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A spatiotemporal precipitation generator based on a censored latent Gaussian field

A. Baxevani ; Jan Lennartsson (Institutionen för matematiska vetenskaper, matematisk statistik)
Water Resources Research (0043-1397). Vol. 51 (2015), 6, p. 4338-4358.
[Artikel, refereegranskad vetenskaplig]

A daily stochastic spatiotemporal precipitation generator that yields precipitation realizations that are quantitatively consistent is described. The methodology relies on a latent Gaussian field that drives both the occurrence and intensity of the precipitation process. For the precipitation intensity, the marginal distributions, which are space and time dependent, are described by a composite model of a gamma distribution for observations below some threshold with a generalized Pareto distribution modeling the excesses above the threshold. Model parameters are estimated from data and extrapolated to locations and times with no direct observations using linear regression of position covariates. One advantage of such a model is that stochastic generator parameters are readily available at any location and time of the year inside the stationarity regions. The methodology is illustrated for a network of 12 locations in Sweden. Performance of the model is judged through its ability to accurately reproduce a series of spatial dependence measures and weather indices. © 2015. American Geophysical Union. All Rights Reserved.

Nyckelord: censoring, covariance structure, extremes, precipitation modeling, space-time latent Gaussian field



Denna post skapades 2015-08-21. Senast ändrad 2015-10-16.
CPL Pubid: 220991

 

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Institutioner (Chalmers)

Institutionen för matematiska vetenskaper, matematisk statistik (2005-2016)

Ämnesområden

Matematik

Chalmers infrastruktur