Ensemble forecasting and
data assimilation in coupled systems
Eugenia Kalnay
Department of Meteorology and the Chaos Group
University of Maryland
References and thanks:
"An ensemble
forecast starts from..."
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The errors of the day are
instabilities of the background flow. At the same verification time, the
forecast uncertainties have the same shape
Strong instabilities of
the background tend to have simple shapes (perturbations lie in a
low-dimensional subspace)
Errors of the day
One approach to create
initial perturbations for ensemble forecasting with errors of the day: breeding
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Data assimilation:
combine a forecast with observations. We make a temperature forecast Tb
and then take an observation To.
A popular way to optimally estimate the truth (analysis) is to minimize the
“3D-Var” cost function:
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The solution: Ensemble
Kalman Filtering
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This advantage continues
into the 3-day forecasts
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Results with Lorenz 40
variable model
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Preliminary LEKF results
with NCEP’s global model
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However, two remaining
problems…
"The atmosphere has
coupled instabilities..."
To help understand the
problem in coupled systems, test breeding in a coupled system. The results
should be valid for other nonlinear approaches such as EnKF.
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The two rules are very
robust, with threat scores >90%
Breeding in a coupled
system
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Results from Lorenz
coupled models
Example of bred vectors
(contours) associated with equatorial unstable waves (color) in the NASA
coupled GCM. The bred vectors (contours) give the most unstable perturbations,
a powerful tool for a dynamical analysis. (Yang et al, 2003)
Experiments with coupled
systems
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Breeding with the NSIPP
coupled GCM (10 year run)
Regression maps with BV
NINO3 index
Regression maps with BV
NINO3 index
Background ENSO vs. ENSO “embryo”
Summary about breeding in
a coupled system
Tentative conclusions
about data assimilation in coupled systems with multiple time scales