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Stochastic daily precipitation models: 1. A comparison of occurrence processes
Author(s) -
Rolda´n José,
Woolhiser David A.
Publication year - 1982
Publication title -
water resources research
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.863
H-Index - 217
eISSN - 1944-7973
pISSN - 0043-1397
DOI - 10.1029/wr018i005p01451
Subject(s) - akaike information criterion , markov chain , mathematics , statistics , continuous time markov chain , precipitation , additive markov chain , maximum likelihood , stochastic modelling , markov model , variable order markov model , meteorology , physics
A first‐order Markov chain and an alternating renewal process (ARP) with a truncated geometric distribution of wet day intervals and a truncated negative binomial distribution of dry day intervals are compared as models describing the occurrence of sequences of wet and dry days. Numerical optimization techniques are used to obtain approximate maximum likelihood estimates of the Fourier coefficients which describe the seasonal variation of the two Markov chain parameters and the three parameters in the alternating renewal process. For the four U.S. stations studied, the Markov chain model was superior to the ARP using the minimum Akaike information criterion.