Variational data assimilation in OOPS¶

The variational application is a generic application for running incremental variational data assimilation.

Supported cost functions and minimizers are described below.

Supported cost functions¶

3D-Var¶

Uses 3D state in the update and in the observer. Does not require model TL/AD.

4D-Ens-Var¶

4DEnVar uses ensembles for estimating ensemble background error covariances. Does not require model TL/AD.

4D-Var¶

The 4D-Var cost function name is reserved for the strong-constraint (perfect model) 4DVar. Requires model TL/AD.

Note

Special case: 3D-FGAT. One could use 4D-Var cost function for running 3DVar-FGAT (first guess at appropriate time) by using an identity tangent-linear model. The resulting analysis increment would be located at the beginning of the assimilation window.

4D-Var-Weak¶

This name is reserved for weak-constraint 4DVar. Two options are available:

using model error forcing control variable
using 4D model state control variable.

Any of the above cost functions could be run with any of the supported minimizers:

Supported minimizers¶

Primal Minimizers¶

PCG : Preconditioned Conjugate Gradients solver
IPCG : Inexact-Preconditioned Conjugate Gradients (G.H. Golub and Q. Ye 1999/00, SIAM J. Sci. Comput. 21(4) 1305-1320)
MINRES : minimal residual method, based on implementation following C. C. Paige and M. A. Saunders, 1975.
GMRESR : generalized minimal residual method (H.A. Van der Vorst and C. Vuik, 1994, Numerical Linear Algebra with Applications, 1(4), 369-386)
PLanczos : standard Preconditioned Lanczos algorithm

Derber-Rosati Minimizers¶

All the minimizers in this section are based on J. Derber and A. Rosati, 1989, J. Phys. Oceanog. 1333-1347

DRPCG : Derber-Rosati Preconditioned Conjugate Gradients. For details see S. Gurol, PhD Manuscript, 2013.
DRIPCG : Derber-Rosati IPCG Minimizer
DRGMRESR : Derber-Rosati GMRESR: “double” version of GMRESR (Van der Vorst & Vuik) following Derber and Rosati.
DRPLanczos : Derber-Rosati Preconditioned Lanczos
DRPFOM : Preconditioned Full Orthogonal Method (FOM): generalization of the Lanczos method to the unsymmetric case.

Left B Preconditioned Minimizer¶

LBGMRESR : Left B Preconditioned GMRESR solver

Dual minimizers¶

RPCG : Augmented Restricted Preconditioned Conjugate Gradients. Based on the algorithm proposed in Gratton and Tshimanga, QJRMS, 135: 1573-1585 (2009).
RPLanczos : Augmented Restricted Lanczos. Lanczos version of RPCG. Based on the algorithm from Gurol, PhD Manuscript, 2013.

SaddlePoint minimizer¶

Variational application yaml structure¶

The following block of code gives the main components of the yaml file needed to run a 3d-var:

---
cost function:
  cost type: #one of the supported cost functions
  time window:
     begin: # beginning of the data assimilation window
     length: # length of the data assimilation window
     bound to include: end # which window bound is inclusive (options: begin, end (default)).
  analysis variables: #variables used for the analysis
  geometry:
    #geometry of the model
  background:
    #background file
  background error:
    #one of the supported background error covariance matrix
  observations:
    obs perturbations: #switch for observation perturbations (default false)
    observers:
      #list of observation files
variational:
  minimizer:
    algorithm: #one of the supported minimizers
  iterations: #each item of this list defines an outer loop
  - diagnostics: #(optional)
      departures: ombg #will save 'observations - H(background)' in the output file
    gradient norm reduction: #target norm for the minimization of the gradient
    ninner: #maximum number of iterations in this outer loop
    geometry:
      #geometry of the model
  - #another outer loop
    [...]
final:
  diagnostics: #(optional)
    departures: oman #will save 'observations - H(analysis)' in the output file
output:
  #path, file name, ... to save the analysis