Dépôt numérique

Regional modeling of daily precipitation fields across the Great Lakes region (Canada) using the CFSR reanalysis.

Khedhaouiria, Dikra ORCID logoORCID: https://orcid.org/0000-0002-2445-9209; Mailhot, Alain ORCID logoORCID: https://orcid.org/0000-0002-5479-1421 et Favre, Anne-Catherine (2020). Regional modeling of daily precipitation fields across the Great Lakes region (Canada) using the CFSR reanalysis. Stochastic Environmental Research and Risk Assessment , vol. 34 , nº 9. pp. 1385-1405. DOI: 10.1007/s00477-019-01722-x.

Ce document n'est pas hébergé sur EspaceINRS.


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 post-processing 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.

Type de document: Article
Mots-clés libres: meta-gaussian latent field; precipitation post-processing; stochastic simulations; reanalysis;
Centre: Centre Eau Terre Environnement
Date de dépôt: 15 mai 2024 17:06
Dernière modification: 15 mai 2024 17:06
URI: https://espace.inrs.ca/id/eprint/15318

Gestion Actions (Identification requise)

Modifier la notice Modifier la notice