Ocean data assimilation at CERFACS: the OPAVAR and NEMOVAR projects

1
Ocean data assimilation at CERFACS the OPAVAR
and NEMOVAR projects

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

2
Outline

• The OPAVAR project
• Background
• Current research activities
• The NEMOVAR project
• Background
• Objectives
• Implementation plan
• Current status

3
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

4
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)

5
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

6
Outline

• The OPAVAR project
• Background
• Current research activities
• The NEMOVAR project
• Background
• Objectives
• Implementation plan
• Current status

7
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

8
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

9
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
10
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 Develop a hybrid system with NEMO in the
  outer loop and OPAVAR in the inner loop
• Phase 4 Develop an MPP implementation of 3D-Var
  with NEMO in the outer loop and NEMOVAR in the
  inner loop
• Phase 5 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

11
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 ECMWFs 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 ECMWFs
  computers

12
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
• ORCA2o versus ORCA1o (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

13
Observation-minus-model diagnostics
1987-2001 regional temperature statistics
NW extra-trop Pacific
NW extra-trop Pacific
Depth (m)
Mean (oC)
Standard deviation (oC)
14
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)

15
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)
• 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
• Direct Initialization
• Correct the now initial conditions directly
• Restart the integration with an Euler forward
  step
• Reinitialize certain diagnostic variables

16
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

17
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