Changes between Version 10 and Version 11 of OMI_observation_modules

Timestamp:

Nov 24, 2020, 5:28:49 PM (5 years ago)

Author:

lnerger

Comment:

--

OMI_observation_modules

@@ -29 +29 @@
 Each obs-module contains four routines:
 
- - init_dim_obs 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 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 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 calls a PDAF-OMI routine to apply covariance localization. One can set localization parameters, like the localization radius, for each observation type. 
+ - `init_dim_obs` 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` 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` 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` 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_TYPE_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_TYPE 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 initializations by itself.
 
 == Data type obs_f == 
@@ -96 +98 @@
  1. `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`).
+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`.
 
 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.
@@ -176 +178 @@
 1.      Adapt localize_covar for the observation type (if using a the local EnKF)
 1.      Add subroutine calls for the new observation type into the routines in callback_obs_pdafomi.F90
+
+
+== Implementation hints for init_dim_obs_f ==
+
+=== `thisobs%doassim` ===
+
+Set this variable to 1 to let the filter assimilate this observation. The setting is usually conditional on the value of `assim_TYPE` which is set in `init_pdaf`: 
+{{{
+   IF (assim_TYPE) thisobs%doassim = 1
+}}}
+
+=== `thisobs%ncoord` ===
+
+This variable specifies the dimension of the distnace 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)
+
+For 0 and 1 any distance unit can be used. The computed distance will be in the same unit. For 2 and 3 the input coordinates are in radians and the distance is computed in meters.
+
+See `/models/lorenz96_omi/` for an example using case 1 with periodicity in one dimension.
+
+=== `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%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
+
+
+=== `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 [wiki:OMI_observation_operators 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` ===
+
+
+
+