A Gaussian Markov random field model for total yearly precipitation over the African Sahel

A spatio-temporal model is constructed to interpolate yearly pre-cipitation data from 1982 to 1996 over the African Sahel. The pre-cipitation 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 ap-proach. The formulation as a Markov field allows for relatively efficient computations, even though the field has more than 3 · 104 nodes.