A Gaussian Markov random field model for total yearly precipitation over the African Sahel
Lindström, Johan LU
and Lindgren, Finn
LU
(2008)
In Preprints in Mathematical Sciences
2008:8.
- Abstract
- 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... (More) - 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. (Less)
Please use this url to cite or link to this publication:
https://lup.lub.lu.se/record/4730170
- author
- Lindström, Johan
LU
and Lindgren, Finn
LU
- organization
- publishing date
- 2008
- type
- Contribution to journal
- publication status
- unpublished
- subject
- in
- Preprints in Mathematical Sciences
- volume
- 2008:8
- pages
- 22 pages
- publisher
- Lund University
- external identifiers
-
- other:LUTFMS-5074-2008
- ISSN
- 1403-9338
- project
- Spatio-Temporal Estimation for Mixture Models and Gaussian Markov Random Fields - Applications to Video Analysis and Environmental Modelling
- language
- English
- LU publication?
- yes
- id
- ed5e68fb-818c-4f15-b447-413dbdff9fe4 (old id 4730170)
- date added to LUP
- 2016-04-01 13:15:48
- date last changed
- 2018-11-21 20:14:17
@article{ed5e68fb-818c-4f15-b447-413dbdff9fe4,
abstract = {{A spatio-temporal model is constructed to interpolate yearly precipitation<br/><br>
data from 1982 to 1996 over the African Sahel. The precipitation<br/><br>
data used in the analysis comes from the Global Historical<br/><br>
Climatology Network.<br/><br>
The spatio-temporal model is based on a Gaussian Markov random<br/><br>
field approach with AR(1)-dependence in time and a spatial component<br/><br>
modeled using an approximation of a field withMat´ern covariance. The<br/><br>
model is defined on an irregular grid on a segment of the sphere, both<br/><br>
avoiding the issue of matching observations to a regularly spaced grid,<br/><br>
and handling the curvature of the Earth.<br/><br>
The model is estimated using a Markov chain Monte Carlo approach.<br/><br>
The formulation as a Markov field allows for relatively efficient<br/><br>
computations, even though the field has more than 3*10^4 nodes.}},
author = {{Lindström, Johan and Lindgren, Finn}},
issn = {{1403-9338}},
language = {{eng}},
publisher = {{Lund University}},
series = {{Preprints in Mathematical Sciences}},
title = {{A Gaussian Markov random field model for total yearly precipitation over the African Sahel}},
url = {{https://lup.lub.lu.se/search/files/3266503/4730173.pdf}},
volume = {{2008:8}},
year = {{2008}},
}