pyPDAF.PDAF.omi_put_state_hyb3dvar_lestkf_nondiagR

pyPDAF.PDAF.omi_put_state_hyb3dvar_lestkf_nondiagR()

It is recommended to use pyPDAF.PDAF.localomi_put_state_hyb3dvar_lestkf_nondiagR() or pyPDAF.PDAF.localomi_put_state_hyb3dvar_lestkf().

PDAFlocal-OMI modules require fewer user-supplied functions and improved efficiency.

Hybrid 3DEnVar for a single DA step using non-diagnoal observation error covariance matrix without post-processing, distributing analysis, and setting next observation step, where the background error covariance is hybridised by a static background error covariance, and a flow-dependent background error covariance estimated from ensemble.

Compared to pyPDAF.PDAF.omi_assimilate_hyb3dvar_lestkf_nondiagR(), 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. This function is typically used when there are not enough CPUs to run the ensemble in parallel, and some ensemble members have to be run serially. The pyPDAF.PDAF.get_state() function follows this function call to ensure the sequential DA.

The 3DVar generates an ensemble mean and the ensemble perturbation is generated by LESTKF in this implementation. 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. The iterative optimisation:

    1. py__cvt_pdaf

    2. py__cvt_ens_pdaf

    3. py__obs_op_lin_pdaf

    4. py__prodRinvA_pdaf

    5. py__obs_op_adj_pdaf

    6. py__cvt_adj_pdaf

    7. py__cvt_adj_ens_pdaf

    8. core DA algorithm

  6. py__cvt_pdaf

  7. py__cvt_ens_pdaf

  8. Perform LESTKF:

    1. py__init_n_domains_p_pdaf

    2. py__init_dim_obs_pdaf

    3. py__obs_op_pdaf (for each ensemble member)

    4. loop over each local domain:

      1. py__init_dim_l_pdaf

      2. py__init_dim_obs_l_pdaf

      3. py__g2l_state_pdaf

      4. py__prodRinvA_l_pdaf

      5. core DA algorithm

      6. py__l2g_state_pdaf

Deprecated since version 1.0.0: This function is replaced by pyPDAF.PDAF.localomi_put_state_hyb3dvar_lestkf() and pyPDAF.PDAF.localomi_put_state_hyb3dvar_lestkf_nondiagR()

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]]) –

    Full 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__prodRinvA_pdaf (Callable[step:int, dim_obs_p:int, rank:int, obs_p : ndarray[tuple[dim_obs_p], np.float64], A_p : ndarray[tuple[dim_obs_p, rank], np.float64], C_p : ndarray[tuple[dim_obs_p, rank], np.float64]]) –

    Provide product R^-1 A

    Callback Parameters
    • stepint

      • Current time step

    • dim_obs_pint

      • Number of observations at current time step (i.e. the size of the observation vector)

    • rankint

      • Number of the columns in the matrix processes here. This is usually the ensemble size minus one (or the rank of the initial covariance matrix)

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

      • Vector of observations

    • A_pndarray[tuple[dim_obs_p, rank], np.float64]

      • Input matrix provided by PDAF

    • C_pndarray[tuple[dim_obs_p, rank], np.float64]

      • Output matrix

    Callback Returns
    • C_pndarray[tuple[dim_obs_p, rank], np.float64]

      • Output matrix

  • py__cvt_ens_pdaf (Callable[iter:int, dim_p:int, dim_ens:int, dim_cvec_ens:int, ens_p : ndarray[tuple[dim_p, dim_ens], np.float64], v_p : ndarray[tuple[dim_cvec_ens], 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 dimension of state

    • dim_ensint

      • Ensemble size

    • dim_cvec_ensint

      • Dimension of control vector

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

      • PE-local ensemble

    • v_pndarray[tuple[dim_cvec_ens], np.float64]

      • PE-local control vector

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

      • PE-local state increment

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

      • PE-local state increment

  • py__cvt_adj_ens_pdaf (Callable[iter:int, dim_p:int, dim_ens:int, dim_cv_ens_p:int, ens_p : ndarray[tuple[dim_p, dim_ens], np.float64], Vcv_p : ndarray[tuple[dim_p], np.float64], cv_p : ndarray[tuple[dim_cv_ens_p], np.float64]]) –

    Apply adjoint control vector transform matrix

    Callback Parameters
    • iterint

      • Iteration of optimization

    • dim_pint

      • PE-local observation dimension

    • dim_ensint

      • Ensemble size

    • dim_cv_ens_pint

      • PE-local dimension of control vector

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

      • PE-local ensemble

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

      • PE-local input vector

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

      • PE-local result vector

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

      • PE-local result vector

  • 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__prodRinvA_l_pdaf (Callable[domain_p:int, step:int, dim_obs_l:int, rank:int, obs_l : ndarray[tuple[dim_obs_l], np.float64], A_l : ndarray[tuple[dim_obs_l, rank], np.float64], C_l : ndarray[tuple[dim_obs_l, rank], np.float64]]) –

    Provide product R^-1 A for analysis domain

    Callback Parameters
    • domain_pint

      • Index of current local analysis domain

    • stepint

      • Current time step

    • dim_obs_lint

      • Number of local observations at current time step (i.e. the size of the local observation vector)

    • rankint

      • Number of the columns in the matrix processes here. This is usually the ensemble size minus one (or the rank of the initial covariance matrix)

    • obs_lndarray[tuple[dim_obs_l], np.float64]

      • Local vector of observations

    • A_lndarray[tuple[dim_obs_l, rank], np.float64]

      • Input matrix provided by PDAF

    • C_lndarray[tuple[dim_obs_l, rank], np.float64]

      • Output matrix

    Callback Returns
    • C_lndarray[tuple[dim_obs_l, rank], np.float64]

      • Output matrix

  • py__init_n_domains_p_pdaf (Callable[step:int, n_domains_p:int]) –

    Provide number of local analysis domains

    Callback Parameters
    • stepint

      • current time step

    • n_domains_pint

      • pe-local number of analysis domains

    Callback Returns
    • n_domains_pint

      • pe-local number of analysis domains

  • py__init_dim_l_pdaf (Callable[step:int, domain_p:int, dim_l:int]) –

    Init state dimension for local ana. domain

    Callback Parameters
    • stepint

      • current time step

    • domain_pint

      • current local analysis domain

    • dim_lint

      • local state dimension

    Callback Returns
    • dim_lint

      • local state dimension

  • py__init_dim_obs_l_pdaf (Callable[domain_p:int, step:int, dim_obs_f:int, dim_obs_l:int]) –

    Initialize local dimimension of obs. vector

    Callback Parameters
    • domain_pint

      • index of current local analysis domain

    • stepint

      • current time step

    • dim_obs_fint

      • full dimension of observation vector

    • dim_obs_lint

      • local dimension of observation vector

    Callback Returns
    • dim_obs_lint

      • local dimension of observation vector

  • py__g2l_state_pdaf (Callable[step:int, domain_p:int, dim_p:int, state_p : ndarray[tuple[dim_p], np.float64], dim_l:int, state_l : ndarray[tuple[dim_l], np.float64]]) –

    Get state on local ana. domain from full state

    Callback Parameters
    • stepint

      • current time step

    • domain_pint

      • current local analysis domain

    • dim_pint

      • pe-local full state dimension

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

      • pe-local full state vector

    • dim_lint

      • local state dimension

    • state_lndarray[tuple[dim_l], np.float64]

      • state vector on local analysis domain

    Callback Returns
    • state_lndarray[tuple[dim_l], np.float64]

      • state vector on local analysis domain

  • py__l2g_state_pdaf (Callable[step:int, domain_p:int, dim_l:int, state_l : ndarray[tuple[dim_l], np.float64], dim_p:int, state_p : ndarray[tuple[dim_p], np.float64]]) –

    Init full state from local state

    Callback Parameters
    • stepint

      • current time step

    • domain_pint

      • current local analysis domain

    • dim_lint

      • local state dimension

    • state_lndarray[tuple[dim_l], np.float64]

      • state vector on local analysis domain

    • dim_pint

      • pe-local full state dimension

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

      • pe-local full state vector

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

      • pe-local full state vector

  • 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