1. 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 CERFACSApril 19 th E.G.U 2007

2. Outline Sophie RICCI – Post-doct CERFACSApril 19 th E.G.U 2007 Ongoing developments on OPAVAR Background Development of an ensemble Var system Assimilation of SLA Assimilation of SST NEMOVAR Project Background Implementation plan Current status

3. Variational Data assimilation in OPAVAR: Background Sophie RICCI – Post-doc CERFACSApril 19 th 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

4. 0022 Variational Data assimilation in OPAVAR: Current research activities Sophie RICCI – Post-doc CERFACSApril 19 th 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

5. Variational Data assimilation in OPAVAR: Current research activities Sophie RICCI – Post-doc CERFACSApril 19 th 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).

6. Variational Data assimilation in OPAVAR: Current research activities Sophie RICCI – Post-doc CERFACSApril 19 th 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)

7. 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 CERFACSApril 19 th E.G.U 2007 Assimilation of SST

8. 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 CERFACSApril 19 th E.G.U 2007

9. Validation of the SST assimilation scheme Sophie RICCI – Post-doc CERFACSApril 19 th 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

10. Validation of the SST assimilation scheme Sophie RICCI – Post-doc CERFACSApril 19 th E.G.U 2007 Mean fit to data over 1993 -1994 AmO BmO

11. Validation of the SST assimilation scheme versus in- situ (independent) T profile data (ENACT) Sophie RICCI – Post-doc CERFACSApril 19 th E.G.U 2007 ControlAssimilation

12. Modelling the background and observation error covariances for SST Sophie RICCI – Post-doc CERFACSApril 19 th 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 dT b /dz for the determination of the vertical diffusion length scale.

13. Modelling the background and observation error covariances for SST Sophie RICCI – Post-doc CERFACSApril 19 th 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

14. 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 CERFACSApril 19 th E.G.U 2007

15. NEMOVAR Project : Implementation plan Sophie RICCI – Post-doc CERFACSApril 19 th 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

16. NEMOVAR Project : Implementation plan Sophie RICCI – Post-doc CERFACSApril 19 th 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 Develop a hybrid system with NEMO in outer loop and OPAVAR in inner loop

17. NEMOVAR Project : Implementation plan Sophie RICCI – Post-doc CERFACSApril 19 th 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)

18. Conclusions and future work Sophie RICCI – Post-doc CERFACSApril 19 th 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