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

M. De Felice; K. Kavvadias


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  <dc:creator>M. De Felice</dc:creator>
  <dc:creator>K. Kavvadias</dc:creator>
  <dc:date>2019-04-24</dc:date>
  <dc:description># ERA-NUTS (1980-2018)

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

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

An example of the analysis that can be performed with ERA-NUTS is shown in this video.

Important: 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 sending an email or opening an Issue in the associated Github repository.

## Data
The time-series have hourly/daily/monthly frequency and are aggregated following the NUTS  2016 classification. 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).

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

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

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

  - 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.
  - Comma Separated Value ("single index" format for all the variables and the time frequencies and "stacked" only for daily and monthly)
 
All the CSV files are stored in a zipped file for each variable.

## Methodology

The time-series have been generated using the following workflow:

  1. The NetCDF files are downloaded from the Copernicus Data Store from the ERA5 hourly data on single levels from 1979 to present dataset
  2. The data is read in R with the climate4r packages and aggregated using the function `/get_ts_from_shp` from panas. 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.
  3. The derived variables (wind speed, CDD/HDD, clear-sky) are computed and all the CSV files are generated using R
  4. The NetCDF are created using `xarray` in Python 3.7.

NOTE: air temperature, solar radiation, runoff and wind speed hourly data have been rounded with two decimal digits.

## Example notebooks

In the folder `notebooks` on the associated Github repository 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 enlopy package.

There are currently two notebooks:

  - exploring-ERA-NUTS: it shows how to open the NetCDF files (with Dask), how to manipulate and visualise them.
  - ERA-NUTS-explore-with-widget: explorer interactively the datasets with [jupyter]() and ipywidgets.

The notebook `exploring-ERA-NUTS` is also available rendered as HTML.

## Additional files

In the folder `additional files`on the associated Github repository 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.

## License

This dataset is released under CC-BY-4.0 license.

 </dc:description>
  <dc:identifier>https://zenodo.org/record/2650191</dc:identifier>
  <dc:identifier>10.5281/zenodo.2650191</dc:identifier>
  <dc:identifier>oai:zenodo.org:2650191</dc:identifier>
  <dc:relation>doi:10.5281/zenodo.2650190</dc:relation>
  <dc:rights>info:eu-repo/semantics/openAccess</dc:rights>
  <dc:rights>https://creativecommons.org/licenses/by/4.0/legalcode</dc:rights>
  <dc:subject>era5</dc:subject>
  <dc:subject>copernicus</dc:subject>
  <dc:subject>time-series</dc:subject>
  <dc:subject>meteorology</dc:subject>
  <dc:subject>climate</dc:subject>
  <dc:subject>energy modelling</dc:subject>
  <dc:subject>power system modelling</dc:subject>
  <dc:subject>renewable energy</dc:subject>
  <dc:title>ERA-NUTS: time-series based on C3S ERA5 for European regions</dc:title>
  <dc:type>info:eu-repo/semantics/other</dc:type>
  <dc:type>dataset</dc:type>
</oai_dc:dc>
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