Ocean data assimilation at CERFACS: the OPAVAR and NEMOVAR projects

Anthony Weaver, Sophie Ricci (CDD CNES-TOSCA), Nicolas Daget (PhD) CERFACS, Toulouse 1

Outline



The OPAVAR project

– Background – Current research activities 

The NEMOVAR project

– Background – Objectives – Implementation plan – Current status 2

The OPAVAR project: Background

 OPAVAR is a variational data assimilation system which has been developed at CERFACS for the community ocean general circulation model OPA version 8.2

– Incremental approach – Supports both 3D-Var (FGAT) and 4D-Var – Widely used by the community (in France and abroad) for different applications (data assimilation, singular vectors) – Has been used with the global (ORCA2), tropical Pacific (TDH) and North Atlantic (NATL) configurations – The basis of the MERCATOR assimilation system SAM-3 3

The OPAVAR project: Background

 OPAVAR is used at CERFACS: – For research and development in assimilation methods   3D-Var vs. 4D-Var Covariance modelling and estimation   Assimilation of different data types Minimization and preconditioning methods (collab. ALGO) – For application to ocean reanalysis and initialization for climate forecasting   EU projects ENACT and ENSEMBLES CLIVAR-GODAE reanalysis inter-comparison pilot project  This work is/has been supported by both national and European projects – TOSCA, GMMC, PNEDC, LEFE – DEMETER, ENACT (FP5) and ENSEMBLES (FP6) 4

Current research activities with OPAVAR

   Development of an ensemble ocean assimilation / forecast system (Nicolas Daget, PhD) – For initialization of coupled models for seasonal and decadal climate forecasting (ENSEMBLES) – For estimating flow-dependent forecast error statistics for calibrating the background error covariance model Assimilation of SST and SSS (Sophie Ricci, PostDoc) – 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 – Preparations for the arrival of SMOS data Assimilation of altimeter SLA data – Work started by Charles Deltel and Jérôme Vialard at LOCEAN – Work continued by Elisabeth Remy (MERCATOR) in collaboration with GLOBC  Sensitivity experiments to different Mean Dynamic Topography (MDT) products (model- or data-derived) used for referencing the SLA data  Include the MDT as an additional control variable in the assimilation problem 5

Outline



The OPAVAR project

– Background – Current research activities 

The NEMOVAR project

– Background – Objectives – Implementation plan – Current status 6

Background for the NEMOVAR project

  OPAVAR is a useful research tool but has limitations for future development and operational applications – Written mostly in the OPA8.2 coding style (Fortran-77) – No distributed memory (MPI) parallelization – Difficult to adapt to configurations other than ORCA2 – OPA8.2 is not actively developed anymore – All work within the OPA developers team is focussed on the new NEMO version of the OPA model – Not a long term solution to base developments on OPA8.2

The next ECMWF operational seasonal forecasting system (System 4) will employ NEMO and an ocean initialization scheme based on OPAVAR – Late 2005, A. Weaver (CERFACS) and K. Mogensen (ECMWF) discussed on how to transfer the variational data assimilation system from OPA to NEMO – This was the start of the NEMOVAR project 7

Goals for the NEMOVAR project

  Short term (in ~2 years) goal – Develop a 3D-Var system based on NEMO – Support distributed memory parallelization  Possible also support shared memory parallelization – Support different global (ORCA) configurations  Limited area versions of NEMO may be included later – Support T and S profiles, multi-satellite altimeter observations, SST and SSS products, and velocity observations – Support multi-incremental configurations where lower resolution can be used in the inner loop compared to the outer loop – Produce ensembles of 3D-Var analyses for forecast initialization and background-error calibration Long term goal – A full 4D-Var system with all of the above properties – Depends on the availability of the NEMO tangent-linear and adjoint models 8

The basic structure of the NEMOVAR algorithm

(inherited from OPAVAR) Compute the model background trajectory, and the initial data-model misfit BEGIN OUTER LOOP BEGIN INNER LOOP Compute an

increment

to the model control variables to reduce the misfit (iteratively minimize a quadratic cost function) END INNER LOOP Update the model trajectory using the

increment

, and compute the new data-model misfit END OUTER LOOP 9

NEMOVAR implementation plan: overview

    We have defined the following plan:  Phase 1 : Split the existing OPAVAR Fortran code into separate executables for the inner and outer loops  Phase 2  : Develop an MPP implementation of the observation operators in the outer loop using NEMO T, S profiles, SLA + MDT  SST, SSS, velocity – Phase 3 – Phase 4 – Phase 5 : Develop a hybrid system with NEMO in the outer loop and OPAVAR in the inner loop : Develop an MPP implementation of 3D-Var with NEMO in the outer loop and NEMOVAR in the inner loop : Develop an MPP implementation of 4D-Var with NEMO in the outer loop and NEMOVAR in the inner loop The hybrid system (Phase 3) has been developed and is now being tested By Phase 4 we will have achieved our short term goal By Phase 5 we will have achieved our long term goal 10

Development platforms and code maintenance

    The main development platform is the IBM power5+ computers at ECMWF The code development and maintenance is being done using the Perforce versioning control system available on ECMWF’s computers The prepIFS GUI is used to setup and launch experiments The script system is based on the SMS system developed by ECMWF – The scripts are written so a simpler and more portable version of the script system can be made available to people not having access to ECMWF’s computers 11

Outer loop developments for NEMO

 Developments in Phase 2 and part of Phase 3 could be included in the NEMO reference – An opportunity to standardize outer loop operations (observation operators, application of increments, trajectory output) that are common to incremental-based assimilation algorithms (not just variational).

– The comparison of model and data via observation operators provides a valuable stand-alone diagnostic for model validation and observation monitoring in forced or coupled mode.

 Two model-data comparison studies are planned with ECMWF – ORCA2 o versus ORCA1 o (and possibly higher resolution configurations) – ERA-interim versus ERA-40 forced model runs  There was general interest expressed at the NEMO Developers meeting but there are no immediate plans to integrate this into the NEMO reference.

– Individual groups should contact us if interested 12

Observation-minus-model diagnostics

1987-2001 regional temperature statistics

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o



y

o



c

)

b

) Control 3D-Var NW extra-trop Pacific

y

o



a

) 3D-Var

y

o



H

(

x

b

) 

H



x

a

3D-Var NW extra-trop Pacific Mean ( o C) Standard deviation ( o C)

Outer loop developments for NEMO: current status    Observation operators – T and S profiles, sea-level anomalies – 2D interpolation: bilinear remapping, nearest neighbour, polynomial – 1D interpolation: linear, cubic spline – Optimized parallel grid search  Observations distributed according to NEMO domain decomposition – Temporal averaging (e.g., for some buoy data) – Support point measurements and maps – Designed so that it is straightforward to add a new data type (e.g., SSS from SMOS) – Dynamic memory allocation Data-bases currently available – T and S profiles from ENACT/ENSEMBLES historical data-base – T and S profiles from Coriolis real-time data-base – Altimeter data  Along-track anomalies from CLS multi-satellite data-base  Model-gridded MDT (Rio and model-generated products) Data-bases to be included in the near future – Model-gridded SST (from Reynolds OIv2 + HadSST) – SST from OSTIA (a multi-satellite GHRSST product) – TAO currents 14

Outer loop developments for NEMO cont.

   Feedback files of obs-model information for diagnostic studies and/or assimilation (in the inner loop) Model trajectory storage – Output of the background state at selected times using IOM – Full trajectory storage for 4D-Var not yet implemented Applying the assimilation increment in NEMO (merging of OPAVAR and Met Office NEMO developments) 1. Incremental Analysis Updating (IAU)  Include T, S, SSH, u and v increments in extra tendency terms in the model equations  Possibility to use different IAU weights and variable IAU intervals 2. Direct Initialization    Correct the “now” initial conditions directly Restart the integration with an Euler forward step Reinitialize certain diagnostic variables 15

Final remarks

  The NEMOVAR developments are quite general and do not target any model resolution in particular.

Our objective is to develop a flexible and efficient global ocean assimilation platform that can be used with both – low-resolution configurations for climate studies / forecasting – high-resolution configurations for ocean mesoscale studies / forecasting 16

The NEMOVAR core development team

   Anthony Weaver, CERFACS Kristian Mogensen, Magdalena Balmaseda, ECMWF Arthur Vidard, INRIA (Grenoble)

with contributions from

    Sophie Ricci, Nicolas Daget, CERFACS Elisabeth Remy, MERCATOR-OCEAN Matt Martin, Met Office Greg Smith, Reading University 17