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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34 citations as recorded by crossref.
- Distribution-based pooling for combination and multi-model bias correction of climate simulations M. Vrac et al. 10.5194/esd-15-735-2024
- Projected increase in global compound agricultural drought and hot events under climate change W. Shi et al. 10.1016/j.gloplacha.2025.104962
- Is time a variable like the others in multivariate statistical downscaling and bias correction? Y. Robin & M. Vrac 10.5194/esd-12-1253-2021
- Advancing research on compound weather and climate events via large ensemble model simulations E. Bevacqua et al. 10.1038/s41467-023-37847-5
- Direct and indirect application of univariate and multivariate bias corrections on heat-stress indices based on multiple regional-climate-model simulations L. Qiu et al. 10.5194/esd-14-507-2023
- Time of emergence of compound events: contribution of univariate and dependence properties B. François & M. Vrac 10.5194/nhess-23-21-2023
- Correcting biases in tropical cyclone intensities in low-resolution datasets using dynamical systems metrics D. Faranda et al. 10.1007/s00382-023-06794-8
- R2D2 v2.0: accounting for temporal dependences in multivariate bias correction via analogue rank resampling M. Vrac & S. Thao 10.5194/gmd-13-5367-2020
- Opening Pandora's box: reducing global circulation model uncertainty in Australian simulations of the carbon cycle L. Teckentrup et al. 10.5194/esd-14-549-2023
- Differentiated regional impacts of future meteorological changes and anthropogenic emission control on PM2.5 concentrations in China Y. Jiang et al. 10.1016/j.horiz.2025.100151
- Bias Correction for Climate Model Output (1) Method Characteristics and Classification S. WATANABE 10.3178/jjshwr.33.243
- Uni- and multivariate bias adjustment of climate model simulations in Nordic catchments: Effects on hydrological signatures relevant for water resources management in a changing climate F. Tootoonchi et al. 10.1016/j.jhydrol.2023.129807
- Contrasting changes in hydrological processes of the Volta River basin under global warming M. Dembélé et al. 10.5194/hess-26-1481-2022
- Multimodel Errors and Emergence Times in Climate Attribution Studies P. Naveau & S. Thao 10.1175/JCLI-D-21-0332.1
- Bias correction of WRF output for operational avalanche forecasting in the Indian Himalayan region N. Samal & S. Jha 10.1007/s12040-022-01899-w
- Adapting rainfall bias-corrections to improve hydrological simulations generated from climate model forcings D. Robertson et al. 10.1016/j.jhydrol.2023.129322
- Combining global climate models using graph cuts S. Thao et al. 10.1007/s00382-022-06213-4
- Extreme gradient and boosting algorithm for improved bias-correction and downscaling of CMIP6 GCM data across indian river basin C. Thakur et al. 10.1016/j.ejrh.2025.102443
- Near-term impacts of climate variability and change on hydrological systems in West and Central Africa M. Sidibe et al. 10.1007/s00382-019-05102-7
- Making the Output of Seasonal Climate Models More Palatable to Agriculture: A Copula-Based Postprocessing Method M. Li et al. 10.1175/JAMC-D-19-0093.1
- Impact of bias nonstationarity on the performance of uni- and multivariate bias-adjusting methods: a case study on data from Uccle, Belgium J. Van de Velde et al. 10.5194/hess-26-2319-2022
- Multivariate bias corrections of climate simulations: which benefits for which losses? B. François et al. 10.5194/esd-11-537-2020
- On deep learning-based bias correction and downscaling of multiple climate models simulations F. Wang & D. Tian 10.1007/s00382-022-06277-2
- Changes in temperature–precipitation correlations over Europe: are climate models reliable? M. Vrac et al. 10.1007/s00382-022-06436-5
- Future nuclear power outages in a changing climate - A case study on two contrasted French power plants L. Collet et al. 10.1016/j.energy.2025.135207
- Toward Utilizing Similarity in Hydrologic Data Assimilation H. Lee et al. 10.3390/hydrology11110177
- The distinct problems of physical inconsistency and of multivariate bias involved in the statistical adjustment of climate simulations M. Alavoine & P. Grenier 10.1002/joc.7878
- Adjusting spatial dependence of climate model outputs with cycle-consistent adversarial networks B. François et al. 10.1007/s00382-021-05869-8
- Projected mean and extreme precipitation based on bias-corrected simulation outputs of CORDEX Southeast Asia S. Ngai et al. 10.1016/j.wace.2022.100484
- Novel multivariate quantile mapping methods for ensemble post-processing of medium-range forecasts K. Whan et al. 10.1016/j.wace.2021.100310
- Hotspot movement of compound events on the Europe continent S. Doshi et al. 10.1038/s41598-023-45067-6
- Review of bias correction methods for climate model outputs in hydrology A. Menapace et al. 10.1016/j.jhydrol.2025.133213
- Research progresses and prospects of multi-sphere compound extremes from the Earth System perspective Z. Hao & Y. Chen 10.1007/s11430-023-1201-y
- A temporal stochastic bias correction using a machine learning attention model O. Nivron et al. 10.1017/eds.2024.42
Latest update: 13 Jul 2025
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...
