Main program for generating an ensemble of random perturbations,
based on the B matrix (can be homogeneous and isotropic, ensemble-based
or a weighted sum of covariance matrices).
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Algorithm
Uses a gaussian pseudorandom generator to produce an ensemble
of control vectors. These control vectors are transformed into
pertubations in physical space, through the action of the square-root
of the background-error covariance matrix. The ensemble of perturbations
can then be debiased and some smoothing can be applied.
The perturbations thus obtained can then be interpolated and added
to a provided ensemble mean and/or be mixed with some previous time
perturbations.
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File I/O
Input and Output Files
Description of file
analysisgrid
In - File defining grid on which perturbations are generated
targetgrid
In - File defining destination ensemble grid
bgcov
In - Static (i.e. NMC) B matrix file for NWP fields
seaIceAnalysis
In - Sea-ice analysis, to set under-ice perturbations to zero
pert_$YYYYMMDDHH_000_$NNNN
In - Previous date perturbations, when ensemble not provided
$YYYYMMDDHH_006_$NNNN
In/Out - Ensemble member files
pert_$YYYYMMDDHH_000_$NNNN
Out - Output perturbations
Synopsis
Below is a summary of the randomPert program calling sequence:
Initial setups:
Initialize the horizontal and vertical grid objects; for the
generation of the perturbations from analysisgrid and
for the ensemble output from targetgrid.
Setup the B matrices: bmat_setup.
Allocate arrays and a stateVector object on the analysisgrid,
to receive the perturbations and first statistical moments.
Initialize the pseudorandom generator : rng_setup.
Generation of the ensemble of perturbations.
Iterate until the number of required perturbations (NENS)
is generated:
Generate a random control vector from a unit variance gaussian
distribution: rng_gaussian.
Transform control vector to physical space perturbation using
bmat_sqrtB.
Unbias the ensemble of perturbations if REMOVE_MEAN is true.
Smooth variances if smoothVariances is true.
Read ensemble mean if readEnsMean is true:
Allocate a stateVector object on the targetgrid, and then
read the ensemble mean using gio_readFromFile.
if setPertZeroUnderIce is true, allocate a stateVector
object and read sea-ice analysis using gio_readFromFile.
Where the ice fraction is higher than iceFractionThreshold,
the perturbations are set to zero.
Read and debias previous date perturbations if
previousDateFraction is higher than zero.
Final steps:
Allocate a statevector object on the targetgrid, copy ocean
mask and interpolate perturbations using int_interp_gsv
If previousDateFraction higher than zero, average previous
date perturbations with the ones generated for the present date.
If readEnsMean is true, add the ensemble mean to the perturbations.
Write the perturbations using gio_writeToFile. If readEnsMean
is true, write the ensemble mean as well.
&NAMENKF define specific parameters to configure the randomPert
program and is read by the program.
The background-error covariance matrix contributions are defined through
the namelists &NAMBHI, for the homogeneous and isotropic part,
and &NAMBEN for the ensemble part. When the matrix is a weighted
sum, it is important to make sure to define the SCALEFACTOR values.
Note that it would be also possible to define contributions from
&NAMBDIFF for diffusion correlations and &NAMBCHM for homogeneous
and isotropic chemical constituents covariances.
Needed modules
version_mod: MODULE version_mod (prefix=’ver’ category=’8. Low-level utilities and constants’)
midasmpi_mod: MODULE midasMpi_mod (prefix=’mmpi’ category=’8. Low-level utilities and constants’)
ramdisk_mod: MODULE ramDisk_mod (prefix=’ram’ category=’8. Low-level utilities and constants’)
controlvector_mod: MODULE controlVector_mod (prefix=’cvm’ category=’6. High-level data objects’)
gridstatevector_mod: MODULE gridStateVector_mod (prefix=’gsv’ category=’6. High-level data objects’)
gridstatevectorfileio_mod: MODULE gridStateVectorFileIO_mod (prefix=’gio’ category=’4. Data Object transformations’)
bmatrix_mod: MODULE bMatrix_mod (prefix=’bmat’ category=’2. B and R matrices’)
interpolation_mod: MODULE interpolation_mod (prefix=’int’ category=’4. Data Object transformations’)
verticalcoord_mod: MODULE verticalCoord_mod (prefix=’vco’ category=’7. Low-level data objects’)
horizontalcoord_mod: MODULE horizontalCoord_mod (prefix=’hco’ category=’7. Low-level data objects’)
timecoord_mod: MODULE timeCoord_mod (prefix=’tim’ category=’7. Low-level data objects’)
randomnumber_mod: MODULE randomNumber_mod (prefix=’rng’ category=’8. Low-level utilities and constants’)
utilities_mod: MODULE utilities_mod (prefix=’utl’ category=’8. Low-level utilities and constants’)
gridvariabletransforms_mod: MODULE gridVariableTransforms_mod (prefix=’gvt’ category=’4. Data Object transformations’)
