Dataset Open Access
ERA-NUTS: time-series based on C3S ERA5 for European regions
M. De Felice;
K. Kavvadias
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{
"description": "<p><strong># ERA-NUTS (1980-2019)</strong></p>\n\n<p>This dataset contains a set of time-series of meteorological variables based on <a href=\"https://climate.copernicus.eu/climate-reanalysis\">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=\"https://github.com/energy-modelling-toolkit/era-nuts-code\">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=\"https://youtu.be/zVeF8Dv6jlE\">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=\"mailto:matteo.de-felice@ec.europa.eu\">sending an email</a> or opening an Issue in the <a href=\"https://github.com/energy-modelling-toolkit/era-nuts-code\">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=\"https://ec.europa.eu/eurostat/web/nuts/background\">NUTS 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> - <strong>t2m</strong>: 2-meter temperature (`2m_temperature`, Celsius degrees)<br>\n - <strong>ssrd</strong>: Surface solar radiation (`surface_solar_radiation_downwards`, Watt per square meter)<br>\n - <strong>ssrdc</strong>: Surface solar radiation clear-sky (`surface_solar_radiation_downward_clear_sky`, Watt per square meter)<br>\n - <strong>ro</strong>: Runoff (`runoff`, millimeters)<br>\n <br>\nThere are also a set of derived variables:<br>\n - <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 - <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 - <strong>CS</strong>: Clear-Sky index (the ratio between the solar radiation and the solar radiation clear-sky)<br>\n - <strong>HDD</strong>/<strong>CDD</strong>: Heating/Cooling Degree days (derived by 2-meter temperature the <a href=\"https://ec.europa.eu/eurostat/cache/metadata/en/nrg_chdd_esms.htm\">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 <br>\nThe data is provided in two formats:</p>\n\n<p> - 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 - Comma Separated Value ("single index" format for all the variables and the time frequencies and "stacked" only for daily and monthly)<br>\n <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> 1. The NetCDF files are downloaded from the Copernicus Data Store from the <a href=\"https://cds.climate.copernicus.eu/cdsapp#!/dataset/reanalysis-era5-single-levels?tab=form\">ERA5 hourly data on single levels from 1979 to present</a> dataset<br>\n 2. The data is read in R with the <a href=\"http://www.meteo.unican.es/climate4R\">climate4r</a> packages and aggregated using the function `/get_ts_from_shp` from <a href=\"https://github.com/matteodefelice/panas\">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 3. The derived variables (wind speed, CDD/HDD, clear-sky) are computed and all the CSV files are generated using R<br>\n 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=\"https://github.com/energy-modelling-toolkit/era-nuts-code\">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=\"https://github.com/kavvkon/enlopy\">enlopy</a> package.</p>\n\n<p>There are currently two notebooks:</p>\n\n<p> - <strong>exploring-ERA-NUTS</strong>: it shows how to open the NetCDF files (with Dask), how to manipulate and visualise them.<br>\n - <strong>ERA-NUTS-explore-with-widget</strong>: explorer interactively the datasets with [<a href=\"https://jupyter.org/\">jupyter</a>]() and <a href=\"https://ipywidgets.readthedocs.io/en/stable/\">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=\"https://github.com/energy-modelling-toolkit/era-nuts-code\">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=\"https://creativecommons.org/licenses/by/4.0/\">CC-BY-4.0 license</a>.</p>\n\n<p> </p>",
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"affiliation": "European Commission, Joint Research Centre (JRC)",
"@id": "https://orcid.org/0000-0002-5457-3045",
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"name": "M. De Felice"
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"url": "https://zenodo.org/record/3663518",
"datePublished": "2020-02-12",
"version": "1980-2019",
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5,959
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| All versions | This version | |
|---|---|---|
| Views | 5,959 | 329 |
| Downloads | 1,029 | 217 |
| Data volume | 210.0 GB | 58.5 GB |
| Unique views | 4,291 | 285 |
| Unique downloads | 398 | 73 |
Indexed in
- Publication date:
- February 12, 2020
- DOI:
-
- Keyword(s):
- era5 copernicus time-series meteorology climate energy modelling power system modelling renewable energy
- License (for files):
- Creative Commons Attribution 4.0 International