We propose a vector auto-regressive moving average process as a model for d
aily weather data. For the rainfall variable a monotonic transformation is
applied to achieve marginal normality, thus, defining a latent variable, wi
th zero rainfall data corresponding to censored values below a threshold. M
ethodology is presented for model identification, estimation and validation
, illustrated using data from Mylnefield, Scotland. The new model, a vector
second-order auto-regressive first-order moving average (VARMA(2,1)) proce
ss, fits the data better, and produces more realistic simulated series than
, existing models of Richardson [Water Resources Res. 17 (1981) 182] and Pe
iris and McNicol [Agric. Forest Meteorol. 79 (1996) 219]. (C) 2001 Elsevier
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