pyPDAF.generate_obs

pyPDAF.generate_obs()

Generation of synthetic observations based on given error statistics and observation operator.

The generated synthetic observations are based on each member of model forecast. Therefore, an ensemble of observations can be obtained. In a typical experiment, one may only need one ensemble member. The implementation strategy is similar to an assimilation step. This means that, one can reuse many user-supplied functions for assimilation and observation generation.

User-supplied functions are executed in the following sequence:

  1. py__collect_state_pdaf

  2. py__prepoststep_state_pdaf

  3. py__init_dim_obs_pdaf

  4. py__obs_op_pdaf

  5. py__get_obs_f_pdaf

  6. py__prepoststep_state_pdaf

  7. py__distribute_state_pdaf

  8. py__next_observation_pdaf

Parameters:

  • py__collect_state_pdaf (Callable) – Routine to collect a state vector

  • py__distribute_state_pdaf (Callable) – Routine to distribute a state vector

  • py__init_dim_obs_pdaf (Callable) – Initialize dimension of full observation vector

  • py__obs_op_pdaf (Callable) – Full observation operator

  • py__get_obs_f_pdaf (Callable) – Initialize observation vector

  • py__prepoststep_pdaf (Callable) – User supplied pre/poststep routine

  • py__next_observation_pdaf (Callable) – Provide information on next forecast

  • outflag (int) – Status flag

Returns:

outflag – Status flag

Return type:

int