University of Reading Research Data Archive

Future climate projections of surface weather variables, wind power, and solar power capacity factors across North-West Europe

How to cite this Dataset


The files contain hourly time-series of weather and energy variables which have been climate-adjusted to include the impact of climate change from five different climate model simulations, with the climate change impacts centred on the year 2035 (i.e. the mean change seen from 2020-2050). The climate-adjustment is implemented as a delta climate-change correction to observed historic weather data from the ERA5 reanalysis (which is also supplied alongside for convenience).

Hourly time series of surface meteorological variables useful for energy meteorology studies have been supplied (e.g. 2m temperature, near-surface wind speeds and surface solar irradiance), as well as heating/cooling degree-days, wind power capacity factors, and solar power capacity factors. The time series are calculated at national level across a sub-set of countries over North-West Europe, as well as for a sub-set of smaller regions over Great Britain and its surrounding seas. The locations of available wind and solar farms are used to weight wind and solar capacity factors respectively. 2020 population data is used to weight surface meteorological variables. The datasets have been produced to increase the use of meteorological data within power system modelling.

When citing this dataset please refer to the publication: Calibrated hourly historical and near-future energy-meteorology variables for energy system modelling (see related CentAUR publications).

Resource Type: Dataset
Creators: Bloomfield, Hannah ORCID logoORCID: and Brayshaw, David
Rights-holders: University of Reading
Data Publisher: University of Reading
Publication Year: 2021
Data last accessed: 20 April 2024
Metadata Record URL:
Organisational units: Science > School of Mathematical, Computational and Physical Sciences > Department of Meteorology
Participating Organisations: University of Reading
Keywords: energy-meteorology, wind power generation, solar power generation
Data Availability: OPEN


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