ensemble kalman filtering in the mitgcm with dart
DESCRIPTION
Ensemble Kalman Filtering in the MITgcm with DART. SIO: Ibrahim Hoteit Bruce Cornuelle NCAR: Jeffrey Anderson Tim Hoar Nancy Collins. MIT , Sep 22, 2008. Regional MITgcm Assimilation at SIO. Tropical Pacific 1/3 degree and 1/6 degree - PowerPoint PPT PresentationTRANSCRIPT
Ensemble Kalman Filtering in the MITgcm with DART
MIT, Sep 22, 2008
o SIO: Ibrahim Hoteit Bruce Cornuelle
o NCAR: Jeffrey Anderson Tim Hoar Nancy Collins
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o Tropical Pacific 1/3 degree and 1/6 degree
o CalCOFI 1/10 degreeo Gulf of Mexico 1/10 degreeo San Diego region 1 kmo Taiwan region (to come) All with ECCO-adjoint
assimilation
… Ensemble Kalman Filtering
Regional MITgcm Assimilation at SIO
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o What For?o Ensemble Kalman Filteringo Pros & Conso Data Assimilation Research Testbed
-- DARTo DART implementation with MITgcmo An Example of Fit
Outline
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What For?■ The BP – Scripps GOM project:
o Predict the front of the loop current in the Gulf of Mexico
o Deploy gliders and HF radars and use data assimilation
o 1/10o Gulf of Mexico MITgcm forced by ECCO
■ Ensemble Kalman Filtering with MITgcm: o No update of the background covariance in the
adjoint method o Proved better for forecasting o Add new assimilation capability to MITgcm
(complement adjoint?)
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Analysis Step
Ensemble Kalman Filtering (EnKF)
Forecast Step • Correct forecast
• Update error covariance
• Kalman Gain
P P Pa f f fk k k k kG H
• Integrate analysis
• Update error covariance
, 1 1 , 1P P Qf a Tk k k k k k kM M 1P [ P R ]f T f T
k k k k k k kG H H H
[ ]a f fk k k k k kx x G y H x
, 1 1f ak k k kx M x
■ EnKFs: Represent uncertainties about the state estimate by an ensemble of points
Integrate ensemble with
ModelP =
Cov(ensemble)
Kalman FilterEnsemble Kalman Filter
■ Easily portable■ Provide estimates of the background
covariance matrix■ Offers more flexibility from an OI to Ensemble Kalman
filters
Pros & Cons
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■ Rank deficient Localization and Inflation are needed!
■ A software facility employing different EnKFs
■ DART is designed so that incorporating new models and observations requires minimal coding of a small set of interface routines
■ Advanced localization/inflation schemes■ Operationally used with CAM and WRF at
NCAR, MA2 at GFDL, and elsewhere …
Data Assimilation Research Testbed --DART
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DART Implementation with MITgcm
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■ No modification to the MITgcm■ Scaling of DART parallel algorithm is
independent of model■ Enabled for assimilation of most ocean data
sets
MITgcmIntegrate ensemble in time
DATA
DARTUpdate
ensemble with Data
INTERFACE
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An Example of Fit
Pseudo-DATA Posterior
July 1st, 1996
12 weeks later
Prior
30 members
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■ The machinery is now working
■ Early testing shows good fit
■ ... Will be tested with real data
Thank you
To conclude