Conditional stochastic model for generating daily precipitation time series

Climate Research

Hans von Storch¹  Aristita Busuioc¹ 

关键词: Stochastic model;    Precipitation;    CCA;    Markov chain;    Monte Carlo experiments;   

DOI  :  10.3354/cr024181

来源: Inter-Research Science Publishing

【 摘 要 】

ABSTRACT: 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 canonicalcorrelation 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 Markovchain) 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 meanprecipitation 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, whichare 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. Otherstatistical 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 maximumdaily 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 beused 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.

【 授权许可】

Unknown