Articles | Volume 23, issue 2
https://doi.org/10.5194/hess-23-773-2019
© Author(s) 2019. 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-23-773-2019
© Author(s) 2019. This work is distributed under
the Creative Commons Attribution 4.0 License.
the Creative Commons Attribution 4.0 License.
Research article
| 
12 Feb 2019
Research article |
| 12 Feb 2019
| 12 Feb 2019
Multivariate stochastic bias corrections with optimal transport
Laboratoire des Sciences du Climat et de l'Environnement, UMR 8212,
CEA-CNRS-UVSQ, IPSL & U Paris-Saclay, Gif-sur-Yvette, France
Mathieu Vrac
Laboratoire des Sciences du Climat et de l'Environnement, UMR 8212,
CEA-CNRS-UVSQ, IPSL & U Paris-Saclay, Gif-sur-Yvette, France
Philippe Naveau
Laboratoire des Sciences du Climat et de l'Environnement, UMR 8212,
CEA-CNRS-UVSQ, IPSL & U Paris-Saclay, Gif-sur-Yvette, France
Pascal Yiou
Laboratoire des Sciences du Climat et de l'Environnement, UMR 8212,
CEA-CNRS-UVSQ, IPSL & U Paris-Saclay, Gif-sur-Yvette, France
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Saved (final revised paper)
Latest update: 09 Oct 2026
Short summary
Bias correction methods are used to calibrate climate model outputs with respect to observations. In this article, a non-stationary, multivariate and stochastic bias correction method is developed based on optimal transport, accounting for inter-site and inter-variable correlations. Optimal transport allows us to construct a joint distribution that minimizes energy spent in bias correction. Our methodology is tested on precipitation and temperatures over 12 locations in southern France.
Bias correction methods are used to calibrate climate model outputs with respect to...
