Bulg. J. Phys. vol.44 no.2 (2017), pp. 162-188



Development of Stochastic Daily Weather Generator Conditional on Atmospheric Circulation. Part 1: Daily Precipitation Model

N. Neykov, P. Neytchev
National Institute of Meteorology and Hydrology, Bulgarian Academy of Sciences, 66 Tsarigradsko shose Blvd., 1784 Sofia, Bulgaria
Abstract. We consider development of daily precipitation models for 31 sites in Bulgaria. The precipitation processes are modeled as a two-state first-order nonstationary Markov model with mixed transition density of a discrete component at zero and a continuous component describing non-zero amounts. Binary logistic regression is used to fit the occurrence data, and the intensity series is modeled by gamma distribution. Standard software for generalized linear models can be used to perform the computations. Detailed model validation is carried out on various aspects. The proposed model reproduces well the precipitation statistics for the observed and reserved data.

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