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Project: "Downscaling of precipitation: development, calibration and validation of a probabilisitc Bayesian approach"

Partners:
  • University of Siegen, Chair of Water Resources Management and Climate Impact Research, Prof. P. Reggiani
Sponsor:
  • German Research Foundation (DFG)
Duration:
  • 2 years
Short description:

Downscaling of atmospheric model output is necessary to map variables from low-resolution spatial scales of observation or model prediction down to local scales, at which variables are needed for a wide range of applications, including data gap filling, hydrological or glaciological predictions, climate prognosis, irrigation or energy forecasting. Statistical downscaling is performed by seeking stochastic relationships between large-scale observed indicators and/or model output, serving as predictors, and a local-scale predictand. The underlying transformations are usually linear regressions, or more general non-linear transformations, such as quantile matching. In both cases, stationary homoscedastic relationships between stochastic variables are assumed, which correctly map the conditional mean across the transformation, but not necessarily the tails of the distributions, which characterize extreme meteorological events.

 
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