pyPDAF.PDAF.generate_obs

pyPDAF.PDAF.generate_obs()

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

When diagonal observation error covariance matrix is used, it is recommended to use pyPDAF.PDAF.omi_generate_obs() functionalities for fewer user-supplied functions and improved efficiency.

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.

The function is a combination of pyPDAF.PDAF.put_state_generate_obs() and pyPDAF.PDAF.get_state().

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_pda

  5. py__init_obserr_f_pdaf

  6. py__get_obs_f_pdaf

  7. py__prepoststep_state_pdaf

  8. py__distribute_state_pdaf

  9. py__next_observation_pdaf

Parameters:

  • py__collect_state_pdaf (Callable[dim_p:int, state_p : ndarray[tuple[dim_p], np.float64]]) –

    Collect state vector from model/any arrays to pdaf arrays

    Callback Parameters
    • dim_pint

      • pe-local state dimension

    • state_pndarray[tuple[dim_p], np.float64]

      • local state vector

    Callback Returns
    • state_pndarray[tuple[dim_p], np.float64]

      • local state vector

  • py__distribute_state_pdaf (Callable[dim_p:int, state_p : ndarray[tuple[dim_p], np.float64]]) –

    distribute a state vector from pdaf to the model/any arrays

    Callback Parameters
    • dim_pint

      • PE-local state dimension

    • state_pndarray[tuple[dim_p], np.float64]

      • PE-local state vector

    Callback Returns
    • state_pndarray[tuple[dim_p], np.float64]

      • PE-local state vector

  • py__init_dim_obs_pdaf (Callable[step:int, dim_obs_p:int]) –

    The primary purpose of this function is to obtain the dimension of the observation vector. In OMI, in this function, one also sets the properties of obs_f, read the observation vector from files, setting the observation error variance when diagonal observation error covariance matrix is used. The pyPDAF.PDAF.omi_gather_obs function is also called here.

    Callback Parameters
    • stepint

      • current time step

    • dim_obs_pint

      • dimension of observation vector

    Callback Returns
    • dim_obs_pint

      • dimension of observation vector

  • py__obs_op_pdaf (Callable[step:int, dim_p:int, dim_obs_p:int, state_p : ndarray[tuple[dim_p], np.float64], m_state_p : ndarray[tuple[dim_obs_p], np.float64]]) –

    Observation operator

    Callback Parameters
    • stepint

      • Current time step

    • dim_pint

      • Size of state vector (local part in case of parallel decomposed state)

    • dim_obs_pint

      • Size of PE-local observation vector

    • state_pndarray[tuple[dim_p], np.float64]

      • Model state vector

    • m_state_pndarray[tuple[dim_obs_p], np.float64]

      • Observed state vector (i.e. the result after applying the observation operator to state_p)

    Callback Returns
    • m_state_pndarray[tuple[dim_obs_p], np.float64]

      • Observed state vector (i.e. the result after applying the observation operator to state_p)

  • py__get_obs_f_pdaf (Callable[step:int, dim_obs_f:int, observation_f : ndarray[tuple[dim_obs_f], np.float64]]) –

    Provide observation vector to user

    Callback Parameters
    • stepint

      • Current time step

    • dim_obs_fint

      • Size of the full observation vector

    • observation_fndarray[tuple[dim_obs_f], np.float64]

      • Full vector of synthetic observations (process-local)

    Callback Returns
    • observation_fndarray[tuple[dim_obs_f], np.float64]

      • Full vector of synthetic observations (process-local)

  • py__init_obserr_f_pdaf (Callable[step:int, dim_obs_f:int, obs_f : ndarray[tuple[dim_obs_f], np.float64], obserr_f : ndarray[tuple[dim_obs_f], np.float64]]) –

    Initialize vector of observation error standard deviations

    Callback Parameters
    • stepint

      • Current time step

    • dim_obs_fint

      • Full dimension of observation vector

    • obs_fndarray[tuple[dim_obs_f], np.float64]

      • Full observation vector

    • obserr_fndarray[tuple[dim_obs_f], np.float64]

      • Full observation error stddev

    Callback Returns
    • obserr_fndarray[tuple[dim_obs_f], np.float64]

      • Full observation error stddev

  • py__prepoststep_pdaf (Callable[step:int, dim_p:int, dim_ens:int, dim_ens_l:int, dim_obs_p:int, state_p : ndarray[tuple[dim_p], np.float64], uinv : ndarray[tuple[dim_ens-1, dim_ens-1], np.float64], ens_p : ndarray[tuple[dim_p, dim_ens], np.float64], flag:int]) –

    Preprocesse the ensemble before analysis and postprocess the ensemble before distributing to the model for next forecast

    Callback Parameters
    • stepint

      • current time step (negative for call before analysis/preprocessing)

    • dim_pint

      • PE-local state vector dimension

    • dim_ensint

      • number of ensemble members

    • dim_ens_lint

      • number of ensemble members run serially on each model task

    • dim_obs_pint

      • PE-local dimension of observation vector

    • state_pndarray[tuple[dim_p], np.float64]

      • pe-local forecast/analysis state (the array ‘state_p’ is generally not initialised in the case of ESTKF/ETKF/EnKF/SEIK, so it can be used freely here.)

    • uinvndarray[tuple[dim_ens-1, dim_ens-1], np.float64]

      • Inverse of the transformation matrix in ETKF and ESKTF; inverse of matrix formed by right singular vectors of error covariance matrix of ensemble perturbations in SEIK/SEEK. not used in EnKF.

    • ens_pndarray[tuple[dim_p, dim_ens], np.float64]

      • PE-local ensemble

    • flagint

      • pdaf status flag

    Callback Returns
    • state_pndarray[tuple[dim_p], np.float64]

      • pe-local forecast/analysis state (the array ‘state_p’ is generally not initialised in the case of ESTKF/ETKF/EnKF/SEIK, so it can be used freely here.)

    • uinvndarray[tuple[dim_ens-1, dim_ens-1], np.float64]

      • Inverse of the transformation matrix in ETKF and ESKTF; inverse of matrix formed by right singular vectors of error covariance matrix of ensemble perturbations in SEIK/SEEK. not used in EnKF.

    • ens_pndarray[tuple[dim_p, dim_ens], np.float64]

      • PE-local ensemble

  • py__next_observation_pdaf (Callable[stepnow:int, nsteps:int, doexit:int, time:float]) –

    Routine to provide number of forecast time steps until next assimilations, model physical time and end of assimilation cycles

    Callback Parameters
    • stepnowint

      • the current time step given by PDAF

    • nstepsint

      • number of forecast time steps until next assimilation; this can also be interpreted as number of assimilation function calls to perform a new assimilation

    • doexitint

      • whether to exit forecasting (1 for exit)

    • timefloat

      • current model (physical) time

    Callback Returns
    • nstepsint

      • number of forecast time steps until next assimilation; this can also be interpreted as number of assimilation function calls to perform a new assimilation

    • doexitint

      • whether to exit forecasting (1 for exit)

    • timefloat

      • current model (physical) time

Returns:

flag – Status flag

Return type:

int