Disaggregating daily precipitations into hourly values with a transformed censored latent Gaussian process
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.
KeywordsStochastic weather generator Composite likelihood Pairwise likelihood Truncated Gaussian process
We thank Emilio Porcu and Alessandro Fassò for inviting us to join this special issue. Among others, we thank Pierre Ailliot, Valérie Monbet, Philippe Naveau and Carlo Gaetan for fruitful discussions about this work. This work was part of the project CLIMATOR (2007–2010) (http://w3.avignon.inra.fr/projet_climator/) funded by ANR (The French National Research Agency). CLIMATOR was lead by Nadine Brisson who left us in Octobre 2011. We dedicate this work to her memory.
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