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WP4: Statistical-dynamic up- and downscaling of meteorological data

Icon for WP4: Two squares, one is the detail of the other

Climate projections, commonly made at rather coarse resolutions (e.g. 100km), must be downscaled to a resolution relevant for future renewable energy resource assessment, while the high-resolution results of WP1-3 must be up scaled to regional and even global scales. For example, a critical input for energy economics modeling is the statistics of the prevailing weather patterns, which need to be derived from historical and future climate data. On the other hand, to extrapolate the high-resolution from the Jülich Observatory for Cloud Evolution (JOYCE) site to a large region requires the characterization and generalization of the environmental setting for cloud development and evolution, e.g., land surface patterns. In WP4, we will first analyze the historical data and IPCC AR5 climate projections to identify the prevailing weather patterns and their statistics using the techniques of current weather type (CWT) analysis and empirical orthogonal functions. Historical data are available from the ECMWF (European Center for Middle Range Weather Forecast) and the climate projection data are available from the IPCC AR5 achieves. The regional climate modeling system CEMSYS being constructed at UoC will be used for renewable energy simulation in central Europe. To this end, CEMSYS will be centered at the JOYCE site and run in statistical-dynamic downscaling mode for long-term (annual to decadal) solar-and-wind combined renewable energy modeling with a resolution of 10 km. The JOYCE site data from monitoring and modeling will be compared with and used to validate the CEMSYS simulations. As soon as the new ICON model becomes publically available, we will explore its possible applications to the renewable energy simulations and its coupling with energy economics models.


  • Prof. Dr. Yaping Shao (IGM)
  • PD Dr. Ulrich Löhnert (IGM)
  • Prof. Dr. Roel Neggers (IGM)
  • Prof. Dr. Felix Höffler (EWI)
  • PD Dr. Dietmar Lindenberger (EWI)