Regional modeling of daily precipitation fields across the Great Lakes region (Canada) using the CFSR reanalysis
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ORIGINAL PAPER
Regional modeling of daily precipitation fields across the Great Lakes region (Canada) using the CFSR reanalysis Dikra Khedhaouiria1
•
Alain Mailhot1 • Anne-Catherine Favre2
Springer-Verlag GmbH Germany, part of Springer Nature 2019
Abstract High densities of local-scale daily precipitation series across relatively large domains are of special interest for a wide range of applications (e.g., hydrological modeling, agriculture). The focus of the present study is to post-process gridded precipitation from a single reanalysis to correct bias and scale mismatch with observations, and to extend the same postprocessing at sites without historical data. A Stochastic Model Output Statistical approach combined with meta-Gaussian spatiotemporal random fields, calibrated at sites, is employed to post-process the Climate Forecast System Reanalysis (CFSR) precipitation. The post-processed data, characterized by local parameters, is then mapped across the Great Lakes region (Canada) using two different approaches: (1) kriging, and (2) Vector Generalized Additive Model (VGAM) with spatial covariates. The kriging enables the interpolation of these parameters, while the spatial VGAM helps to spatially post-process CFSR precipitation using a single model. The k-fold cross-validation procedure is employed to assess the ability of the two approaches to predict selected characteristics and climate indices. The kriging and spatial VGAM approaches modeled effectively the distribution of the precipitation process to similar extents (e.g., mean daily precipitation, variability and the number of wet days). The kriging approach produces slightly better estimates of climate indices than the spatial VGAM models. Both approaches demonstrate significant improvement of the metric estimation compared to those of CFSR without post-processing. Keywords Meta-Gaussian latent field Precipitation post-processing Stochastic simulations Reanalysis
1 Introduction Several applications in hydrology, impact studies or in agricultural impact assessments (Ambrosino et al. 2014), often require high-quality local precipitation series, as opposed to gridded precipitation series. Ideally, spatiotemporal precipitation fields covering large domains and long periods are needed. This allows, for example, continuous modeling of the infiltration-runoff across a basin, Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00477-019-01722-x) contains supplementary material, which is available to authorized users. & Dikra Khedhaouiria [email protected] 1
Institut national de la recherche scientifique, Centre Eau Terre Environnement (INRS-ETE), Quebec, Canada
2
CNRS, IRD, IGE, Grenoble INP, University of Grenobles Alpes, 38000 Grenoble, France
and thus improves tools used for risk assessment (Lamb et al. 2016). Across Canada, and for many other countries, precipitation records do not fulfill these requirements (Kidd et al. 2017), especially in northern regions which are expected to develop i
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