A Gaussian Markov random field model for total yearly precipitation over the African Sahel
Author
Summary, in English
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 withMat´ern 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*10^4 nodes.
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 withMat´ern 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*10^4 nodes.
Department/s
Publishing year
2008
Language
English
Publication/Series
Preprints in Mathematical Sciences
Volume
2008:8
Full text
Document type
Journal article
Publisher
Lund University
Topic
- Probability Theory and Statistics
Status
Unpublished
ISBN/ISSN/Other
- ISSN: 1403-9338
- LUTFMS-5074-2008