A Gaussian Markov Random Field Model for Total Yearly Precipitation over
the African Sahel
Johan Lindström and Finn Lindgren
Centre for Mathematical Sciences
Lund Institute of Technology,
A spatio-temporal model is constructed to interpolate 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 approach
with AR(1)-dependence in time and a spatial component modeled using an
approximation of a field with Matérn covariance. The model is defined
on an irregular grid on a segment of the sphere, both avoiding the issue
of matching observations to a regularly spaced grid, and handling the curvature
of the Earth.
The model is estimated using a Markov chain Monte Carlo approach. The formulation
as a Markov field allows for relatively efficient computations, even though
the field has more than
3 x 104 nodes.