Contact: | Jeff Anderson |
Reviewers: | |
Revision: | $Revision: 1.5 $ |
Release Name: | $Name: pre_iceland $ |
Change Date: | $Date: 2005/09/12 22:04:20 $ |
Change history: | see CVS log |
This is the highest level in DART where a module has access to details of both the assim_model structure and the observation structure. Operations like the evaluation of forward operators that require a knowledge of both the observations and the model details are computed here.
types_mod utilities_mod location_mod (depends on model choice) assim_model_mod obs_sequence_mod obs_def_mod time_manager_mod ensemble_manager_mod
use obs_model_mod, only : | get_close_states |
move_ahead |
type(obs_sequence_type), intent(in) :: seq integer, intent(in) :: key real(r8), intent(in) :: radius integer, intent(out) :: numinds integer, dimension(:), intent(out) :: indices real(r8), dimension(:), intent(out) :: dist real(r8), dimension(:), intent(in) :: x
Given an observation from an observation sequence, find the number of state variables that are within distance 'radius' of the observation location. Return the indices of the close states and the distances between each and the observation. The model state vector is, unfortunately, needed to compute locations of model state variables in many complex models.
seq | An observation sequence |
key | Integer key identifying an observation in the sequence |
radius | Find close states within this radius |
numinds | Number of close states found |
indices | Indices of the close state variables |
dist | The distance between each close state and the observation |
x | The model state vector, needed to compute distance in some models |
type(ensemble_type), intent(in) :: ens_handle integer, intent(in) :: ens_size integer, intent(in) :: model_size type(obs_sequence_type), intent(in) :: seq integer, intent(in) :: last_key_used integer, dimension(2), intent(out) :: key_bounds integer, intent(out) :: num_obs_in_set integer, intent(in) :: async character(len=129), intent(in) :: adv_ens_command
Given an observation sequence and an ensemble, determines how to advance the model so that the next set of observations can be assimilated. Also returns the first and last keys and the number of observations to be assimilated at this time. The algorithm implemented here (one might want to have other variants) first finds the time of the next observation that has not been assimilated at a previous time. It also determines the time of the ensemble state vectors. It then uses information about the model's time stepping capabilities to determine the time to which the model can be advanced that is CLOSEST to the time of the next observation. For now, this algorithm assumes that the model's timestep is a constant. A window of width equal to the model timestep is centered around the closest model time to the next observation and all observations in this window are added to the set to be assimilated.
ens_handle | Identifies the model state ensemble |
ens_size | Number of ensemble members |
model_size | Size of model state vector |
seq | An observation sequence |
last_key_used | Identifies the last observation from the sequence that has been used |
key_bounds | Returned lower and upper bound on observations to be used at this time |
num_obs_in_set | Number of observations to be used at this time |
async | Controls how model is advanced (see filter) |
adv_ens_command | Command issued by shell for async option 2 (see filter) |
Routine | Message | Comment |
---|---|---|
move_ahead | next obs time not in model time window | Error in algorithm to compute observation window |