Articles | Volume 22, issue 6
https://doi.org/10.5194/hess-22-3175-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
https://doi.org/10.5194/hess-22-3175-2018
© Author(s) 2018. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Multivariate bias adjustment of high-dimensional climate simulations: the Rank Resampling for Distributions and Dependences (R2D2) bias correction
Laboratoire des Sciences du Climat et de l'Environnement (LSCE-IPSL,
CNRS), Centre d'Etudes de Saclay, Orme des Merisiers, 91190
Gif-sur-Yvette, France
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Latest update: 06 Dec 2024
Short summary
This study presents a multivariate bias correction method named R2D2 to adjust both the 1d-distributions and inter-variable/site dependence structures of climate simulations in a high-dimensional context, while providing some stochasticity. R2D2 is tested on temperature and precipitation reanalyses and illustrated on future simulations. In both cases, R2D2 is able to correct the spatial and physical dependence, opening proper use of climate simulations for impact (e.g. hydrological) models.
This study presents a multivariate bias correction method named R2D2 to adjust both the...