The variational application is a generic application for running incremental variational data assimilation.
Supported cost functions and minimizers are described below.
Uses 3D state in the update and in the observer. Does not require model TL/AD.
4DEnVar uses ensembles for estimating ensemble background error covariances. Does not require model TL/AD.
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.
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:
- 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
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.
- LBGMRESR : Left B Preconditioned GMRESR solver
- 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.
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
window begin: #beginning of the data assimilation window
window length: #length of the data assimilation window
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:
#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