hresol (mandatory): the horizontal resolution of the control vector.
accepted values:
hpixels: use the native resolution of the corresponding data
regions: aggregate pixels into regions using a mask specified by the user
hbands: aggregate pixels by lon/lat bands
ibands: aggregate pixels by column/row index bands
global: optimize one factor for the whole spatial extent of the data
Warning
This argument determines whether the parameter is included in the control vector.
All other arguments will be ignored if this one is not specified.
vresol (optional): the vertical resolution of the control vector.
accepted values:
vpixels: use the native resolution of the corresponding data
kbands: aggregate pixels into vertical bands by level index
column (default): optimize one factor for the whole vertical
extent of the data
tresol (optional): the main temporal resolution of the control vector. Should be a
pandas syntax string value. If not specified, only one increment for the full
inversion window
tsubresol (optional): secondary resolution for the control vector. If
tsubresol is not a divider of tresol, the final temporal resolution will
keep tresol as anchors and them split them accordingly to tsubresol
and fitting the size of the last sub-period of each period.
For instance if tresol is 1MS and tsubresol is 10D,
the control vector will have a monthly resolution with 3 sub-periods per month:
the two first periods are 10-days long according to tsubresol and the third sub-period
fills the remaining days of the months, hence between 8 days (for February) to 11
days for 31-day-long months
type (optional): type of increments:
accepted values:
scalar (default): multiplicative increments.
The control vector and the uncertainty matrix store unitless scaling factors
physical: additive increments.
The control vector and the uncertainty matrix store the values in the
original prior data set
xb_scale: a scalar to apply to the prior before any computation
xb_value: an offset to apply to the prior before any computation
err: scaling factor to apply to the prior to compute the standard
deviation of prior uncertainties.
err_type (optional): complement to err; approach used to compute
prior uncertainties from prior values; used only when type = physical:
glob_err (optional): used only when type = physical.
Can be used to specify a total error for the spatial extent of the prior. The
standard deviation of each spatial component of the control vector is scale,
so that the total error (accounting for the horizontal correlations if any) matches the one specified
structure:
total (mandatory): the area-weighted sum of all prior values
is scaled according to this value.
unit_scale (optional, default is 1): scaling factor to apply to
the sum of prior values. Use if the value specified in total is not
in the same unit as the one in the prior values
- bash
surface_unit (optional, default is False): set to True if the
total value is given per unit of surface
- bash
frequency_unit (optional, default is False): set to True if the
total value is given per unit of time
- bash
account_correlations (optional, default is True): account or not
for correlations to compute the total errors, i.e. also summing non-diagonal
terms of the covariance matrix
lowlim_error (optional): lower limit for the standard deviation of
prior uncertainties. The threshold is computed using the physical values of the
prior data
structure:
err (mandatory): lower threshold for errors
unit_scale (optional, default is 1): scaling factor to apply to
prior values. Use if the value specified in err is not in the
same unit as the one in the prior values
hcorrelations (optional): horizontal correlations. In most cases,
the matrix B is not explicitly built. Instead, Kronecker products are used for
each temporal slice of the control vector, horizontal correlations are used
structure:
sigma: the horizontal correlation length in kilometers
landsea (optional, default is False): separate land and sea pixels
sigma_land: the horizontal correlation length for land pixels
sigma_sea: the horizontal correlation length for sea pixels
filelsm: the path to the land-sea mask; it is a NetCDF with a
variable lsm; ocean pixels are pixels with lsm < 0.5
dump_hcorr (optional, default is False):
save horizontal correlations (as eigen vectors and values) for later use;
they are saved in the folder $WORKDIR/controlvect/correlations/;
the name of each file is: horcor_{hresol}_{nlon}x{nlat}_cs{
sigma_sea}_cl{sigma_land}.bin; a suffix _lbc is appended if
correlations are computed for a lateral boundary condition component
dircorrel (optional): where to look for pre-computed
correlations; files are looked for in the folder following the same format
as for dump_hcorr
evalmin (optional, default is 0): minimal value for eigen
values to filter out
crop_chi (optional, default is False): if True, the regularized vector
\(\mathbf{\chi}\) has a reduced dimension (consistent with
evalmin) compared to the full control vector
tcorrelations (optional): lower limit for the standard deviation of
prior uncertainties. The threshold is computed using the physical values of the
prior data
structure:
multi_sigmas (default is False): it is possible to convolve multiple
temporal correlation lengths and type (see below).
if multi_sigmas is True, add a sub-paragraph sigmas,
with multiple entries; for each entry (the name has no importance), specify the sigma_t and
type; this read as follows:
tcorrelations:
multi_sigmas: True
sigmas:
sigma1:
type: isotrope
sigma_t: "3D"
sigma2:
type: frequency
freq: "1D"
sigma_t: "10D"
sigma3:
type: category
scale: "hourofday"
sigma_t: "50D"
Note
Please note the if multi_sigmas is True, only the correlation
values below sigmas will be accounted for.
sigma_t (mandatory): temporal correlation length; should be a
pandas frequency string
type (mandatory): type of temporal correlation
accepted values:
isotrope: correlations are simply computed following the
temporal distance:
\(r = \exp((\delta t / \sigma_t) ^ 2)\)
frequency: only control vector components separated by a period of
exactly the given frequency will be correlated,
still following the same formula as for isotrope;
for instance if frequency = 1D, only components at the
same hour of the day will be correlated with each others
category: the temporal distance to apply the correlation
formula is calculated by temporal categories
accepted values: [hourofday, dayofweek,
monthofyear]
for instance, with hourofday, a component at 12:00 on a given day
will be more correlated to a component at 13:00 for another
day, than with a component at 18:00 of the same day
dump_tcorr (optional, default is False):
save horizontal correlations (as eigen vectors and values) for later use;
they are saved in the folder $WORKDIR/controlvect/correlations/;
the name of each file is: tempcor_{datei}_{datef}_per{period}_ct{
sigma_t}_{sigma_type}.bin; a suffix _lbc is appended if
dircorrel (optional): where to look for pre-computed correlations
evalmin (optional, default is 0): minimal value for eigen
values to filter out
crop_chi (optional, default is False): if True, the regularized vector
\(\mathbf{\chi}\) has a reduced dimension (consistent with
evalmin) compared to the full control vector
