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Non-Gaussian spatiotemporal simulation of multisite daily precipitation: downscaling framework.


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Ben Alaya, Mohamed Ali; Ouarda, Taha B. M. J.; Chebana, Fateh (2018). Non-Gaussian spatiotemporal simulation of multisite daily precipitation: downscaling framework. Climate Dynamics , vol. 50 , nº 1-2. p. 1-15. DOI: 10.1007/s00382-017-3578-0.

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Probabilistic regression approaches for downscaling daily precipitation are very useful. They provide the whole conditional distribution at each forecast step to better represent the temporal variability. The question addressed in this paper is: how to simulate spatiotemporal characteristics of multisite daily precipitation from probabilistic regression models? Recent publications point out the complexity of multisite properties of daily precipitation and highlight the need for using a non-Gaussian flexible tool. This work proposes a reasonable compromise between simplicity and flexibility avoiding model misspecification. A suitable nonparametric bootstrapping (NB) technique is adopted. A downscaling model which merges a vector generalized linear model (VGLM as a probabilistic regression tool) and the proposed bootstrapping technique is introduced to simulate realistic multisite precipitation series. The model is applied to data sets from the southern part of the province of Quebec, Canada. It is shown that the model is capable of reproducing both at-site properties and the spatial structure of daily precipitations. Results indicate the superiority of the proposed NB technique, over a multivariate autoregressive Gaussian framework (i.e. Gaussian copula).

Type de document: Article
Mots-clés libres: statistical downscaling; vector generalized linear model; multisite daily precipitation; copula; multivariate autoregressive; Gaussian field; binary entropy; non parametric bootstrapping
Centre: Centre Eau Terre Environnement
Date de dépôt: 29 janv. 2018 21:11
Dernière modification: 09 mai 2018 19:39
URI: http://espace.inrs.ca/id/eprint/6343

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