We would like to advise list members that an RSS Lancashire and East Cumbria local group seminar will take place 4-5pm on Thursday 23rd February in room A54 of the Postgraduate Statistics Centre, Lancaster University. The seminar will be followed by refreshments. All are welcome to attend.
Title: Spatio-temporal weather models
Speaker: Chris Glasbey (Biomathematics and Statistics Scotland)
Abstract:
We develop contrasting spatio-temporal models for two weather variables: solar radiation and rainfall. For solar radiation the aim is to assess the performance of area networks of photo-voltaic cells. Although radiation measured at a sufficiently fine temporal scale has a bimodal marginal distribution (Glasbey, 2001), averages of 10-minute or longer duration can be transformed to be approximately Gaussian, and we fit a spatio-temporal auto-regressive moving average (STARMA) process (Glasbey and Allcroft, 2008). For rainfall, the aim is to disaggregate to a finer spatial scale than that observed. To overcome the difficulty that the marginal distribution of hourly rainfall has a singularity at zero and so is highly non-Gaussian, we apply a monotonic transformation. This defines a latent Gaussian variable, with zero rainfall corresponding to censored values below a threshold, which we model using a spatio-temporal Gaussian Markov random field (Allcroft and Glasbey, 2003). For both models, computations are simplified by approximating space by a torus and using Fourier transforms.
Allcroft, D.J. and Glasbey, C.A. (2003). A latent Gaussian Markov random field model for spatio-temporal rainfall disaggregation. Applied Statistics, 52, 487-498.
Glasbey CA (2001). Nonlinear autoregressive time series with multivariate Gaussian mixtures as marginal distributions. Applied Statistics, 50, 143-154
Glasbey, C.A. and Allcroft, D.J. (2008). A STARMA model for solar radiation. Applied Statistics, 57, 343-355.
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