An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross‐validation experiment
By J. Gutiérrez, Douglas Maraun, M. Widmann, R. Huth, E. Hertig, R. Benestad, O. Rössler, J. Wibig, Renate A. I. Wilcke, S. Kotlarski and 28 more. 2016
256 citations recorded by the source corpus.
VALUE is an open European collaboration to intercompare downscaling approaches for climate change research, focusing on different validation aspects (marginal, temporal, extremes, spatial, process‐based, etc.). Here we describe the participating methods and first results from the first experiment, using “perfect” reanalysis (and reanalysis‐driven regional climate model (RCM)) predictors to assess the intrinsic performance of the methods for downscaling precipitation and temperatures over a set of 86 stations representative of the main climatic regions in Europe. This study constitutes the largest and most comprehensive to date intercomparison of statistical downscaling methods, covering the three common downscaling approaches (perfect prognosis, model output statistics—including bias correction—and weather generators) with a total of over 50 downscaling methods representative of the most common techniques.
Subfields: Atmospheric Science, Global and Planetary Change
View Paper: https://www.semanticscholar.org/paper/ba813b2558f54600bf7ace4309210a735f2e4346
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In Atmospheric Science
- P. Ramos-Calzado
- M. Pastor-Saavedra
- E. Rodríguez‐Camino
- J. García‐Valero
- Alfonso Hernanz
- M. Domínguez
In Global and Planetary Change
- P. Ramos-Calzado
- M. Pastor-Saavedra
- E. Rodríguez‐Camino
- J. García‐Valero
- Alfonso Hernanz
- M. Domínguez
Citations in the corpus
- Evaluation of statistical downscaling methods for climate change projections over Spain: Present conditions with perfect predictors (2021, methodology): “The five methods evaluated in this article are applied in Perfect Prognosis (PP) mode (see Gutiérrez et al., 2019; Hertig et al., 2019); when used to generate regional climate scenarios they use predictors from reanalysis for calibration and are applied over GCMs projections.”