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

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Abstract

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).

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Authors and Affiliations

  1. Pacific Climate Impacts Consortium, University of Victoria, PO Box 1700, Stn CSC, Victoria, BC, V8W2Y2, Canada

    M. A. Ben Alaya

  2. INRS-ETE, 490 rue de la Couronne, Quebec, QC, G1K 9A9, Canada

    T. B. M. J. Ouarda & F. Chebana

  3. Masdar Institute of Science and Technology, P.O. Box 54224, Abu Dhabi, United Arab Emirates

    T. B. M. J. Ouarda

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  1. M. A. Ben Alaya
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  2. T. B. M. J. Ouarda
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  3. F. Chebana
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Correspondence to M. A. Ben Alaya.

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Ben Alaya, M.A., Ouarda, T.B.M.J. & Chebana, F. Non-Gaussian spatiotemporal simulation of multisite daily precipitation: downscaling framework. Clim Dyn 50, 1–15 (2018). https://doi.org/10.1007/s00382-017-3578-0

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