4DVar Normalization Tutorial: Difference between revisions

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<div class="title">Error Covariance Normalization</div>
<div class="title">Error Covariance Normalization</div>
The various files in the <span class="twilightBlue">Normalization</span> folder are needed to run the 4-Dimensional Variational ('''4D-Var''') data assimilation error covariance model that computes normalization coeffiecients in the California Current System, 1/3&deg; resolution, application ('''WC13''').
The various files in the <span class="twilightBlue">Normalization</span> folder are needed to run the 4-Dimensional Variational ('''4D-Var''') data assimilation error covariance model that computes normalization coefficients in the California Current System, 1/3&deg; resolution, application ('''WC13''').


The computation of 4D-Var error covariance normalization coefficients is very expensive and depends on the grid size. The ones computed here use the expensive "exact" method. For large grids, we need to use the "randomization" approach. The normalization coefficients need to be computed only once for a particular application provided that the grid, land/sea masking (if any), and decorrelation scales remain the same.  
The computation of 4D-Var error covariance normalization coefficients is very expensive and depends on the grid size. The ones computed here use the expensive "exact" method. For large grids, we need to use the "randomization" approach. The normalization coefficients need to be computed only once for a particular application provided that the grid, land/sea masking (if any), and decorrelation scales remain the same.  
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#*MPI flags, USE_MPI and USE_MPIF90
#*MPI flags, USE_MPI and USE_MPIF90
#*Path of MPI, NetCDF, and ARPACK libraries according to the compiler. Notice that you need to provide the correct places of these libraries for your computer. If you want to ignore this section, comment out the assignment for the variable USE_MY_LIBS.
#*Path of MPI, NetCDF, and ARPACK libraries according to the compiler. Notice that you need to provide the correct places of these libraries for your computer. If you want to ignore this section, comment out the assignment for the variable USE_MY_LIBS.
#Notice that the most important CPP options for this application are specified in the [[build_Sript|build script]] instead of <span class="twilightBlue">wc13.h</span>:<div class="box"><span class="twilightBlue">setenv MY_CPP_FLAGS "-DNORMALIZATION"</span></div>This is to allow flexibility with different CPP options.<br /><br />For this to work, however, any '''#undef''' directives MUST be avoided in the header file <span class="twilightBlue">wc13.h</span> since it has precedence during C-preprocessing.
#Notice that the most important CPP option for this application is specified in the [[build_Sript|build script]] instead of <span class="twilightBlue">wc13.h</span>:<div class="box"><span class="twilightBlue">setenv MY_CPP_FLAGS "-DNORMALIZATION"</span></div>This is to allow flexibility with different CPP options.<br /><br />For this to work, however, any '''#undef''' directives MUST be avoided in the header file <span class="twilightBlue">wc13.h</span> since it has precedence during C-preprocessing.
#You MUST use the [[build_Script|build script]] to compile.
#You MUST use the [[build_Script|build script]] to compile.
#Customize the ROMS input script <span class="twilightBlue">ocean_wc13.in</span> and specify the appropriate values for the distributed-memory partition. It is set by default to:<div class="box">[[Variables#NtileI|NtileI]] == 1                              ! I-direction partition<br />[[Variables#NtileJ|NtileJ]] == 8                              ! J-direction partition</div>Notice that the adjoint-based algorithms can only be run in parallel using MPI. This is because of the way that the adjoint model is constructed.
#Customize the ROMS input script <span class="twilightBlue">ocean_wc13.in</span> and specify the appropriate values for the distributed-memory partition. It is set by default to:<div class="box">[[Variables#NtileI|NtileI]] == 1                              ! I-direction partition<br />[[Variables#NtileJ|NtileJ]] == 8                              ! J-direction partition</div>Notice that the adjoint-based algorithms can only be run in parallel using MPI. This is because of the way that the adjoint model is constructed.
#Customize the configuration script [[job_normalization|job_normalization.sh]] and provide the appropriate place for the "substitute" Perl script:<div class="box"><span class="twilightBlue">set SUBSTITUTE=${ROMS_ROOT}/ROMS/Bin/substitute</span></div>This script is distributed with ROMS and it is found in the ROMS/Bin sub-directory. Alternatively, you can define ROMS_ROOT environmental variable in your .cshrc login script. For example, I have:<div class="box"><span class="twilightBlue">setenv ROMS_ROOT /home/arango/ocean/toms/repository/trunk</span></div>
#Customize the configuration script [[job_normalization|job_normalization.sh]] and provide the appropriate place for the "substitute" Perl script:<div class="box"><span class="twilightBlue">set SUBSTITUTE=${ROMS_ROOT}/ROMS/Bin/substitute</span></div>This script is distributed with ROMS and it is found in the ROMS/Bin sub-directory. Alternatively, you can define ROMS_ROOT environmental variable in your .cshrc login script. For example, I have:<div class="box"><span class="twilightBlue">setenv ROMS_ROOT /home/arango/ocean/toms/repository/trunk</span></div>
#Execute the configuration [[job_normalization|job_normalization.sh]] '''BEFORE''' running the model.  It copies the required files and creates <span class="twilightBlue">c4dvar.in</span> input script from template <span class="twilightBlue">s4dvar.in</span>.
#Execute the configuration [[job_normalization|job_normalization.sh]] '''BEFORE''' running the model.  It copies the required files and creates the <span class="twilightBlue">c4dvar.in</span> input script from template <span class="twilightBlue">s4dvar.in</span>.
#Run ROMS with data assimilation:<div class="box"><span class="red">mpirun -np 8 oceanM ocean_wc13.in > & log &</span></div>
#Run ROMS with data assimilation:<div class="box"><span class="red">mpirun -np 8 oceanM ocean_wc13.in > & log &</span></div>


Revision as of 17:14, 29 June 2010

Error Covariance Normalization

The various files in the Normalization folder are needed to run the 4-Dimensional Variational (4D-Var) data assimilation error covariance model that computes normalization coefficients in the California Current System, 1/3° resolution, application (WC13).

The computation of 4D-Var error covariance normalization coefficients is very expensive and depends on the grid size. The ones computed here use the expensive "exact" method. For large grids, we need to use the "randomization" approach. The normalization coefficients need to be computed only once for a particular application provided that the grid, land/sea masking (if any), and decorrelation scales remain the same.


WarningThis page is under constructionWarning


Important CPP Options

NORMALIZATION 4D-Var error covariance normalization factors
WC13 Application CPP option

Input NetCDF Files

Grid File: ../Data/wc13_grd.nc
Nonlinear Initial File: wc13_ini.nc
Forcing File 01: ../Data/coamps_wc13_lwrad_down.nc
Forcing File 02: ../Data/coamps_wc13_Pair.nc
Forcing File 03: ../Data/coamps_wc13_Qair.nc
Forcing File 04: ../Data/coamps_wc13_rain.nc
Forcing File 05: ../Data/coamps_wc13_swrad.nc
Forcing File 06: ../Data/coamps_wc13_Tair.nc
Forcing File 07: ../Data/coamps_wc13_wind.nc
Boundary File: ../Data/wc13_ecco_bry.nc

Initial Conditions STD File: ../Data/wc13_std_i.nc
Model STD File: ../Data/wc13_std_m.nc
Boundary Conditions STD File: ../Data/wc13_std_b.nc
Surface Forcing STD File: ../Data/wc13_std_f.nc

Output NetCDF Files

Initial Conditions Norm File: wc13_nrm_i.nc
Model Norm File: wc13_nrm_m.nc
Boundary Conditions Norm File: wc13_nrm_b.nc
Surface Forcing Norm File: wc13_nrm_f.nc

Various Scripts and Include Files

build.bash bash shell script to compile application
build.sh csh Unix script to compile application
job_normalization.sh job configuration script
ocean_wc13.in ROMS standard input script for WC13
s4dvar.in 4D-Var standard input script template
wc13.h WC13 header with CPP options

Instructions

To run this application you need to take the following steps:

  1. Customize your preferred build script and provide the appropriate values for:
    • Root directory, MY_ROOT_DIR
    • ROMS source code, MY_ROMS_SRC
    • Fortran compiler, FORT
    • MPI flags, USE_MPI and USE_MPIF90
    • Path of MPI, NetCDF, and ARPACK libraries according to the compiler. Notice that you need to provide the correct places of these libraries for your computer. If you want to ignore this section, comment out the assignment for the variable USE_MY_LIBS.
  2. Notice that the most important CPP option for this application is specified in the build script instead of wc13.h:
    setenv MY_CPP_FLAGS "-DNORMALIZATION"
    This is to allow flexibility with different CPP options.

    For this to work, however, any #undef directives MUST be avoided in the header file wc13.h since it has precedence during C-preprocessing.
  3. You MUST use the build script to compile.
  4. Customize the ROMS input script ocean_wc13.in and specify the appropriate values for the distributed-memory partition. It is set by default to:
    NtileI == 1  ! I-direction partition
    NtileJ == 8  ! J-direction partition
    Notice that the adjoint-based algorithms can only be run in parallel using MPI. This is because of the way that the adjoint model is constructed.
  5. Customize the configuration script job_normalization.sh and provide the appropriate place for the "substitute" Perl script:
    set SUBSTITUTE=${ROMS_ROOT}/ROMS/Bin/substitute
    This script is distributed with ROMS and it is found in the ROMS/Bin sub-directory. Alternatively, you can define ROMS_ROOT environmental variable in your .cshrc login script. For example, I have:
    setenv ROMS_ROOT /home/arango/ocean/toms/repository/trunk
  6. Execute the configuration job_normalization.sh BEFORE running the model. It copies the required files and creates the c4dvar.in input script from template s4dvar.in.
  7. Run ROMS with data assimilation:
    mpirun -np 8 oceanM ocean_wc13.in > & log &

References

Moore, A.M., H.G. Arango, G. Broquet, B.S. Powell, J. Zavala-Garay, and A.T. Weaver, 2010: The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems, Part I: System overview, Ocean Modelling, draft.


Moore, A.M., H.G. Arango, and G. Broquet, C. Edwards, M. Veneziani, B.S. Powell, D. Foley, J. Doyle, D. Costa, and P. Robinson, 2010: The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems, Part II: Performance and application to the California Current System, Ocean Modelling, Draft.


Moore, A.M., H.G. Arango, and G. Broquet, C. Edwards, M. Veneziani, B.S. Powell, D. Foley, J. Doyle, D. Costa, and P. Robinson, 2010: The Regional Ocean Modeling System (ROMS) 4-dimensional variational data assimilation systems, Part III: Observation Impact and Observation Sensitivity in the California Current System, Ocean Modelling, Draft.
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