Disaggregating daily precipitations into hourly values with a transformed censored latent Gaussian process

Original Article

Abstract

A problem often encountered in agricultural and ecological modeling is to disaggregate daily precipitations into vectors of hourly precipitations used as input values by crop and plant models. A stochastic model for rainfall data, based on transformed censored latent Gaussian process is described. Compared to earlier similar work, our transform function provides an accurate fit for both the body and the heavy tail of the precipitation distribution. Simple empirical relationships between the parameters estimated at different time scales are established. These relationships are used for the disaggregation of daily values at stations where hourly values are not available. The method is illustrated on two stations located in the Paris basin.

Keywords

Stochastic weather generator Composite likelihood  Pairwise likelihood Truncated Gaussian process 

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Copyright information

© Springer-Verlag Berlin Heidelberg 2014

Authors and Affiliations

  1. 1.UR546 Biostatistics and Spatial Processes (BioSP)INRAAvignonFrance

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