Conditional stochastic model for generating daily precipitation time series

The purpose of this paper is the construction of a conditional stochastic model to generate daily precipitation time series. The model is a mixture of a 2-state first-order Markov chain and a statistical downscaling model based on canonical correlation analysis (CCA). The CCA model links the large-scale circulation, represented by the European sea-level pressure (SLP) field, with 4 precipitation distribution parameters: i.e. 2 transition probabilities and 2 gamma distribution parameters. This model is tested for the Bucharest station, for which long observed daily time series were available (1901-1999). The comparison of the capabilities of the conditional stochastic model and an unconditional stochastic model (based only on a Markov chain) is presented using ensembles of 1000 runs of the 2 models. The performance of the conditional stochastic model is analyzed in 2 steps. First, the ability of the CCA model for estimating the 4 precipitation distribution parameters is assessed. Second, the performance of both stochastic models in reproducing the statistical features of the observed precipitation time series is analyzed. The CCA model is most accurate for winter and autumn (transition probabilities), less accurate for the mean precipitation amount on wet days and inaccurate for the shape parameter. There are no significant dissimilarities between the conditional and unconditional models regarding their performance except for the linear trend and interannual variability, which are better captured by the conditional model. Some statistical features are well reproduced by both stochastic models for all seasons, such as mean and expected maximum duration of wet/dry intervals, daily mean of precipitation for wet days. Other statistical features are only partially reproduced by both models or are better reproduced by one of the models, such as mean duration of dry interval, standard deviation of daily precipitation amount, seasonal mean of rainy days and expected maximum daily precipitation. For all seasons, generally, the frequency of shorter dry intervals is underestimated and that of longer dry intervals (greater than 9 d) is overestimated. In conclusion, the conditional stochastic model presented in this paper can be used to generate daily precipitation time series for winter and autumn. For the other seasons, the unconditional model can be used to reproduce some statistical features.