Seminar Announcement
| Spatio-Temporal Modelling of Precipitation using Gaussian Markov Random Fields |
Johan Lindstrom, Ph.D , Lund University, Sweden
Monday, September 22, 2008
4:00 p.m. 223 Weber
| ABSTRACT |
A spatio-temporal model is constructed for interpolation of yearly
precipitation data from 1982 to 1996 over the African Sahel. The
precipitation data used in the analysis comes from the Global Historical
Climatology Network.
The spatio-temporal model is based on a Gaussian Markov random field
(GMRF) with AR(1)-dependence in time and a spatial component modeled using
a GMRF that approximates a stationary field with Matern covariance. The model
is defined on an irregular grid on a segment of the sphere, handling the
curvature of the Earth and avoiding the issue of matching irregularly
spaced observations to regularly spaced grid points.
The model is estimated using a Markov chain Monte Carlo approach. The
formulation as a Markov field allows for efficient computations, even
though the data consists of more than 4000 measurement points interpolated
to a spatio-temporal field with 30000 grid points.