Climate change is stated as one of the biggest challenges of our time resulting in many unwanted effects. The response of cloud fractional cover (CFC), i.e. the portion of the sky covered by clouds, to future climate is associated with high uncertainties. CFC will affect the rate of global warming and different parts of the society such as agriculture and solar energy production.
However unfortunately, projection of future CFC is challenging. Here we present the European Cloud Cover dataset that consists of satellite observations of CFC and observations (reanalysis) of air temperature, surface pressure and specific and relative humidity. The dataset can thus be used to potentially improve the projections of future CFC and again improve projection of future global warming and other climate effects. The data to compute the CFC are obtained from the The European Organisation for the Exploitation of Meteorological Satellites data portal and the other variables from European Centre for Medium-Range Weather Forecasts.
Given the large amounts of data (17 GB) with high spatial and temporal resolution and the complexities of cloud formations, we believe that machine learning can be useful to learn the relation between CFC and the other variables. To the best of our knowledge this is the first dataset of this kind.
A Python notebook to read the data is available at https://github.com/simula/european-cloud-cover.git.
Data Usage: https://eoportal.eumetsat.int/userMgmt/dataUsageHelp.faces
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