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
(2015).
Probabilistic Multisite Statistical Downscaling for Daily Precipitation Using a Bernoulli-Generalized Pareto Multivariate Autoregressive Model.
Journal of Climate
, vol. 28
.
pp. 2349-2364.
DOI: 10.1175/JCLI-D-14-00237.1.
Résumé
A Bernoulli-Generalized Pareto multivariate autoregressive (BMAR) model is proposed in this paper for multisite statistical downscaling of daily precipitations. The proposed model relies on a probabilistic framework in order to describe the conditional probability density function of precipitation at each station for a given day and handles multivariate dependence in both time and space using a multivariate autoregressive model. In a probabilistic framework, BMAR employs a regression model whose outputs are parameters of the mixed Bernoulli-Generalized Pareto distribution. As a stochastic component, the BMAR employs a latent multivariate autoregressive Gaussian field to preserve lag-0 and lag-1 cross-correlations of precipitation at multiple sites. The proposed model is applied for the downscaling of AOGCM data to daily precipitation in the southern part of Quebec, Canada. Reanalysis products are used in this study to assess the potential of the proposed method. Based on the mean errors (ME), the root mean square errors (RMSE), precipitations indices, and the ability to preserve lag-0 and lag-1 cross-correlation, results of the study indicate the superiority of the proposed model over a multivariate multiple linear regression (MMLR) model and a multisite hybrid statistical downscaling procedure that combines MMLR and a stochastic generator schemes.
| Type de document: | Article |
|---|---|
| Mots-clés libres: | regression analysis; time series; probability forecasts/models/distribution; statistical forecasting; reanalysis data; stochastic models |
| Centre: | Centre Eau Terre Environnement |
| Date de dépôt: | 23 avr. 2018 15:13 |
| Dernière modification: | 21 févr. 2022 17:30 |
| URI: | https://espace.inrs.ca/id/eprint/3886 |
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