Output details
15 - General Engineering
Heriot-Watt University (joint submission with University of Edinburgh)
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Output 0 of 0 in the submission
Article title
Nested sampling particle filter for nonlinear data assimilation
Type
D - Journal article
DOI
Title of journal
Quarterly Journal of the Royal Meteorological Society
Article number
n/a
Volume number
n/a
Issue number
n/a
First page of article
-
ISSN of journal
0035-9009
Year of publication
2013
URL
-
Number of additional authors
2
Additional information
This paper presented a completely new data-assimilation technique for updating the states of a computer model with sparse observations, to improve predictability. The approach was demonstrated on a challenging problem representing the dynamics of a climate model without assuming the states as Gaussian. This approach can also be applied to nuclear disposal sites, contaminant transport and oil recovery problem. This paper is a result of a multi-disciplinary, international collaboration (UT-Austin, KAUST). This work was funded through the KAUST UT-Austin Academic Excellence Alliance Program (UT-Austin office of sponsored projects OSP-200702891, $3,001,332).
Interdisciplinary
Yes
Cross-referral requested
-
Research group
C - Energy & Resource Management
Proposed double-weighted
No
Double-weighted statement
-
Reserve for a double-weighted output
No
Non-English
No
English abstract
-
