Ben Alaya, Mohamed Ali; Chebana, Fateh 
ORCID: https://orcid.org/0000-0002-3329-8179 et Ouarda, Taha B. M. J. ORCID: https://orcid.org/0000-0002-0969-063X
(2016).
Multisite and multivariable statistical downscaling using a Gaussian copula quantile regression model.
Climate Dynamics
, vol. 47
, nº 5-6.
pp. 1383-1397.
DOI: 10.1007/s00382-015-2908-3.
Résumé
Statistical downscaling techniques are required to refine atmosphere–ocean global climate data and provide reliable meteorological information such as a realistic temporal variability and relationships between sites and variables in a changing climate. To this end, the present paper introduces a modular structure combining two statistical tools of increasing interest during the last years: (1) Gaussian copula and (2) quantile regression. The quantile regression tool is employed to specify the entire conditional distribution of downscaled variables and to address the limitations of traditional regression-based approaches whereas the Gaussian copula is performed to describe and preserve the dependence between both variables and sites. A case study based on precipitation and maximum and minimum temperatures from the province of Quebec, Canada, is used to evaluate the performance of the proposed model. Obtained results suggest that this approach is capable of generating series with realistic correlation structures and temporal variability. Furthermore, the proposed model performed better than a classical multisite multivariate statistical downscaling model for most evaluation criteria.
| Type de document: | Article |
|---|---|
| Mots-clés libres: | climate downscaling; Gaussian copula; quantile regression; temperature; precipitation; multisite; multivariable |
| Centre: | Centre Eau Terre Environnement |
| Date de dépôt: | 07 mai 2018 18:32 |
| Dernière modification: | 21 févr. 2022 17:24 |
| URI: | https://espace.inrs.ca/id/eprint/6314 |
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