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Dataset Open Access

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

M. De Felice; K. Kavvadias

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  <identifier identifierType="DOI">10.5281/zenodo.3663518</identifier>
      <creatorName>M. De Felice</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="">0000-0002-5457-3045</nameIdentifier>
      <affiliation>European Commission, Joint Research Centre (JRC)</affiliation>
      <creatorName>K. Kavvadias</creatorName>
      <nameIdentifier nameIdentifierScheme="ORCID" schemeURI="">0000-0002-8314-7547</nameIdentifier>
      <affiliation>European Commission, Joint Research Centre (JRC)</affiliation>
    <title>ERA-NUTS: time-series based on C3S ERA5 for European regions</title>
    <subject>energy modelling</subject>
    <subject>power system modelling</subject>
    <subject>renewable energy</subject>
    <date dateType="Issued">2020-02-12</date>
  <resourceType resourceTypeGeneral="Dataset"/>
    <alternateIdentifier alternateIdentifierType="url"></alternateIdentifier>
    <relatedIdentifier relatedIdentifierType="DOI" relationType="IsVersionOf">10.5281/zenodo.2650190</relatedIdentifier>
    <rights rightsURI="">Creative Commons Attribution 4.0 International</rights>
    <rights rightsURI="info:eu-repo/semantics/openAccess">Open Access</rights>
    <description descriptionType="Abstract">&lt;p&gt;&lt;strong&gt;# ERA-NUTS (1980-2019)&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;This dataset contains a set of time-series of meteorological variables based on &lt;a href=""&gt;Copernicus Climate Change Service (C3S) ERA5 reanalysis&lt;/a&gt;. The data files can be downloaded from here while notebooks and other files can be found on the &lt;a href=""&gt;associated Github repository&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;This data has been generated with the aim of providing hourly time-series of the &lt;strong&gt;meteorological variables&lt;/strong&gt; commonly used for power system modelling and, more in general, studies on energy systems.&lt;/p&gt;

&lt;p&gt;An example of the analysis that can be performed with ERA-NUTS is shown &lt;a href=""&gt;in this video&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;Important&lt;/strong&gt;: &lt;em&gt;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 &lt;a href=""&gt;sending an email&lt;/a&gt; or opening an Issue in the &lt;a href=""&gt;associated Github repository&lt;/a&gt;.&lt;/em&gt;&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;## Data&lt;/strong&gt;&lt;br&gt;
The time-series have hourly/daily/monthly frequency and are aggregated following the &lt;a href=""&gt;NUTS&amp;nbsp; 2016 classification&lt;/a&gt;. 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).&lt;/p&gt;

&lt;p&gt;This dataset contains NUTS0/1/2 time-series for the following variables obtained from the &lt;strong&gt;ERA5 reanalysis data&lt;/strong&gt; (in brackets the name of the variable on the Copernicus Data Store and its unit measure):&lt;/p&gt;

&lt;p&gt;&amp;nbsp; - &lt;strong&gt;t2m&lt;/strong&gt;: 2-meter temperature (`2m_temperature`, Celsius degrees)&lt;br&gt;
&amp;nbsp; - &lt;strong&gt;ssrd&lt;/strong&gt;: Surface solar radiation (`surface_solar_radiation_downwards`, Watt per square meter)&lt;br&gt;
&amp;nbsp; - &lt;strong&gt;ssrdc&lt;/strong&gt;: Surface solar radiation clear-sky (`surface_solar_radiation_downward_clear_sky`, Watt per square meter)&lt;br&gt;
&amp;nbsp; - &lt;strong&gt;ro&lt;/strong&gt;: Runoff (`runoff`, millimeters)&lt;br&gt;
There are also a set of derived variables:&lt;br&gt;
&amp;nbsp; - &lt;strong&gt;ws10&lt;/strong&gt;: Wind speed at 10 meters (derived by `10m_u_component_of_wind` and `10m_v_component_of_wind`, meters per second)&lt;br&gt;
&amp;nbsp; - &lt;strong&gt;ws100&lt;/strong&gt;: Wind speed at 100 meters (derived by `100m_u_component_of_wind` and `100m_v_component_of_wind`, meters per second)&lt;br&gt;
&amp;nbsp; - &lt;strong&gt;CS&lt;/strong&gt;: Clear-Sky index (the ratio between the solar radiation and the solar radiation clear-sky)&lt;br&gt;
&amp;nbsp; - &lt;strong&gt;HDD&lt;/strong&gt;/&lt;strong&gt;CDD&lt;/strong&gt;: Heating/Cooling Degree days (derived by 2-meter temperature the &lt;a href=""&gt;EUROSTAT definition&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;For each variable we have &lt;strong&gt;350 599 hourly samples&lt;/strong&gt; (from 01-01-1980 00:00:00 to 31-12-2019 23:00:00) for &lt;strong&gt;34/115/309 regions&lt;/strong&gt; (NUTS 0/1/2).&lt;br&gt;
The data is provided in two formats:&lt;/p&gt;

&lt;p&gt;&amp;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.&lt;br&gt;
&amp;nbsp; - Comma Separated Value (&amp;quot;single index&amp;quot; format for all the variables and the time frequencies and &amp;quot;stacked&amp;quot; only for daily and monthly)&lt;br&gt;
All the CSV files are stored in a zipped file for each variable.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;## Methodology&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;The time-series have been generated using the following workflow:&lt;/p&gt;

&lt;p&gt;&amp;nbsp; 1. The NetCDF files are downloaded from the Copernicus Data Store from the &lt;a href="!/dataset/reanalysis-era5-single-levels?tab=form"&gt;ERA5 hourly data on single levels from 1979 to present&lt;/a&gt; dataset&lt;br&gt;
&amp;nbsp; 2. The data is read in R with the &lt;a href=""&gt;climate4r&lt;/a&gt; packages and aggregated using the function `/get_ts_from_shp` from &lt;a href=""&gt;panas&lt;/a&gt;. 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.&lt;br&gt;
&amp;nbsp; 3. The derived variables (wind speed, CDD/HDD, clear-sky) are computed and all the CSV files are generated using R&lt;br&gt;
&amp;nbsp; 4. The NetCDF are created using `xarray` in Python 3.7.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;NOTE&lt;/strong&gt;: air temperature, solar radiation, runoff and wind speed hourly data have been rounded with two decimal digits.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;## Example notebooks&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;In the folder `notebooks` on the &lt;a href=""&gt;associated Github repository&lt;/a&gt; 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 &lt;a href=""&gt;enlopy&lt;/a&gt; package.&lt;/p&gt;

&lt;p&gt;There are currently two notebooks:&lt;/p&gt;

&lt;p&gt;&amp;nbsp; - &lt;strong&gt;exploring-ERA-NUTS&lt;/strong&gt;: it shows how to open the NetCDF files (with Dask), how to manipulate and visualise them.&lt;br&gt;
&amp;nbsp; - &lt;strong&gt;ERA-NUTS-explore-with-widget&lt;/strong&gt;: explorer interactively the datasets with [&lt;a href=""&gt;jupyter&lt;/a&gt;]() and &lt;a href=""&gt;ipywidgets&lt;/a&gt;.&lt;/p&gt;

&lt;p&gt;The notebook `exploring-ERA-NUTS` is also available rendered as HTML.&lt;br&gt;
&lt;strong&gt;## Additional files&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;In the folder `additional files`on the &lt;a href=""&gt;associated Github repository&lt;/a&gt; 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.&lt;/p&gt;

&lt;p&gt;&lt;strong&gt;## License&lt;/strong&gt;&lt;/p&gt;

&lt;p&gt;This dataset is released under &lt;a href=""&gt;CC-BY-4.0 license&lt;/a&gt;.&lt;/p&gt;

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