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A hidden seasonal switching model for high‐resolution breakpoint rainfall data
Author(s) -
Sansom John,
Thomson Peter
Publication year - 2010
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 - 10.1029/2009wr008602
Subject(s) - homogeneous , breakpoint , seasonality , hidden semi markov model , hidden markov model , markov chain , invariant (physics) , climatology , maximum likelihood , meteorology , statistics , mathematics , econometrics , markov model , computer science , geography , artificial intelligence , variable order markov model , geology , biology , combinatorics , biochemistry , chromosomal translocation , gene , mathematical physics
A nonhomogeneous hidden semi‐Markov model (NHSMM) for breakpoint rainfall data is proposed which extends the homogeneous hidden semi‐Markov model (HSMM) of Sansom and Thomson (2001) to incorporate stochastic seasonality. The NHSMM model is able to switch seasons at times that are earlier or later than expected and, in this way, is able to explain additional seasonal variability due to varying length seasons. The model's hidden rainfall states align with precipitation mechanisms that are seasonally invariant, but the state dynamics are assumed to vary with season. Recursions for constructing the likelihood are developed and the EM algorithm used to fit the parameters of the model. An application of the model to breakpoint rainfall measurements from Invercargill, New Zealand, is discussed, and the results of fitting a number of different NHSMMs are compared to those from fitting the non‐seasonal homogeneous HSMM.