Por favor, use este identificador para citar o enlazar este ítem:
http://hdl.handle.net/20.500.11765/13771
An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross-validation experiment
Título : | An intercomparison of a large ensemble of statistical downscaling methods over Europe: Results from the VALUE perfect predictor cross-validation experiment |
Autor : | Gutiérrez Llorente, José Manuel; Maraun, Douglas; Widmann, Martin; Huth, Radan; Hertig, Elke; Benestad, Rasmus; Rössler, Ole; Wibig, Joanna; Wilcke, Renate Anna Irma; Kotlarski, Sven; San-Martín, Daniel; Herrera García, Sixto; Bedia, Joaquín; Casanueva, Ana; Manzanas, Rodrigo; Iturbide, Maialen; Vrac, Mathieu; Dubrovsky, Martin; Ribalaygua Batalla, Jaime; Pórtoles, Javier; Räty, O.; Räisänen, Jouni Antero; Hingray, Benoît; Raynaud, Dominique; Casado Calle, María Jesús![]() ![]() |
Palabras clave : | Downscaling; Bias adjustment; Perfect prognosis; Weather gen33 erators; Model output statistics; CORDEX; Reproducibility |
Fecha de publicación : | 2019 |
Editor: | Wiley; Royal Meteorological Society |
Citación : | International Journal of Climatology. 2019, 39(9), p. 3750-3785 |
Versión del editor: | https://doi.org/10.1002/joc.5462 |
Resumen : | 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. |
Patrocinador: | J.M.G. and S.H. acknowledge partial funding from MULTI-SDM project (MINECO/FEDER, CGL2015-66583-R). B.H. and D.R. acknowledge COMPLEX project (FP7-ENV-2012, No: 308601). M.T. was supported by HOPE project (MINECO, CGL2014-52571-R). |
URI : | http://hdl.handle.net/20.500.11765/13771 |
ISSN : | 0899-8418 1097-0088 |
Colecciones: | Artículos científicos 2019-2022 |
Ficheros en este ítem:
Fichero | Descripción | Tamaño | Formato | ||
---|---|---|---|---|---|
![]() | IJC_2019_Gutierrez.pdf | 8,59 MB | Adobe PDF | ![]() Visualizar/Abrir |
Los ítems de Arcimis están protegidos por una Licencia Creative Commons, salvo que se indique lo contrario.
