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On Binary Time Series Obtained From Continuous Time Point Processes Describing Rainfall
Author(s) -
Guttorp Peter
Publication year - 1986
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/wr022i006p00897
Subject(s) - series (stratigraphy) , binary number , point process , process (computing) , poisson distribution , time series , computer science , precipitation , mathematics , statistical physics , statistics , meteorology , geology , geography , physics , paleontology , arithmetic , operating system
Recent work on modeling rainfall occurrence has used continuous time point process models. In many cases the available data is not sufficiently precise to properly fit such a model. For example, if only occurrence or nonoccurrence of precipitation is given daily, a more appropriate model would be a binary time series. In this work we relate some parameters of the time series thus obtained to parameters of the underlying (but not observed) continuous time process. We carry out the details for several models proposed in the literature, such as the Neyman‐Scott Poisson cluster process and the Smith‐Karr renewal Cox process. An application of the theory is illustrated on some Washington State rainfall data.