It is recommended to use OMI functionalities for fewer user-supplied functions and improved efficiency.I.e., pyPDAF.PDAF.omi_assimilate_3dvar or pyPDAF.PDAF.omi_assimilate_3dvar_nondiagR.
It is recommended to use pyPDAF.PDAF.omi_assimilate_3dvar() or pyPDAF.PDAF.omi_assimilate_3dvar_nondiagR().
It is recommended to use OMI functionalities for fewer user-supplied functions and improved efficiency.
It is recommended to use pyPDAF.PDAF.omi_assimilate_en3dvar_estkf() or pyPDAF.PDAF.omi_assimilate_en3dvar_estkf_nondiagR().
It is recommended to use local module with OMI functionalities for fewer user-supplied functions and improved efficiency.
It is recommended to use pyPDAF.PDAF.localomi_assimilate_en3dvar_lestkf() or pyPDAF.PDAF.localomi_assimilate_en3dvar_lestkf_nondiagR().
It is recommended to use pyPDAF.PDAF.omi_assimilate_global() or pyPDAF.PDAF.omi_assimilate_enkf_nondiagR().
It is recommended to use pyPDAF.PDAF.omi_assimilate_global() or pyPDAF.PDAF.omi_assimilate_enkf_nondiagR().
It is recommended to use pyPDAF.PDAF.omi_assimilate_global() or pyPDAF.PDAF.omi_assimilate_global_nondiagR() instead of this function.
It is recommended to use pyPDAF.PDAF.omi_assimilate_global() or pyPDAF.PDAF.omi_assimilate_global_nondiagR().
It is recommended to use OMI functionalities for fewer user-supplied functions and improved efficiency.
It is recommended to use pyPDAF.PDAF.omi_assimilate_hyb3dvar_estkf() or pyPDAF.PDAF.omi_assimilate_hyb3dvar_estkf_nondiagR().
It is recommended to use local module with OMI functionalities for fewer user-supplied functions and improved efficiency.
It is recommended to use pyPDAF.PDAF.localomi_assimilate_hyb3dvar_lestkf() or pyPDAF.PDAF.localomi_assimilate_hyb3dvar_lestkf_nondiagR().
It is recommended to use pyPDAF.PDAF.omi_assimilate_lenkf() or pyPDAF.PDAF.omi_assimilate_lenkf_nondiagR().
It is recommended to use pyPDAF.PDAF.omi_assimilate_lenkf() or pyPDAF.PDAF.omi_assimilate_lenkf_nondiagR().
It is recommended to use pyPDAF.PDAF.localomi_assimilate() or pyPDAF.PDAF.localomi_assimilate_nondiagR().
It is recommended to use pyPDAF.PDAF.localomi_assimilate() or pyPDAF.PDAF.localomi_assimilate_nondiagR().
It is recommended to use local module with OMI functionalities for fewer user-supplied functions and improved efficiency.
It is recommended to use pyPDAF.PDAF.localomi_assimilate() or pyPDAF.PDAF.localomi_assimilate_lnetf_nondiagR().
It is recommended to use local module with OMI functionalities for fewer user-supplied functions and improved efficiency.
It is recommended to use pyPDAF.PDAF.localomi_assimilate() or pyPDAF.PDAF.localomi_assimilate_lknetf_nondiagR().
It is recommended to use local module with OMI functionalities for fewer user-supplied functions and improved efficiency.
It is recommended to use pyPDAF.PDAF.localomi_assimilate() or pyPDAF.PDAF.localomi_assimilate_nondiagR().
It is recommended to use OMI functionalities for fewer user-supplied functions and improved efficiency.
It is recommended to use pyPDAF.PDAF.omi_assimilate_global() or pyPDAF.PDAF.omi_assimilate_nonlin_nondiagR().
It is recommended to use OMI functionalities for fewer user-supplied functions and improved efficiency.
It is recommended to use pyPDAF.PDAF.omi_assimilate_global() or pyPDAF.PDAF.omi_assimilate_nonlin_nondiagR().
It is recommended to use OMI functionalities for fewer user-supplied functions and improved efficiency.
It is recommended to use pyPDAF.PDAF.omi_assimilate_global() or pyPDAF.PDAF.omi_assimilate_global_nondiagR().
It is recommended to use OMI functionalities for fewer user-supplied functions and improved efficiency.
It is recommended to use pyPDAF.PDAF.omi_assimilate_global() or pyPDAF.PDAF.omi_assimilate_global_nondiagR().
This function does not perform any DA.
It is used to preprocess and postprocess of the ensemble.
This function finalise the PDAF systems including deaclloating all arrays in PDAF.
This function finalise the PDAF systems including freeing all memory used by PDAF.
This function calculates the effective sample size of a particle filter as defined in Doucet et al. 2001 p.
In the local filters (LESKTF, LETKF, LSEIK, LNETF) this function returns the full observation coordinates from process-local observation coordinates.
When diagonal observation error covariance matrix is used, it is recommended to use OMI functionalities for fewer user-supplied functions and improved efficiency.
Generation of synthetic observations based on given error statistics and observation operator.
This function returns the flag that indicates if the DA is performed in the last time step.
The function is called during the analysis step of a global filter.
Using 3DVar for DA with non-diagonal observation error covariance matrix.
Using 3DEnVar for DA with non-diagonal observation error covariance matrix.
Using Hybrid 3DEnVar for DA with non-diagonal observation error covariance matrix.
Using stochastic EnKF (ensemble Kalman filter) with covariance localisation for DA with non-diagonal observation error covariance matrix.
Using stochastic EnKF (ensemble Kalman filter) for DA with non-diagonal observation error covariance matrix.
Global DA filters for DA except for 3DVars with non-diagonal observation error covariance matrix.
Using nonlinear filters (particle filter, NETF) for DA except for 3DVars with non-diagonal observation error covariance matrix.
It is recommended to use local module for fewer user-supplied functions and improved efficiency.
It is recommended to use local module for fewer user-supplied functions and improved efficiency.
It is recommended to use local module for fewer user-supplied functions and improved efficiency.
It is recommended to use local module for fewer user-supplied functions and improved efficiency.
It is recommended to use local module for fewer user-supplied functions and improved efficiency.
It is recommended to use local module for fewer user-supplied functions and improved efficiency.
Set index vector to map local state vector to global state vectors.
It is recommended to use OMI functionalities for fewer user-supplied functions and improved efficiency.I.e., pyPDAF.PDAF.omi_assimilate_3dvar or pyPDAF.PDAF.omi_assimilate_3dvar_nondiagR. -Using 3DVar for DA without OMI. This is a deterministic filtering scheme. This function should be called at each model time step.
-The function is a combination of pyPDAF.PDAF.put_state_3dvar and pyPDAF.PDAF.get_state, and executes the user-supplied function 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__init_obs_pdaf -Starting the iterative optimisation: -6. py__cvt_pdaf -7. py__obs_op_lin_pdaf -8. py__prodRinvA_pdaf -9. py__obs_op_adj_pdaf -10. py__cvt_adj_pdaf -11. core DA algorithm -After the iterations: -12. py__cvt_pdaf -13. py__prepoststep_state_pdaf -14. py__distribute_state_pdaf -15. py__next_observation_pdaf
+It is recommended to use pyPDAF.PDAF.omi_assimilate_3dvar()
+or pyPDAF.PDAF.omi_assimilate_3dvar_nondiagR().
PDAF-OMI modules require fewer user-supplied functions and improved efficiency.
+3DVar DA for a single step without OMI. +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.
+The function is a combination of pyPDAF.PDAF.put_state_3dvar()
+and pyPDAF.PDAF.get_state().
py__collect_state_pdaf
py__prepoststep_state_pdaf
py__init_dim_obs_pdaf
py__obs_op_pdaf
py__init_obs_pdaf
py__cvt_pdaf
py__obs_op_lin_pdaf
py__prodRinvA_pdaf
py__obs_op_adj_pdaf
py__cvt_adj_pdaf
core DA algorithm
py__cvt_pdaf
py__prepoststep_state_pdaf
py__distribute_state_pdaf
py__next_observation_pdaf
Deprecated since version 1.0.0: This function is replaced by pyPDAF.PDAF.omi_assimilate_3dvar()
+and pyPDAF.PDAF.omi_assimilate_3dvar_nondiagR()
It is recommended to use OMI functionalities for fewer user-supplied functions and improved efficiency. I.e., pyPDAF.PDAF.omi_assimilate_en3dvar_estkf or pyPDAF.PDAF.omi_assimilate_en3dvar_estkf_nondiagR. -Using 3DEnVar for DA without OMI. The background error covariance matrix is estimated by ensemble. The 3DEnVar only calculates the analysis of the ensemble mean. An ESTKF is used to generate ensemble perturbations. This function should be called at each model time step.
-The function is a combination of pyPDAF.PDAF.put_state_en3dvar_estkf and pyPDAF.PDAF.get_state, and executes the user-supplied function 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__init_obs_pdaf -Starting the iterative optimisation: -6. py__cvt_ens_pdaf -7. py__obs_op_lin_pdaf -8. py__prodRinvA_pdaf -9. py__obs_op_adj_pdaf -10. py__cvt_adj_ens_pdaf -11. core 3DEnVar algorithm -After the iterations: -12. py__cvt_ens_pdaf -Perform ESTKF: 13. py__init_dim_obs_pdaf -14. py__obs_op_pdaf (for ensemble mean) -15. py__init_obs_pdaf -16. py__obs_op_pdaf (for each ensemble member) -17. py__init_obsvar_pdaf (only relevant for adaptive forgetting factor schemes) -18. py__prodRinvA_pdaf -19. core ESTKF algorithm -20. py__prepoststep_state_pdaf -21. py__distribute_state_pdaf -22. py__next_observation_pdaf
+It is recommended to use pyPDAF.PDAF.omi_assimilate_en3dvar_estkf()
+or pyPDAF.PDAF.omi_assimilate_en3dvar_estkf_nondiagR().
PDAF-OMI modules require fewer user-supplied functions and improved efficiency.
+3DEnVar for a single DA step. +The background error covariance matrix is estimated by an ensemble. +The 3DEnVar only calculates the analysis of the ensemble mean. +An ESTKF is used along with 3DEnVar to generate ensemble perturbations. +This function should be called at each model time step.
+The function is a combination of pyPDAF.PDAF.put_state_en3dvar_estkf()
+and pyPDAF.PDAF.get_state().
py__collect_state_pdaf
py__prepoststep_state_pdaf
py__init_dim_obs_pdaf
py__obs_op_pdaf
py__init_obs_pdaf
py__cvt_ens_pdaf
py__obs_op_lin_pdaf
py__prodRinvA_pdaf
py__obs_op_adj_pdaf
py__cvt_adj_ens_pdaf
core 3DEnVar algorithm
py__cvt_ens_pdaf
py__init_dim_obs_pdaf
py__obs_op_pdaf (for ensemble mean)
py__init_obs_pdaf
py__obs_op_pdaf (for each ensemble member)
py__init_obsvar_pdaf +(only relevant for adaptive forgetting factor schemes)
py__prodRinvA_pdaf
core ESTKF algorithm
py__prepoststep_state_pdaf
py__distribute_state_pdaf
py__next_observation_pdaf
Deprecated since version 1.0.0: This function is replaced by pyPDAF.PDAF.omi_assimilate_en3dvar_estkf()
+and pyPDAF.PDAF.omi_assimilate_en3dvar_estkf_nondiagR()
It is recommended to use local module with OMI functionalities for fewer user-supplied functions and improved efficiency. I.e., pyPDAF.PDAF.localomi_assimilate_en3dvar_lestkf or pyPDAF.PDAF.localomi_assimilate_en3dvar_lestkf_nondiagR. -Using 3DEnVar for DA without OMI. The background error covariance matrix is estimated by ensemble. The 3DEnVar only calculates the analysis of the ensemble mean. An LESTKF is used to generate ensemble perturbations. This function should be called at each model time step.
-The function is a combination of pyPDAF.PDAF.put_state_en3dvar_lestkf and pyPDAF.PDAF.get_state, and executes the user-supplied function 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__init_obs_pdaf -Starting the iterative optimisation: -6. py__cvt_ens_pdaf -7. py__obs_op_lin_pdaf -8. py__prodRinvA_pdaf -9. py__obs_op_adj_pdaf -10. py__cvt_adj_ens_pdaf -11. core DA algorithm -After the iterations: -12. py__cvt_ens_pdaf -Perform LESTKF: -13. py__init_n_domains_p_pdaf -14. py__init_dim_obs_pdaf -15. py__obs_op_pdaf (for each ensemble member -16. py__init_obs_pdaf (if global adaptive forgetting factor is used (type_forget=1 in pyPDAF.PDAF.init))(if global adaptive forgetting factor is used (type_forget=1 in pyPDAF.PDAF.init)(if global adaptive forgetting factor is used (type_forget=1 in pyPDAF.PDAF.init))(if global adaptive forgetting factor is used (type_forget=1 in pyPDAF.PDAF.init))(if global adaptive forgetting factor is used (type_forget=1 in pyPDAF.PDAF.init)(if global adaptive forgetting factor is used (type_forget=1 in pyPDAF.PDAF.init -17. py__init_obsvar_pdaf (if global adaptive forgetting factor is used) -loop over each local domain: -18. py__init_dim_l_pdaf -19. py__init_dim_obs_l_pdaf -20. py__g2l_state_pdaf -21. py__g2l_obs_pdaf (localise mean ensemble in observation space) -22. py__init_obs_l_pdaf -23. py__g2l_obs_pdaf (localise each ensemble member in observation space) -24. py__init_obsvar_l_pdaf (only called if local adaptive forgetting factor (type_forget=2) is used)(only called if local adaptive forgetting factor (type_forget=2 -25. py__prodRinvA_l_pdaf -26. core DA algorithm -27. py__l2g_state_pdaf -28. py__prepoststep_state_pdaf -29. py__distribute_state_pdaf -30. py__next_observation_pdaf
+It is recommended to use pyPDAF.PDAF.localomi_assimilate_en3dvar_lestkf()
+or pyPDAF.PDAF.localomi_assimilate_en3dvar_lestkf_nondiagR().
PDAF-OMI modules require fewer user-supplied functions and improved efficiency.
+3DEnVar for a single DA step where the ensemble anomaly is generated by LESTKF. +The background error covariance matrix is estimated by ensemble. +The 3DEnVar only calculates the analysis of the ensemble mean. +An LESTKF is used to generate ensemble perturbations. +This function should be called at each model time step.
+The function is a combination of pyPDAF.PDAF.put_state_en3dvar_lestkf()
+and pyPDAF.PDAF.get_state().
py__collect_state_pdaf
py__prepoststep_state_pdaf
py__init_dim_obs_pdaf
py__obs_op_pdaf
py__init_obs_pdaf
py__cvt_ens_pdaf
py__obs_op_lin_pdaf
py__prodRinvA_pdaf
py__obs_op_adj_pdaf
py__cvt_adj_ens_pdaf
core DA algorithm
py__cvt_ens_pdaf
py__init_n_domains_p_pdaf
py__init_dim_obs_pdaf
py__obs_op_pdaf +(for each ensemble member)
py__init_obs_pdaf
+(if global adaptive forgetting factor is used
+type_forget=1 in pyPDAF.PDAF.init())
py__init_obsvar_pdaf +(if global adaptive forgetting factor is used)
py__init_dim_l_pdaf
py__init_dim_obs_l_pdaf
py__g2l_state_pdaf
py__g2l_obs_pdaf +(localise mean ensemble in observation space)
py__init_obs_l_pdaf
py__g2l_obs_pdaf +(localise each ensemble member in observation space)
py__init_obsvar_l_pdaf +(only called if local adaptive forgetting factor +type_forget=2 is used)
py__prodRinvA_l_pdaf
core DA algorithm
py__l2g_state_pdaf
py__prepoststep_state_pdaf
py__distribute_state_pdaf
py__next_observation_pdaf
Deprecated since version 1.0.0: This function is replaced by pyPDAF.PDAF.localomi_assimilate_en3dvar_lestkf()
+and pyPDAF.PDAF.localomi_assimilate_en3dvar_lestkf_nondiagR()
It is recommended to use pyPDAF.PDAF.omi_assimilate_global()
-or pyPDAF.PDAF.omi_assimilate_enkf_nondiagR().
pyPDAF.PDAF.omi_assimilate_enkf_nondiagR().
PDAF-OMI modules require fewer user-supplied functions and improved efficiency.
-Using stochastic EnKF (ensemble Kalman filter) [1] for DA without OMI. This function should be called at each model time step.
+Stochastic EnKF (ensemble Kalman filter) [1] for a single DA step without OMI. This function should be called at each model time step.
The function is a combination of pyPDAF.PDAF.put_state_enkf() and pyPDAF.PDAF.get_state().