pyPDAF.PDAF.omi_put_state_3dvar

pyPDAF.PDAF.omi_put_state_3dvar()

3DVar DA for a single DA step using diagnoal observation error covariance matrix without post-processing, distributing analysis, and setting next observation step.

See pyPDAF.PDAF.omi_put_state_3dvar_nondiagR() for non-diagonal observation error covariance matrix.

Compared to pyPDAF.PDAF.omi_assimilate_3dvar(), this function has no get_state() call. This means that the analysis is not post-processed, and distributed to the model forecast by user-supplied functions. The next DA step will not be assigned by user-supplied functions as well. The pyPDAF.PDAF.get_state() function follows this function call to ensure the sequential DA.

When 3DVar is used, the background error covariance matrix has to be modelled for cotrol variable transformation. This is a deterministic filtering scheme so no ensemble and parallelisation is needed. This function should be called at each model time step.

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. Iterative optimisation:

    1. py__cvt_pdaf

    2. py__obs_op_lin_pdaf

    3. py__obs_op_adj_pdaf

    4. py__cvt_adj_pdaf

    5. core DA algorithm

  6. py__cvt_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__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__cvt_pdaf (Callable[iter:int, dim_p:int, dim_cvec:int, cv_p : ndarray[tuple[dim_cvec], np.float64], Vv_p : ndarray[tuple[dim_p], np.float64]]) –

    Apply control vector transform matrix to control vector

    Callback Parameters
    • iterint

      • Iteration of optimization

    • dim_pint

      • PE-local observation dimension

    • dim_cvecint

      • Dimension of control vector

    • cv_pndarray[tuple[dim_cvec], np.float64]

      • PE-local control vector

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

      • PE-local result vector (state vector increment)

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

      • PE-local result vector (state vector increment)

  • py__cvt_adj_pdaf (Callable[iter:int, dim_p:int, dim_cvec:int, Vcv_p : ndarray[tuple[dim_p], np.float64], cv_p : ndarray[tuple[dim_cvec], np.float64]]) –

    Apply adjoint control vector transform matrix

    Callback Parameters
    • iterint

      • Iteration of optimization

    • dim_pint

      • PE-local observation dimension

    • dim_cvecint

      • Dimension of control vector

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

      • PE-local result vector (state vector increment)

    • cv_pndarray[tuple[dim_cvec], np.float64]

      • PE-local control vector

    Callback Returns
    • cv_pndarray[tuple[dim_cvec], np.float64]

      • PE-local control vector

  • py__obs_op_lin_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]]) –

    Linearized observation operator

    Callback Parameters
    • stepint

      • Current time step

    • dim_pint

      • PE-local dimension of state

    • dim_obs_pint

      • Dimension of observed state

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

      • PE-local model state

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

      • PE-local observed state

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

      • PE-local observed state

  • py__obs_op_adj_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]]) –

    Adjoint observation operator

    Callback Parameters
    • stepint

      • Current time step

    • dim_pint

      • PE-local dimension of state

    • dim_obs_pint

      • Dimension of observed state

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

      • PE-local model state

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

      • PE-local observed state

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

      • PE-local model state

  • 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

  • outflag (int) – Status flag

Returns:

outflag – Status flag

Return type:

int