DOI: 10.5281/zenodo.4280851
title: Data accompanying "European wind and solar power generation variability over a century"

description: <strong>Data accompanying "European wind and solar power generation variability over a century"</strong> We provide wind and solar wind power generation on a gridbox level as netcdf files and country level generation timeseries as a dictionary of pandas timeseries. Due to storage size limitation, only the ensemble means are available. The data is compressed and needs to be unzipped first. The folder <strong>Solar</strong> contains three subfolders, each representing one panel orientation scenario. For each scenario, data is available from 1901 to 2009 in seperate files. Each file contains 3h data on the CERA20C grid over Europe. The folder <strong>Wind</strong> contains three subfolders, each representing different wind turbines. Data is available from summer 1905 to 2005. The wind data does not cover the same time period as the solar data because we only report it where the Lanczos filter is fully defined and this leads to a shorter covered timeperiod. The folder <strong>countrygroup</strong> contains a pickled dictionary of generation timeseries in the country groups defined in the publication. It is structured as follows: 1) <pre><code class="language-python">country_generation['seasonal']</code></pre> is based on monthly mean generation from 1980 to 2000 while <pre><code class="language-python">country_generation['multidecadal']</code></pre> is based on 20 year running mean generation that is available from 1917 to 1996. 2) For both timescales ('seasonal' and 'multidecadal'), data is available for each of the country groups defined in Table 1 of the publication. 3) For each country group and timescale, generation data is available seperately for wind and solar power. For illustration, For illustration, <pre><code class="language-python">country_generation['multidecadal']['Portugal, Spain']['wind']</code></pre> contains 20 year running means of wind power generation in Portugal and Spain covering 1917 to 1996. The dictionary can be loaded in python as <pre><code class="language-python">import pickle BASE_PATH = "" # add path to data with open(BASE_PATH + "country_generation.pickle", "rb") as handle: country_generation = pickle.load(handle)</code></pre> <strong>Input data from centennial reanalyses</strong> Our work relies on input data from the National Oceanic and Atmospheric Administration (NOAA) and the Cooperative Institute for Research in Environmental Sciences (CIRES), and the European Centre for Medium Range Weather Prediction (ECMWF). We use the centennial reanalyses 20CRv3 from NOAA-CIRES and CERA20C from ECMWF. For a documentation of the datasets please read: Slivinski, L. C. <em>et al.</em> Towards a more reliable historical reanalysis: Improvements for version 3 of the Twentieth Century Reanalysis system. <em>Q.J.R. Meteorol. Soc.</em> qj.3598 (2019). Laloyaux, P. <em>et al.</em> CERA-20C: A Coupled Reanalysis of the Twentieth Century. <em>J. Adv. Model. Earth Syst.</em> <strong>10</strong>, 1172–1195 (2018).
description: Production of this dataset and the corresponding publication was made possible through an ETH Fellowship funded by the Uniscientia foundation and the ETH foundation.