Ocean Data Variational Assimilation with OPA: Ongoing developments with OPAVAR and implementation...
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Transcript of Ocean Data Variational Assimilation with OPA: Ongoing developments with OPAVAR and implementation...
Ocean Data Variational Assimilation with OPA:
Ongoing developments with OPAVAR and implementation plan for NEMOVAR
Sophie RICCI, Anthony Weaver, Nicolas Daget, Elisabeth Remy
Sophie RICCI – Post-doct CERFACS April 19th E.G.U 2007
Outline
Sophie RICCI – Post-doct CERFACS April 19th E.G.U 2007
Ongoing developments on OPAVARBackground
Development of an ensemble Var system
Assimilation of SLA
Assimilation of SST
NEMOVAR ProjectBackground
Implementation plan
Current status
Variational Data assimilation in OPAVAR: Background
Sophie RICCI – Post-doc CERFACS April 19th E.G.U 2007
OPAVAR is a variational data assimilation system which has been developed
at CERFACS for the community ocean general circulation model OPA, version 8.2
Used for research and developments in assimilation methods
covariance modeling and estimation
minimization methods
assimilation of different data types
Used for application to ocean reanalysis and initialization for climate forecasting
EU projects ENACT and ENSEMBLES
CLIVAR-GODAE reanalysis inter-comparison pilot project
0 02 2
Variational Data assimilation in OPAVAR: Current research activities
Sophie RICCI – Post-doc CERFACS April 19th E.G.U 2007
See Nicolas Daget's poster
Total temperature standard deviation (Param 100m) Total temperature standard deviation (ENS -100m)
Development of an ensemble variational ocean assimilation/forecast system
for initialization of coupled models for seasonal and decadal climate forecasting
(ENSEMBLES)
for estimating flow-dependent background error statistics
Variational Data assimilation in OPAVAR: Current research activities
Sophie RICCI – Post-doc CERFACS April 19th E.G.U 2007
Assimilation of altimeter SLA data : (Elisabeth Remy – MERCATOR)
Development of methods in OPAVAR to project altimeter data into the sub-surface
(Weaver et al. 2005 QJRMS) using a flow-dependent balance operator within the
control variable.
The impact of altimeter SLA data is very sensitive to the quality of the Mean
Dynamic Topography (CLS Rio-03 product).
Variational Data assimilation in OPAVAR: Current research activities
Sophie RICCI – Post-doc CERFACS April 19th E.G.U 2007
Assimilation of SST data : (Sophie Ricci)
Replace our current “nudging” scheme by Var assimilation
Covariance model development
Account for spatially and temporally correlated observation error
(important for gridded surface products)
Account for state dependent , vertically correlated background error to make
better use of surface data in the mixed layer
Covariance model developments are general and will be useful in the future for
SSS data assimilation (SMOS)
Nudging :
Relaxation to Reynolds SST (daily, interpolated on the model grid)
For seasonal forecasting initialization, a strong relaxation is often used
(e.g., λ = - 200 W / m².K)
Advantages of Var assimilation versus nudging:
Possibility to take proper account of error estimates in the SST data
SST data assimilated simultaneously with other data (via the cost function)
Possibility to make better use of surface data via the background error
covariances
Sophie RICCI – Post-doc CERFACS April 19th E.G.U 2007
Assimilation of SST
3D-Var assimilation experiment
Daily assimilation of Reynolds SST model (ORCA2) gridded products
Weak relaxation coef. λ = - 40 W / m².K at the poles and 0 W / m².K at the
equator
Spatially varying observation error variance estimates from NCEP
Assimilation of SST
Sophie RICCI – Post-doc CERFACS April 19th E.G.U 2007
Validation of the SST assimilation scheme
Sophie RICCI – Post-doc CERFACS April 19th E.G.U 2007
Fit to the assimilated SST Reynolds observations for the background (black)
and analysis (red) for 1993:
Positive skill :
The assimilation brings
the analysis closer to the
observations than the
background, as
expected
Validation of the SST assimilation scheme
Sophie RICCI – Post-doc CERFACS April 19th E.G.U 2007
Mean fit to data over 1993 -1994
AmO
BmO
Validation of the SST assimilation scheme versus in-
situ (independent) T profile data (ENACT)
Sophie RICCI – Post-doc CERFACS April 19th E.G.U 2007
Control Assimilation
Modelling the background and observation error
covariances for SST
Sophie RICCI – Post-doc CERFACS April 19th E.G.U 2007
Background error:
The vertical correlation length scale should be representative of the mixed layer
depth. This could be done using a parametrization such as dTb/dz for the
determination of the vertical diffusion length scale.
Modelling the background and observation error
covariances for SST
Sophie RICCI – Post-doc CERFACS April 19th E.G.U 2007
Observations errors:
Spatial correlations for gridded products can be modelled efficiently using a
diffusion operator (Weaver and Ricci, 2004)
Temporal correlations can modelled efficiently using a recursive filter (Purser et
al. 2003)
In 3D-Var, we need access to the inverse of the observation error covariance
operator. It is straightforward to derive the inverse of the above operators.
These general correlation operators can be applied to other mapped data types
such as SSS and SLA
OPAVAR is a useful research tool but has limitations for future development
and operational applications
OPA8.2 is no longer actively developed
No distributed memory parallelization
NEMO, the new version of OPA, will be used in the next ECMWF
operational seasonal forecasting system
Transfer the variational data assimilation system from OPA to NEMO
Collaborative project lead by CERFACS and ECMWF
(K. Mogensen, M. Balmaseda)
NEMOVAR Project : Background
Sophie RICCI – Post-doc CERFACS April 19th E.G.U 2007
NEMOVAR Project : Implementation plan
Sophie RICCI – Post-doc CERFACS April 19th E.G.U 2007
Short term goal (~ 2 years)
To have a 3D-Var system based on NEMO
Support distributed memory parallelization
Support different global ORCA configurations
Support T and S profiles, multi-satellite altimeter observations, SST and SSS
products and velocity observations (point measurements and maps)
Support multi-incremental configurations where lower resolution can be used in
the inner-loop compared to the outer loop
NEMOVAR Project : Implementation plan
Sophie RICCI – Post-doc CERFACS April 19th E.G.U 2007
Compute the model background trajectory, and initial data-model misfit
Compute an increment to control variables to reduce
misfit (iteratively minimize a quadratic cost function)
Update the model trajectory using the increment and compute the new
data-model misfit
BEGIN OUTER LOOP
BEGIN INNER LOOP
END OUTER LOOP
END INNER LOOP
OPAVAR
NEMO
NEMO
Develop a hybrid system with NEMO in outer loop and OPAVAR in inner loop
NEMOVAR Project : Implementation plan
Sophie RICCI – Post-doc CERFACS April 19th E.G.U 2007
Long term goal
A full 4D-Var system with all of the properties described previously
This is dependent on the existence of a tangent-linear and adjoint of the
NEMO Model (NEMOTAM)
This work is being coordinated by A. Vidard, from INRIA based on the
TAPENADE automatic differentiation tool developed by INRIA (L. Hascoet)
Conclusions and future work
Sophie RICCI – Post-doc CERFACS April 19th E.G.U 2007
Our objective is to develop a flexible and efficient global ocean assimilation platform for
assimilation of multiple data types (T, S profile, SST, SSS, SLA, velocity)
Climate studies/forecasting with low-resolution configurations
Ocean mesoscale studies/forecasting with high-resolution configurations
Comparison between any model run and independent observations for diagnostics
Model validation
Observation monitoring (before assimilation)
All past and current development from OPAVAR will be transfered to NEMOVAR