MPO 674 Lecture 20 3/26/15. 3d-Var vs 4d-Var.
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Transcript of MPO 674 Lecture 20 3/26/15. 3d-Var vs 4d-Var.
Ensemble Kalman Filters
• Want flow-dependent, dynamical covariances• Several different types of Kalman filter exist, all of
which have a linear inference. Non-linear filters are too hard. Seek simple approximations ...
• Ensemble Kalman filters use Pf = Zf ZfT
Zf is an n x k matrix containing k ensemble perturbations (about a mean state) of length n.
Perturbation
Pf = Zf ZfT
(u’1)1 (u’1)2 (u’1)3
(v’1)1 (v’1)2 (v’1)3
(T’1)1 (T’1)2 (T’1)3
(p’1)1 (p’1)2 (p’1)3
(u’2)1 (u’2)2 (u’2)3
(v’2)1 (v’2)2 (v’2)3
(T’2)1 (T’2)2 (T’2)3
(p’2)1 (p’2)2 (p’2)3
(u’3)1 (u’3)2 (u’3)3
… … …
… … …
(u’1)1 (v’1)1 (T’1)1 (p’1)1 (u’2)1 (v’2)1 (T’2)1 (p’2)1 (u’3)1
(u’1)2 (v’1)2 (T’1)2 (p’1)2 (u’2)2 (v’2)2 (T’2)2 (p’2)2 (u’3)2
(u’1)3 (v’1)3 (T’1)3 (p’1)3 (u’2)3 (v’2)3 (T’2)3 (p’2)3 (u’3)3
1 2 3 Ensemble members
Pf = Zf ZfT
(u’1)1 (v’1)1 (T’1)1 (p’1)1 (u’2)1 (v’2)1 (T’2)1 (p’2)1 (u’3)1
(u’1)2 (v’1)2 (T’1)2 (p’1)2 (u’2)2 (v’2)2 (T’2)2 (p’2)2 (u’3)2
(u’1)3 (v’1)3 (T’1)3 (p’1)3 (u’2)3 (v’2)3 (T’2)3 (p’2)3 (u’3)3
(u’1)1 (u’1)2 (u’1)3
(v’1)1 (v’1)2 (v’1)3
(T’1)1 (T’1)2 (T’1)3
(p’1)1 (p’1)2 (p’1)3
(u’2)1 (u’2)2 (u’2)3
(v’2)1 (v’2)2 (v’2)3
(T’2)1 (T’2)2 (T’2)3
(p’2)1 (p’2)2 (p’2)3
(u’3)1 (u’3)2 (u’3)3
… … …
… … …
1 2 3 Ensemble members
Pf = Zf ZfT
(u’1)1 (v’1)1 (T’1)1 (p’1)1 (u’2)1 (v’2)1 (T’2)1 (p’2)1 (u’3)1
(u’1)2 (v’1)2 (T’1)2 (p’1)2 (u’2)2 (v’2)2 (T’2)2 (p’2)2 (u’3)2
(u’1)3 (v’1)3 (T’1)3 (p’1)3 (u’2)3 (v’2)3 (T’2)3 (p’2)3 (u’3)3
1 2 3 Ensemble members
(u’1)1 (u’1)2 (u’1)3
(v’1)1 (v’1)2 (v’1)3
(T’1)1 (T’1)2 (T’1)3
(p’1)1 (p’1)2 (p’1)3
(u’2)1 (u’2)2 (u’2)3
(v’2)1 (v’2)2 (v’2)3
(T’2)1 (T’2)2 (T’2)3
(p’2)1 (p’2)2 (p’2)3
(u’3)1 (u’3)2 (u’3)3
… … …
… … …
Example of covariance localization
Background-error correlations estimated from 25 members of a 200-member ensemble exhibit a large amount of structure that does not appear to have any physical meaning. Without correction, an observation at the dotted location would produce increments across the globe.
Proposed solution is element-wise multiplication of the ensemble estimates (a) with a smooth correlation function (c) to produce (d), which now resembles thelarge-ensemble estimate (b). This has been dubbed “covariance localization.”
from Hamill, Chapter 6 of “Predictability of Weather and Climate”
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