Version 50 (modified by 4 months ago) (diff) | ,
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PDAF-OMI Observation Modules
PDAF-OMI Guide
- Overview
- callback_obs_pdafomi.F90
- Observation Modules
- Observation operators
- Checking error status
- Debugging functionality
- Implementing the analysis step with OMI
- Porting an existing implemention to OMI
- Additional OMI Functionality
Contents of this page
Overview
The implementation of the observations with OMI is done in observation modules (obs-modules). For each observation type a separate module should be created.
Each obs-module contains four routines (where 'TYPE' will be replaced by the name of the observation):
init_dim_obs_OBSTYPE
initializes all variables holding the information about one observation type. The information about the observation type is stored in a data structure (Fortran derived type).obs_op_OBSTYPE
applies the observation operator to a state vector. One can call an observation operator routine provided by PDAF, or one can to implement a new operator.init_dim_obs_l_OBSTYPE
calls a PDAF-OMI routine to initialize the observation information corresponding to a local analysis domain. One can set localization parameters, like the localization radius, for each observation type.localize_covar_OBSTYPE
calls a PDAF-OMI routine to apply covariance localization. One can set localization parameters, like the localization radius, for each observation type.
The template file obs_OBSTYPE_pdafomi_TEMPLATE.F90
shows the different steps needed when implementing these routines. The main work is to implement init_dim_obs
, while the other routines mainly call a subroutine provided by PDAF-OMI.
In the obs-module the subroutines are named according to the observation type. The template file uses generic names which can be replaced by the user. Having distinct names for each observation type is relevant to include the subroutine from the module in the call-back routine with ‘use’. In the header of each obs-module, the user can declare further variables, e.g. assim_OBSTYPE as a flag to control whether the observation type should be assimilated.
Note: In contrast to the 'classical' implementation of observation routines for PDAF, the global and local filters use the same routines init_dim_obs
and obs_op
. PDAF-OMI recognizes whether a global or local filter is used and does the necessary operations by itself.
Data type obs_f
To ensure the functionality within each obs-module, we rely on a derived data type called obs_f
that contains all information about the observation. One instance of this data type is allocated in each obs-module with the generic variable name thisobs
. A few of the elements of obs_f
are initialized by the user when the observation information is initialized on init_dim_obs_f
. Further variables is set in a call to the routine PDAFomi_gather_obs
. This information is then used by all other routines in the obs-module. The template file obs_OBSTYPE_pdafomi_TEMPLATE.F90
shows the different steps needed to initialize thisobs
.
The mandatory variables in obs_f
that need to be set by the user are:
TYPE obs_f ! ---- Mandatory variables to be set in INIT_DIM_OBS ---- INTEGER :: doassim=0 !< Whether to assimilate this observation type INTEGER :: disttype !< Type of distance computation to use for localization ! (0) Cartesian, (1) Cartesian periodic ! (2) simplified geographic, (3) geographic haversine function ! (10) Cartesian 2+1D factorized, (11) Cartesian periodic 2+1D factorized ! (12) simplified geographic 2+1D factorized ! (13) geographic haversine function 2+1D factorized INTEGER :: ncoord !< Number of coordinates use for distnce computation INTEGER, ALLOCATABLE :: id_obs_p(:,:) !< Indices of process-local observed field in state vector ... END TYPE obs_f
In addition there are optional variables that the be used:
TYPE obs_f ... ! ---- Optional variables - they can be set in INIT_DIM_OBS ---- REAL, ALLOCATABLE :: icoeff_p(:,:) !< Interpolation coefficients for obs. operator (optional) REAL, ALLOCATABLE :: domainsize(:) !< Size of domain for periodicity (<=0 for no periodicity) (optional) ! ---- Variables with predefined values - they can be changed in INIT_DIM_OBS ---- INTEGER :: obs_err_type=0 !< Type of observation error: (0) Gauss, (1) Laplace INTEGER :: use_global_obs=1 !< Whether to use (1) global full obs. !< or (0) obs. restricted to those relevant for a process domain ... END TYPE obs_f
Apart from these variables, there is a number of variables that are set internally when the routine PDAFomi_gather_obs
is called. The full data type can be seen on the page on OMI debugging.
Next to the derived data type obs_f
, there is a derived type obs_l
for localization. This is only used internally. It will be filled in the routine init_dim_obs_l
when calling PDAFomi_init_dim_obs_l
.
init_dim_obs_OBSTYPE
This is the main routine to initialize observation information.
Please see the template file templates/omi/obs_OBSTYPE_pdafomi_TEMPLATE.F90 for a step-by-step description of the implementation steps. |
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Each observation module uses the generic name thisobs for the variable with observation type obs_f
. Elements of thisobs
are accessed like
thisobs%doassim
.
The main variables that the filled in this routine are
- thisobs%doassim: Specify whether this observation type is assimilated
- thisobs%disttype: Specify the type of distance computation
- thisobs%ncoord: Specify the number of dimensions used to compute distances
- dim_obs_p: Count the number of available observations
- obs_p: Fill the vector of observations
- ocoord_p: store the coordinates of the observations
- ivar_obs_p: store the inverse error variance of each observation
- thisobs%id_obs_p: store the indices of state vector elements that correspond to an observation (A single value for observation at grid points, or multiple values for derived quantities or interpolation)
When the observation operator performs interpolation, one further needs to initialize an array of interpolation coefficients (thisobs%icoeff_p). For Cartesian distance computation with periodicity one also needs to set thisobs%domainsize.
Here one can also activate the omission of observations that are too different from the ensemble mean. This is activated by setting thisobs%inno_omit>0.0
When parallel model with domain decomposition is used, the variables with suffix _p
need to describe the observation information for a particular process domain. The following routine will perform the necessary operations to ensure that the parallelization is taken into account by PDAF.
After these variables are filled, one calls
CALL PDAFomi_gather_obs(thisobs, dim_obs_p, obs_p, ivar_obs_p, ocoord_p, & thisobs%ncoord, cradius, dim_obs)
This routine will complete all required initializations for OMI. As such it is mandatory to call the routine.
The routine PDAFomi_gather_obs
returns the number of observations dim_obs
which is the return variable for PDAF.
Notes:
- The value is
cradius
is only used if thisobs%use_global_obs=0. - Also in case of non-isotropic localization
cradius
is a single value. It should be set of the largest radius in the horizontal direction used in the parallel process domain.
obs_op_OBSTYPE
This routine applies the observation operator to a state vector. It returns the observed state vector to PDAF. The routine is used by all filters.
PDAF-OMI provides several observation operators. For example the observation operator for observations that are grid point values is called as:
CALL PDAFomi_obs_op_gridpoint(thisobs, state_p, ostate)
Here, state_p
is the state vector and ostate
is the observed state vector.
For more information on the available observation operators and on how to implement your own observation operator see the documentation of observation operators for OMI.
init_dim_obs_l_OBSTYPE
This routine initializes local observation information. The routine is only used by the domain-localized filters (LESTKF, LETKF, LSEIK, LNETF, LKNETF).
For the initialization the following routine is called:
CALL PDAFomi_init_dim_obs_l(thisobs_l, thisobs, coords_l, & locweight, cradius, sradius, dim_obs_l)
Here, thisobs
and thisobs_l
are the data-type variables obs_f
and obs_l
. dim_obs_l
, the local size of the observation vector, is the direct output of the routine.
Implementation steps:
- Ensure that
coords_l
is filled ininit_dim_l_pdaf
and that the unit ofcoords_l
is the same as that used fo rthe observation coordinates. - Specify the localization variables (These variables are usually set in
init_pdaf
and included withuse mod_assimilation
)locweight
: Type of localization (see table below)cradius
: The localization radius or directional radii (cut-off radius for the observations, weight is always =0 for distances > cradius)sradius
: The support radius (or directional redii) of the localization weight function
Note, that starting with PDAF V2.2.1 these three variables can be either scalar values - for isotropic localization-, or arrays - for non-isotropic localization and for additionally choose separate weights functions for the horizontal and vertical directions (see the notes below for more information)
The setting of locweight
influences the weight function for the localization. The choices are standardized as follows
locweight | 0 | 1 | 2 | 3 | 4 | |
---|---|---|---|---|---|---|
function | unit weight | exponential | 5-th order polynomial | 5-th order polynomial | 5-th order polynomial | |
regulation | - | - | - | regulation using mean variance | regulation using variance of single observation point | |
cradius | weight=0 if distance > cradius | |||||
sradius | no impact | weight = exp(-d / sradius) | weight = 0 if d >= sradius else weight = f(sradius, distance) |
Here, 'regulation' refers to the regulated localization introduced in Nerger, L., Janjić, T., Schröter, J., Hiller, W. (2012). A regulated localization scheme for ensemble-based Kalman filters. Quarterly Journal of the Royal Meteorological Society, 138, 802-812. doi:10.1002/qj.945.
Notes:
- isotropic localization: If
cradius
andsradius
are scalar values, the localization is isotropic. Thus, it uses the samecradius
in all directions. If different localization scales should be applied e.g. in the vertical compared to the horizonal one needs to scale the vertical coordinates. - non-isotropic localization: Nonisotropic localization was introduced with PDAF V2.2:
cradius
andsradius
can be declared as vectors of lengththisobs%ncoords
and each element can get a different value. In this case, the values define a non-isotropic localization according to the values specified incradius
andsradius
. PDAF-OMI will use these values to compute a directional localization radius. - 2D+1D factorized non-isotropic localization: With PDAF V2.2.1 a factorized 2D+1D localization can be specified (see explanation of disttype. If the non-isotropic localization is used one can specify different weight functions for the vertical and horizontal directions. This is achieved by declaring
loweight
as a vector of size 2. Now the first element specifies the weight function (according to the table above) for the horizontal direction and the second element specified the wieght function for the vertical direction. When 'locweight' is used as a scalar variable, it specified the weight function in the horizontal direction while the weight function in the vertical dircetion is a constant value of one.
localize_covar_OBSTYPE
This routine initializes local observation information. The routine is only used by the local EnKF (LEnKF).
For the initialization the following routine is called:
CALL PDAFomi_localize_covar(thisobs, dim_p, locweight, cradius, sradius, & coords_p, HP_p, HPH)
Here, thisobs
is the data-type variable obs_f
. HP_p
and HPH
are the covariance matrices projected onto the observations. The localization will be applied to these variables.
Implementation steps:
- Ensure that
coords_p
is filled inlocalize_covar_pdafomi
- Specify the localization variables (These variables are usually set in
init_pdaf
and included withuse mod_assimilation
)locweight
: Type of localization (see table above)cradius
: The localization radius (cut-off radius for the observations, weight is always =0 for distances > cradius)sradius
: The support radius of the localization weight function
Note, that starting with PDAF V2.2.1 these three variables can be either scalar values - for isotropic localization-, or arrays - for non-isotropic localization and for additionally choose separate weights functions for the horizontal and vertical directions (see the notes below for more information)
Notes:
- isotropic localization: If
cradius
andsradius
are scalar values, the localization is isotropic. Thus, it uses the samecradius
in all directions. If different localization scales should be applied e.g. in the vertical compared to the horizonal one needs to scale the vertical coordinates. - non-isotropic localization: Nonisotropic localization was introduced with PDAF V2.2:
cradius
andsradius
can declared as vectors of lengththisobs%ncoords
and each element can get a different value. In this case, the values defined a non-isotropic localization according to the values specified incradius
andsradius
. - non-isotropic localization: Nonisotropic localization was introduced with PDAF V2.2:
cradius
andsradius
can be declared as vectors of lengththisobs%ncoords
and each element can get a different value. In this case, the values define a non-isotropic localization according to the values specified incradius
andsradius
. PDAF-OMI will use these values to compute a directional localization radius. - 2D+1D factorized non-isotropic localization: With PDAF V2.2.1 a factorized 2D+1D localization can be specified (see explanation of disttype. If the non-isotropic localization is used one can specify different weight functions for the vertical and horizontal directions. This is achieved by declaring
loweight
as a vector of size 2. Now the first element specifies the weight function (according to the table above) for the horizontal direction and the second element specified the wieght function for the vertical direction. When 'locweight' is used as a scalar variable, it specified the weight function in the horizontal direction while the weight function in the vertical dircetion is a constant value of one.
Additional routines for 3D-Var
For the 3D-Var methods added with PDAF V2.0 two more routines are required in the observation module.
obs_op_lin_OBSTYPE
This routine applies the linearized observation operator to a state vector. It returns the observed state vector to PDAF. The routine is used only by the 3D-Var methods.
Note: A separate routine for obs_op_lin_OBSTYPE
is only required if the full observation operator in obs_op_OBSTYPE
is nonlinear. If obs_op_OBSTYPE
is linear, one can just insert calls to this operator in the routine obs_op_lin_pdafomi
in callback_obs_pdafomi.F90
.
PDAF-OMI provides several linear observation operators. For example the observation operator for observations that are grid point values is called as:
CALL PDAFomi_obs_op_gridpoint(thisobs, state_p, ostate)
Here, state_p
is the state vector and ostate
is the observed state vector.
For more information on the available observation operators and on how to implement your own observation operator see the documentation of observation operators for OMI.
obs_op_adj_OBSTYPE
This routine applies the adjoint observation operator to an observation vector. It returns the state vector to PDAF. The routine is used only by the 3D-Var methods.
PDAF-OMI provides consistent pairs of linear observation operators. For example the adjoint observation operator for observations that are grid point values is called as:
CALL PDAFomi_obs_op_adj_gridpoint(thisobs, ostate, state_p)
Here, ostate
is the observation vector and state_p
is the state vector.
For more information on the available observation operators and on how to implement your own observation operator see the documentation of observation operators for OMI.
Implementing a new observation type
To implement a new observation type, the approach is generally as follows:
- Create a copy of
obs_OBSTYPE_pdafomi_TEMPLATE.F90
- Rename the module and its subroutines according to the observation (replacing ‘OBSTYPE’ by name of observation).
- Implement
init_dim_obs
for the observation type following the instructions in the template - Adapt
obs_op
for the observation type - Adapt
init_dim_obs_l
for the observation type (if using a domain_localized filter) - Adapt
localize_covar
for the observation type (if using a the local EnKF) - Add subroutine calls for the new observation type into the routines in
callback_obs_pdafomi.F90
Implementation hints for init_dim_obs
thisobs%doassim
Set this variable to 1 to let the filter assimilate this observation. The setting is usually conditional on the value of assim_OBSTYPE
which is set in init_pdaf
:
IF (assim_OBSTYPE) thisobs%doassim = 1
thisobs%ncoord
This variable specifies the dimension of the distance computations. Thus thisobs%ncoord=2 will lead to distance computations in 2 dimensions.
thisobs%disttype
This variable specifies the type of distance computation. Possible choices are
- 0: Cartesian distance in ncoord dimension
- 1: Cartesian distance in ncoord dimensions with periodicity (Needs specification of thisobs%domainsize(ncoord))
- 2: Approximate geographic distance in meters with horizontal coordinates in radians (latitude: -pi/2 to +pi/2; longitude -pi/+pi or 0 to 2pi)
- 3: Geographic distance computation in meters using haversine formula with horizontal coordinates in radians (latitude: -pi/2 to +pi/2; longitude -pi/+pi or 0 to 2pi)
With PDAF V2.2.1, a 2D+1D factorized localization was introduced for 3-dimensional applications. With the factorized localization, the horizontal distance (components 1 and 2) is treated separately from the vertical direction (3rd component). This is available for both isoptropic and non-isotropic localization and activated using the choices
- 10: Cartesian distance 2D+1D factorized in 3 dimensions
- 11: Cartesian distance 2D+1D factorized in 3 dimensions with periodicity (Needs specification of thisobs%domainsize(ncoord))
- 12: Approximate geographic distance 2D+1D factorized in meters with horizontal coordinates in radians (latitude: -pi/2 to +pi/2; longitude -pi/+pi or 0 to 2pi) and vertical in unit chosen by the user.
- 13: Geographic distance computation 2D+1D factorized in meters using haversine formula with horizontal coordinates in radians (latitude: -pi/2 to +pi/2; longitude -pi/+pi or 0 to 2pi) and vertical in unit as chosen by the user.
Notes:
- When disttype>=10 is specified with isotropic localization the weight function for the vertical direction is constant with a valu eof one. For non-isotropic localization, the weight functions can be separately specified for the vertical and horizontal directions. (see the description of init_dim_obs_l_OBSTYPE for information on how to specify the ono-isotropic localization.
- For 0 and 1 (likewise 10, 11) any distance unit can be used. The computed distance will be in the same unit. For 2 and 3 the horizontal input coordinates are in radians and the distance is computed in meters. Essential is that the grid point coordinates and observation coordinates use the same unit.
- For 3-dimensional localization, the unit of the vertical direction can be chosen by the user. However, for geographic ditances, the unit should be chosen to be 'compatible' with the unit in the horizontal (meter). When isotropic localization is used, the unit for the vertical direction can be scaled do that the length scales in the vertical and horizontal directions are the same (this, e.g., allows to use pressure as the distance measure in the vertical in atmospheric models). For non-isotropic localization, the units can differ without scaling. In ccase of the factorized 2D+1D localization (disttype>=10), the units in the horizontal and vertical directions are independent.
See /models/lorenz96/
for an example using case 1 with periodicity in one dimension.
dim_obs_p
This is a single integer value giving the number of observations. With a parallel model using domain-decomposition this will be the number of observations for the process sub-domain. For observation files holding all observations one can read these and then check which observation redice within the process sub-domain. dim_obs_p
will be used to allocate further arrays and as input argument to PDAFomi_gather_obs
.
obs_p
This should be a vector of real values. It will be used as an argument to PDAFomi_gather_obs
. The order of the entries has to be consistent in the arrays thisobs%id_obs_p
, obs_p
, ivar_obs_p
, and ocoord_p
.
ocoord_p
This should be a rank-2 array of real values with size (thisobs%ncoord, dim_obs_p). It will be used as an argument to PDAFomi_gather_obs
. The order of the entries has to be consistent in the arrays thisobs%id_obs_p
, obs_p
, ivar_obs_p
, and ocoord_p
.
The coordinates of the observation with index k
are given by ocoord_p(:,k)
.
Note: The observation coordinate values will only be used in case of the local filters or for computing interpolation coefficients. The array has always to be allocated because it is used in the call to PDAFomi_gather_obs.
Note: The unit of ocoord_p
and coords_l
(in init_dim_obs_l
) has to be the same. For geographic coordinate computations (thisobs%disttype=2 or =3) the unit used by PDAF-OMI is radian.
ivar_obs_p
This should be a vector of real values. It will be used as an argument to PDAFomi_gather_obs
. The order of the entries has to be consistent in the arrays thisobs%id_obs_p
, obs_p
, ivar_obs_p
, and ocoord_p
.
thisobs%id_obs_p
This array is allocated as
ALLOCATE(thisobs%id_obs_p(NROWS, dim_obs_p))
For a fixed value of the second index the NROWS are the indices of the elements of the state vector that are treated in the observation operator. The value of NROWS depends on the observation operator used for an observation. Examples:
- Using observations that are grid points values:
- NROWS=1
- The entry is the index of a single element of the state vector
- Using observations that are determined by bi-linear interpolation of 4 grid points:
- NROWS=4
- The entries are the indices of four elements of the state vector
Note: This array is only used in the observation operators provided by PDAF-OMI. If you don't use these observation operators, you might not need this array.
thisobs%domainsize
This array has to be allocated as
ALLOCATE(thisobs%domainsize(thisobs%ncoord))
Here one has to specify the size of the domain in each of its thisobs%ncoord dimensions. The information is used to compute the Cartesian distance with periodicity.
Setting one dimension to 0 or a negative value indicates that there is no periodicity in this direction.
thisobs%icoeff_p
This array is allocate the in same way as thisobs%id_obs_p
:
ALLOCATE(thisobs%icoeff_p(NROWS, dim_obs_p))
The value of NROWS has to be the same as for thisobs%id_obs_p
. For a fixed value of the second index the NROWS of the array hold the interpolation coefficients corresponding to the indices specified in thisobs%id_obs_p
.
Please see the documentation of OMI observation operators for information on how to initialize the array thisobs%icoeff_p
using functions provided by PDAF-OMI.
thisobs%obs_err_type
The particle filter methods NETF, LNETF and PF can handle observations with non-Gaussian errors. PDAF-OMI supports the following two choices:
- 0: Gaussian errors (default value)
- 1: double-exponential (Laplace) errors
thisobs%use_global_obs
In the domain-localized filters (LESTK, LETKF, LSEIK, LNETF) observations are assimilated that are located within the localization around some grid point. When a model uses parallelization with domain-decomposition some of these observations might belong to a different process-domain. In the default mode (thisobs%use_global_obs
=1) PDAF-OMI gathers all globally available observations so that each process has access to all observations. It can be more efficient to limit the observations on a process-domain to those observations that are located inside the domain or within the localization radius around it. Then, in the local analyses less observations have to be checked for their distance. Setting thisobs%use_global_obs=0
activates this feature. However, it needs additional preparations to make PDAF-OMI aware of the limiting coordinates of a process sub-domain.
The use of this feature is described in the documentation on using domain-limited observations.
thisobs%inno_omit
Setting this variable to a value > 0.0 activates the functionality that observations are omitted (made irrelevant) from the analysis update if the difference of their value and the ensemble mean to too large. For more information see the page on additional OMI functionality.