Statistics Seminar
School of Mathematics
The University of Edinburgh
Friday 10 October 2008
3.00 p.m. Room 5327, James Clerk Maxwell Building
Chris Glasbey (Biomathematics and Statistics Scotland)
Spatio-temporal weather models
(joint with Dave Allcroft)
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.
Tea and coffee will be available after the seminar in the Mathematics
Common Room (5212).
Natalia Bochkina
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