Weather modelling using a multivariate latent Gaussian model

We propose a vector auto-regressive moving average process as a model for daily weather data. For the rainfall variable a monotonic transformation is applied to achieve marginal normality, thus, defining a latent variable, with zero rainfall data corresponding to censored values below a threshold. M...

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Bibliographic Details
Published in:Agricultural and forest meteorology 2001-09, Vol.109 (3), p.187-201
Main Authors: Durban, M., Glasbey, C.A.
Format: Article
Language:English
Subjects:
Agricultural and forest climatology and meteorology. Irrigation. Drainage
Agricultural and forest meteorology
Agronomy. Soil science and plant productions
Auto-correlation
Biological and medical sciences
Climatology, meteorology
Fundamental and applied biological sciences. Psychology
General agronomy. Plant production
Generalities. Techniques. Climatology. Meteorology. Climatic models of plant production
Likelihood
Rainfall
Simulation
Vector auto-regressive moving average process
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Summary:We propose a vector auto-regressive moving average process as a model for daily weather data. For the rainfall variable a monotonic transformation is applied to achieve marginal normality, thus, defining a latent variable, with zero rainfall data corresponding to censored values below a threshold. Methodology 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)) process, fits the data better, and produces more realistic simulated series than, existing models of Richardson [Water Resources Res. 17 (1981) 182] and Peiris and McNicol [Agric. Forest Meteorol. 79 (1996) 219].
ISSN:0168-1923
1873-2240
DOI:10.1016/S0168-1923(01)00268-4