<br>\n&nbsp; - <strong>ERA-NUTS-explore-with-widget</strong>: explorer interactively the datasets with [<a href=\"\">jupyter</a>]() and <a href=\"\">ipywidgets</a>.</p>\n\n<p>The notebook `exploring-ERA-NUTS` is also available rendered as HTML.<br>\n<br>\n<strong>## Additional files</strong></p>\n\n<p>In the folder `additional files`on the <a href=\"\">associated Github repository</a> there is a map showing the spatial resolution of the ERA5 reanalysis and a CSV file specifying the number of grid points with respect to each NUTS0/1/2 region.</p>\n\n<p><strong>## License</strong></p>\n\n<p>This dataset is released under <a href=\"\">CC-BY-4.0 license</a>.</p>\n\n<p>&nbsp;</p>", 
  "author": [
      "family": "M. De Felice"
      "family": "K. Kavvadias"
  "version": "1980-2018", 
  "type": "dataset", 
  "id": "2650191"
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