Ocean Data Variational Assimilation with OPA: Ongoing developments with OPAVAR and implementation plan for NEMOVAR Sophie RICCI, Anthony Weaver, Nicolas

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

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Variational Data assimilation in OPAVAR: Current research activities


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



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).



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


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


Validation of the SST assimilation scheme


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


Mean fit to data over 1993 -1994

AmO

BmO

Validation of the SST assimilation scheme versus in-

situ (independent) T profile data (ENACT)


Control Assimilation

Modelling the background and observation error

covariances for SST


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


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


NEMOVAR Project : Implementation plan


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



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



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


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

Documents

Ocean Data Variational Assimilation with OPA: Ongoing developments with OPAVAR and implementation plan for NEMOVAR Sophie RICCI, Anthony Weaver, Nicolas