| Version 3 (modified by , 8 days ago) ( diff ) |
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Available options for the different DA methods (PDAF3)
Contents of this page
- Overview of options
- Option outputs using subtype=-1 - Local Ensemble Kalman filters
- Option outputs using subtype=-1 - Global Ensemble Kalman filters
- Option outputs using subtype=-1 - Nonlinear DA Methods
- Option outputs using subtype=-1 - Observation Geneeration
- Option outputs using subtype=-1 - 3D-Var Methods
| This page documents the options for PDAF 3. See the page on available options in PDAF 2.31 for the options valid for the ealier releases. |
There are different options for each of the filter algorithms that need to be specified in the call to PDAF_init or that canbe set using PDAF_set_iparam and PDAF_set_rparam. To display the available options in a compiled assimilation program, one can use run with the specification subtype=-1. In this case PDAF_init wil display the available options for the selected filter algorithm and exit with an error status of -1.
The options are mainly unified over the different DA methods. Here, the nonlinear ensemble methods have somewhaat different options from those for the ensemble Kalman filter variants. Also the 3D-Var methods have a different set of options.
Overview of options
Options for ensemble Kalman filters
Overview of integer options for ensemble Kalman filters. The parameters are explained in the list of options as they are displayed using subtype=-1, which are shown further below.
Parameters shown in italic font have changed in PDAF V3 compared to PDAF V2.3.1. The index position in PDAF V2.3.1 is shown in parenthesis.
| iparam | SEIK | LSEIK | EnKF | LEnKF | ETKF | LETKF | ESTKF | LESTKF | ENSRF |
|---|---|---|---|---|---|---|---|---|---|
| 1 | dim_p | dim_p | dim_p | dim_p | dim_p | dim_p | dim_p | dim_p | dim_p |
| 2 | dim_ens | dim_ens | dim_ens | dim_ens | dim_ens | dim_ens | dim_ens | dim_ens | dim_ens |
| 3 | dim_lag(5) | dim_lag | dim_lag | dim_lag | dim_lag | ||||
| 4 | rank_ana(3) | rank_ana(3) | |||||||
| 5 | type_forget | type_forget | type_forget | type_forget | type_forget | type_forget | |||
| 6 | type_trans | type_trans | type_trans | type_trans | type_trans | type_trans | |||
| 7 | type_sqrt | type_sqrt | type_sqrt | type_sqrt | |||||
| 8 | obs_ens | obs_ens | obs_ens | obs_ens | obs_ens | obs_ens | obs_ens | obs_ens | obs_ens |
| 9 | type_obs | type_obs | type_obs | type_obs | type_obs | type_obs | type_obs | type_obs | type_obs |
Overview of real-valued options for ensemble Kalman filters. The parameters are explained in the list of options as they are displayed using subtype=-1, which are shown further below
| rparam | SEIK | LSEIK | EnKF | LEnKF | ETKF | LETKF | ESTKF | LESTKF | ENSRF |
|---|---|---|---|---|---|---|---|---|---|
| 1 | forget | forget | forget | forget | forget | forget | forget | forget | forget |
Options for nonlinear DA methods
Overview of integer options for nonlinear DA methods. The parameters are explained in the list of options as they are displayed using subtype=-1, which are shown further below.
Parameters shown in italic font have changed in PDAF V3 compared to PDAF V2.3.1. The index position in PDAF V2.3.1 is shown in parenthesis.
| iparam | NETF | LNETF | LKNETF | PF |
|---|---|---|---|---|
| 1 | dim_p | dim_p | dim_p | dim_p |
| 2 | dim_ens | dim_ens | dim_ens | dim_ens |
| 3 | dim_lag | dim_lag | ||
| 4 | type_noise | type_noise | type_hyb(7) | type_noise |
| 5 | type_forget | type_forget | type_forget | |
| 6 | type_trans | type_trans | type_trans | type_resample(3) |
| 7 | type_winf | type_winf | type_winf(6) | |
| 8 | obs_ens | obs_ens | obs_ens | obs_ens |
| 9 | type_obs | type_obs | type_obs | type_obs |
Overview of real-valued options:
| rparam | NETF | LNETF | LKNETF | PF |
|---|---|---|---|---|
| 1 | forget | forget | forget | forget(2) |
| 2 | limit_winf | limit_winf | hyb_g | limit_winf(3) |
| 3 | noise_amp | noise_amp | hyb_k | noise_amp(1) |
Options for 3D-Var methods
Overview of integer options for 3D-Var methods. The parameters are explained in the list of options as they are displayed using subtype=-1, which are shown further below.
Parameters shown in italic font have changed in PDAF V3 compared to PDAF V2.3.1. The index position in PDAF V2.3.1 is shown in parenthesis.
| iparam | 3dvar | en3dvar | hyb3dvar |
|---|---|---|---|
| 1 | dim_p | dim_p | dim_p |
| 2 | dim_ens | dim_ens | dim_ens |
| 3 | type_opt | type_opt | type_opt |
| 4 | dim_cvec | dim_cvec | |
| 5 | dim_cvec_ens | dim_cvec_ens | |
| 6 | solver param1 | solver param1 | solver param1 |
| 7 | solver param2 | solver param2 | solver param2 |
| 8 | obs_ens | obs_ens | obs_ens |
| 9 | type_obs | type_obs | type_obs |
| 10 | |||
| 11 | type_forget | type_forget | |
| 12 | type_trans | type_trans | |
| 13 | type_sqrt | type_sqrt |
Overview of real-valued options:
| rparam | 3dvar | en3dvar | hyb3dvar |
|---|---|---|---|
| 1 | forget | forget | forget |
| 2 | beta | ||
| 3 | solver param3 | solver param3 | solver param3 |
| 4 | solver param4 | solver param4 | solver param4 |
In the overview 'solver param1' to 'solver param4' denote different integer and real-valued parameters that control the different solvers used in the 3D-Var methods.
Option outputs using subtype=-1 - Local Ensemble Kalman filters
LESTKF (filtertype=PDAF_DA_LESTKF=7)
PDAF Available options for LESTKF: PDAF --- Sub-types (Parameter subtype) --- PDAF 0: Standard implementation with ensemble integration PDAF 10: Fixed error space basis PDAF 11: Fixed state covariance matrix PDAF --- Integer parameters (Array param_int) --- PDAF param_int(1): dim_p PDAF Dimension of state vector (>0), required PDAF param_int(2): dim_ens PDAF Ensemble size (>0), required PDAF param_int(3): dim_lag PDAF Size of smoothing lag (>=0), optional PDAF 0: no smoothing (default) PDAF >0: apply smoother up to specified lag PDAF param_int(4): not used PDAF param_int(5) type_forget PDAF Type of forgetting factor; optional PDAF 0: fixed forgetting factor (default) PDAF 1: adaptive forgetting factor for full domain PDAF 2: locally adaptive forgetting factor PDAF param_int(6) type_trans PDAF Type of ensemble transformation matrix; optional PDAF 0: deterministic Omega (default) PDAF 1: random orthonormal Omega orthogonal to (1,...,1)^T PDAF 2: use product of 0 with random orthonomal matrix with eigenvector (1,...,1)^T PDAF (experimental; for random transformations, 1 is recommended) PDAF param_int(7) type_sqrt PDAF Type of transformation matrix square root; optional PDAF 0: symmetric square root (default) PDAF 1: Cholesky decomposition PDAF param_int(8): observe_ens PDAF Application of observation operator H, optional PDAF 0: Apply H to ensemble mean to compute innovation PDAF 1: Apply H to ensemble states; then compute innovation from their mean (default) PDAF param_int(8)=1 is the recomended choice for nonlinear H PDAF param_int(9): type_obs_init PDAF Initialize observations before or after call to prepoststep_pdaf PDAF 0: Initialize observations before call to prepoststep_pdaf PDAF 1: Initialize observations after call to prepoststep_pdaf (default) PDAF --- Floating point parameters (Array param_real) --- PDAF param_real(1): forget PDAF Forgetting factor (usually >0 and <=1), required PDAF --- Further parameters --- PDAF n_modeltasks: Number of parallel model integration tasks PDAF >=1 for subtype 0; not larger than total number of processors PDAF =1 required for subtypes 10 and 11 PDAF screen: Control verbosity of PDAF PDAF 0: no outputs PDAF 1: basic output (default) PDAF 2: 1 plus timing output PDAF 3: 2 plus debug output PDAF +++++++++ End of option overview for the LESTKF ++++++++++
LETKF (filtertype=PDAF_DA_LETKF=5)
PDAF Available options for LETKF: PDAF --- Sub-types (Parameter subtype) --- PDAF 0: full ensemble integration; apply T-matrix analogously to SEIK PDAF 1: full ensemble integration; formulation cf. Hunt et al. (2007) without T matrix PDAF 10: Fixed error space basis; analysis with T-matrix PDAF 11: Fixed state covariance matrix; analysis with T-matrix PDAF --- Integer parameters (Array param_int) --- PDAF param_int(1): dim_p PDAF Dimension of state vector (>0), required PDAF param_int(2): dim_ens PDAF Ensemble size (>0), required PDAF param_int(3): dim_lag PDAF Size of smoothing lag (>=0), optional PDAF 0: no smoothing (default) PDAF >0: apply smoother up to specified lag PDAF param_int(4): not used PDAF param_int(5) type_forget PDAF Type of forgetting factor; optional PDAF 0: fixed forgetting factor (default) PDAF 1: adaptive forgetting factor for full domain PDAF 2: locally adaptive forgetting factor PDAF param_int(6) type_trans PDAF Type of ensemble transformation matrix; optional PDAF 0: deterministic transformation (default) PDAF 2: use product of 0 with random orthonomal matrix with eigenvector (1,...,1)^T PDAF param_int(8): observe_ens PDAF Application of observation operator H, optional PDAF 0: Apply H to ensemble mean to compute innovation PDAF 1: Apply H to ensemble states; then compute innovation from their mean (default) PDAF param_int(8)=1 is the recomended choice for nonlinear H PDAF param_int(9): type_obs_init PDAF Initialize observations before or after call to prepoststep_pdaf PDAF 0: Initialize observations before call to prepoststep_pdaf PDAF 1: Initialize observations after call to prepoststep_pdaf (default) PDAF --- Floating point parameters (Array param_real) --- PDAF param_real(1): forget PDAF Forgetting factor (usually >0 and <=1), required PDAF --- Further parameters --- PDAF n_modeltasks: Number of parallel model integration tasks PDAF >=1 for subtypes 0 and 1; not larger than total number of processors PDAF =1 required for subtypes 10 and 11 PDAF screen: Control verbosity of PDAF PDAF 0: no outputs PDAF 1: basic output (default) PDAF 2: 1 plus timing output PDAF 3: 2 plus debug output PDAF +++++++++ End of option overview for the LETKF ++++++++++
LEnKF (filtertype=PDAF_DA_LENKF=8)
PDAF Available options for LEnKF: PDAF --- Sub-types (Parameter subtype) --- PDAF 0: Standard EnKF analysis with covariance localization PDAF --- Integer parameters (Array param_int) --- PDAF param_int(1): dim_p PDAF Dimension of state vector (>0), required PDAF param_int(2): dim_ens PDAF Ensemble size (>0), required PDAF param_int(3): not used PDAF param_int(4): rank_ana_enkf PDAF maximum rank for inversion of HPH^T, optional, default=0 PDAF for =0, HPH is inverted by solving the representer equation PDAF allowed range is 0 to ensemble size - 1 PDAF param_int(5): not used PDAF param_int(6): not used PDAF param_int(7): not used PDAF param_int(8): observe_ens PDAF Application of observation operator H, optional PDAF 0: Apply H to ensemble mean to compute innovation PDAF 1: Apply H to ensemble states; then compute innovation from their mean (default) PDAF param_int(8)=1 is the recomended choice for nonlinear H PDAF param_int(9): type_obs_init PDAF Initialize observations before or after call to prepoststep_pdaf PDAF 0: Initialize observations before call to prepoststep_pdaf PDAF 1: Initialize observations after call to prepoststep_pdaf (default) PDAF --- Floating point parameters (Array param_real) --- PDAF param_real(1): forget PDAF Forgetting factor (usually >0 and <=1), required PDAF --- Further parameters --- PDAF n_modeltasks: Number of parallel model integration tasks PDAF (>=1; not larger than total number of processors) PDAF screen: Control verbosity of PDAF PDAF 0: no outputs PDAF 1: basic output (default) PDAF 2: 1 plus timing output PDAF 3: 2 plus debug output PDAF +++++++++ End of option overview for the LEnKF ++++++++++
ENSRF/EAKF (filtertype=PDAF_DA_ENSRF=13)
PDAF Available options for ENSRF: PDAF --- Sub-types (Parameter subtype) --- PDAF 0: ENSRF with serial observation processing (cf. Houtekamer/Mitchell, 2002) PDAF 1: EAKF/2-step local least squares filter (cf. Anderson, 2003) PDAF --- Integer parameters (Array param_int) --- PDAF param_int(1): dim_p PDAF Dimension of state vector (>0), required PDAF param_int(2): dim_ens PDAF Ensemble size (>0), required PDAF param_int(3): not used PDAF param_int(4): not used PDAF param_int(5): not used PDAF param_int(6): not used PDAF param_int(7): not used PDAF param_int(8): observe_ens PDAF Application of observation operator H, optional PDAF 0: Apply H to ensemble mean to compute innovation PDAF 1: Apply H to ensemble states; then compute innovation from their mean (default) PDAF param_int(8)=1 is the recomended choice for nonlinear H PDAF param_int(9): type_obs_init PDAF Initialize observations before or after call to prepoststep_pdaf PDAF 0: Initialize observations before call to prepoststep_pdaf PDAF 1: Initialize observations after call to prepoststep_pdaf (default) PDAF --- Floating point parameters (Array param_real) --- PDAF param_real(1): forget PDAF Forgetting factor (usually >0 and <=1), required PDAF --- Further parameters --- PDAF n_modeltasks: Number of parallel model integration tasks PDAF (>=1; not larger than total number of processors) PDAF screen: Control verbosity of PDAF PDAF 0: no outputs PDAF 1: basic output (default) PDAF 2: 1 plus timing output PDAF 3: 2 plus debug output PDAF +++++++++ End of option overview for the ENSRF ++++++++++
LSEIK (filtertype=PDAF_DA_LSEIK=3)
PDAF Available options for LSEIK: PDAF --- Sub-types (Parameter subtype) --- PDAF 0: full ensemble integration; left-sided application of T PDAF 1: full ensemble integration; explicit ensemble transformation PDAF 10: Fixed error space basis PDAF 11: Fixed state covariance matrix PDAF --- Integer parameters (Array param_int) --- PDAF param_int(1): dim_p PDAF Dimension of state vector (>0), required PDAF param_int(2): dim_ens PDAF Ensemble size (>0), required PDAF param_int(3): not used PDAF param_int(4): not used PDAF param_int(5) type_forget PDAF Type of forgetting factor; optional PDAF 0: fixed forgetting factor (default) PDAF 1: adaptive forgetting factor PDAF 2: locally adaptive forgetting factor PDAF param_int(6) type_trans PDAF Type of ensemble transformation matrix; optional PDAF 0: deterministic Omega (default) PDAF 1: random orthonormal Omega orthogonal to (1,...,1)^T PDAF 2: use product of 0 with random orthonomal matrix with eigenvector (1,...,1)^T PDAF (experimental; for random transformations, 1 is recommended) PDAF param_int(7) type_sqrt PDAF Type of transformation matrix square root; optional PDAF (Only relevant for subtype/=11) PDAF 0: symmetric square root (default) PDAF 1: Cholesky decomposition PDAF param_int(8): observe_ens PDAF Application of observation operator H, optional PDAF 0: Apply H to ensemble mean to compute innovation PDAF 1: Apply H to ensemble states; then compute innovation from their mean (default) PDAF param_int(8)=1 is the recomended choice for nonlinear H PDAF param_int(9): type_obs_init PDAF Initialize observations before or after call to prepoststep_pdaf PDAF 0: Initialize observations before call to prepoststep_pdaf PDAF 1: Initialize observations after call to prepoststep_pdaf (default) PDAF --- Floating point parameters (Array param_real) --- PDAF param_real(1): forget PDAF Forgetting factor (usually >0 and <=1), required PDAF --- Further parameters --- PDAF n_modeltasks: Number of parallel model integration tasks PDAF >=1 for subtypes 0 and 1; not larger than total number of processors PDAF =1 required for subtypes 10 and 11 PDAF screen: Control verbosity of PDAF PDAF 0: no outputs PDAF 1: basic output (default) PDAF 2: 1 plus timing output PDAF 3: 2 plus debug output PDAF --- Internal parameter (defined inside PDAF) --- PDAF Nm1vsN: Normalization of covariance matrix; default: 1 PDAF 0: normalization with 1/(Ensemble size) PDAF (original SEIK, mainly for compatibility with older studies) PDAF 1: normalization with 1/(Ensemble size - 1) PDAF (sample covariance matrix consistent with other EnKFs) PDAF +++++++++ End of option overview for the LSEIK filter ++++++++++
Option outputs using subtype=-1 - Global Ensemble Kalman filters
ESTKF (filtertype=PDAF_DA_ESTKF=6)
PDAF Available options for ESTKF: PDAF --- Sub-types (Parameter subtype) --- PDAF 0: Standard implementation with ensemble integration PDAF 10: Fixed error space basis PDAF 11: Fixed state covariance matrix PDAF --- Integer parameters (Array param_int) --- PDAF param_int(1): dim_p PDAF Dimension of state vector (>0), required PDAF param_int(2): dim_ens PDAF Ensemble size (>0), required PDAF param_int(3): dim_lag PDAF Size of smoothing lag (>=0), optional PDAF 0: no smoothing (default) PDAF >0: apply smoother up to specified lag PDAF param_int(4): not used PDAF param_int(5) type_forget PDAF Type of forgetting factor; optional PDAF 0: fixed forgetting factor (default) PDAF 1: adaptive forgetting factor (experimental) PDAF param_int(6) type_trans PDAF Type of ensemble transformation matrix; optional PDAF 0: deterministic Omega (default) PDAF 1: random orthonormal Omega orthogonal to (1,...,1)^T PDAF 2: use product of 0 with random orthonomal matrix with eigenvector (1,...,1)^T PDAF (experimental; for random transformations, 0 or 1 are recommended) PDAF param_int(7) type_sqrt PDAF Type of transformation matrix square root; optional PDAF 0: symmetric square root (default) PDAF 1: Cholesky decomposition PDAF param_int(8): observe_ens PDAF Application of observation operator H, optional PDAF 0: Apply H to ensemble mean to compute innovation PDAF 1: Apply H to ensemble states; then compute innovation from their mean (default) PDAF param_int(8)=1 is the recomended choice for nonlinear H PDAF param_int(9): type_obs_init PDAF Initialize observations before or after call to prepoststep_pdaf PDAF 0: Initialize observations before call to prepoststep_pdaf PDAF 1: Initialize observations after call to prepoststep_pdaf (default) PDAF --- Floating point parameters (Array param_real) --- PDAF param_real(1): forget PDAF Forgetting factor (usually >0 and <=1), required PDAF --- Further parameters --- PDAF n_modeltasks: Number of parallel model integration tasks PDAF >=1 for subtype 0; not larger than total number of processors PDAF =1 required for subtypes 10 and 11 PDAF screen: Control verbosity of PDAF PDAF 0: no outputs PDAF 1: basic output (default) PDAF 2: 1 plus timing output PDAF 3: 2 plus debug output PDAF +++++++++ End of option overview for the ESTKF ++++++++++
ETKF (filtertype=PDAF_DA_ETKF=4)
PDAF Available options for ETKF: PDAF --- Sub-types (Parameter subtype) --- PDAF 0: full ensemble integration; apply T-matrix analogously to SEIK PDAF 1: full ensemble integration; formulation cf. Hunt et al. (2007) without T matrix PDAF 10: Fixed error space basis; analysis with T-matrix PDAF 11: Fixed state covariance matrix; analysis with T-matrix PDAF --- Integer parameters (Array param_int) --- PDAF param_int(1): dim_p PDAF Dimension of state vector (>0), required PDAF param_int(2): dim_ens PDAF Ensemble size (>0), required PDAF param_int(3): dim_lag PDAF Size of smoothing lag (>=0), optional PDAF 0: no smoothing (default) PDAF >0: apply smoother up to specified lag PDAF param_int(4): not used PDAF param_int(5) type_forget PDAF Type of forgetting factor; optional PDAF 0: fixed forgetting factor (default) PDAF 1: adaptive forgetting factor (experimental) PDAF param_int(6) type_trans PDAF Type of ensemble transformation matrix; optional PDAF 0: deterministic transformation (default) PDAF 2: use product of 0 with random orthonomal matrix with eigenvector (1,...,1)^T PDAF param_int(7): not used PDAF param_int(8): observe_ens PDAF Application of observation operator H, optional PDAF 0: Apply H to ensemble mean to compute innovation PDAF 1: Apply H to ensemble states; then compute innovation from their mean (default) PDAF param_int(8)=1 is the recomended choice for nonlinear H PDAF param_int(9): type_obs_init PDAF Initialize observations before or after call to prepoststep_pdaf PDAF 0: Initialize observations before call to prepoststep_pdaf PDAF 1: Initialize observations after call to prepoststep_pdaf (default) PDAF --- Floating point parameters (Array param_real) --- PDAF param_real(1): forget PDAF Forgetting factor (usually >0 and <=1), required PDAF --- Further parameters --- PDAF n_modeltasks: Number of parallel model integration tasks PDAF >=1 for subtypes 0 and 1; not larger than total number of processors PDAF =1 required for subtypes 10 and 11 PDAF screen: Control verbosity of PDAF PDAF 0: no outputs PDAF 1: basic output (default) PDAF 2: 1 plus timing output PDAF 3: 2 plus debug output PDAF +++++++++ End of option overview for the ETKF ++++++++++
EnKF (filtertype=PDAF_DA_ENKF=2)
PDAF Available options for EnKF: PDAF --- Sub-types (Parameter subtype) --- PDAF 0: Full ensemble integration; analysis for 2*dim_obs>dim_ens PDAF 1: Full ensemble integration; analysis for 2*dim_obs<=dim_ens PDAF --- Integer parameters (Array param_int) --- PDAF param_int(1): dim_p PDAF Dimension of state vector (>0), required PDAF param_int(2): dim_ens PDAF Ensemble size (>0), required PDAF param_int(3): dim_lag PDAF Size of smoothing lag (>=0), optional PDAF 0: no smoothing (default) PDAF >0: apply smoother up to specified lag PDAF param_int(4): rank_ana_enkf PDAF maximum rank for inversion of HPH^T, optional, default=0 PDAF for =0, HPH is inverted by solving the representer equation PDAF allowed range is 0 to ensemble size - 1 PDAF param_int(5): not used PDAF param_int(6): not used PDAF param_int(7): not used PDAF param_int(8): observe_ens PDAF Application of observation operator H, optional PDAF 0: Apply H to ensemble mean to compute innovation PDAF 1: Apply H to ensemble states; then compute innovation from their mean (default) PDAF param_int(8)=1 is the recomended choice for nonlinear H PDAF param_int(9): type_obs_init PDAF Initialize observations before or after call to prepoststep_pdaf PDAF 0: Initialize observations before call to prepoststep_pdaf PDAF 1: Initialize observations after call to prepoststep_pdaf (default) PDAF --- Floating point parameters (Array param_real) --- PDAF param_real(1): forget PDAF Forgetting factor (usually >0 and <=1), required PDAF --- Further parameters --- PDAF n_modeltasks: Number of parallel model integration tasks PDAF (>=1; not larger than total number of processors) PDAF screen: Control verbosity of PDAF PDAF 0: no outputs PDAF 1: basic output (default) PDAF 2: 1 plus timing output PDAF 3: 2 plus debug output PDAF +++++++++ End of option overview for the EnKF ++++++++++
SEIK (filtertype=PDAF_DA_SEIK=1)
PDAF Available options for SEIK: PDAF --- Sub-types (Parameter subtype) --- PDAF 0: full ensemble integration; left-sided application of T PDAF 1: full ensemble integration; right-sided application of T PDAF 2: full ensemble integration; explicit ensemble transformation PDAF 10: Fixed error space basis PDAF 11: Fixed state covariance matrix PDAF --- Integer parameters (Array param_int) --- PDAF param_int(1): dim_p PDAF Dimension of state vector (>0), required PDAF param_int(2): dim_ens PDAF Ensemble size (>0), required PDAF param_int(3): not used PDAF param_int(4): not used PDAF param_int(5) type_forget PDAF Type of forgetting factor; optional PDAF 0: fixed forgetting factor (default) PDAF 1: adaptive forgetting factor (experimental) PDAF param_int(6) type_trans PDAF Type of ensemble transformation matrix; optional PDAF 0: deterministic Omega (default) PDAF 1: random orthonormal Omega orthogonal to (1,...,1)^T PDAF 2: use product of 0 with random orthonomal matrix with eigenvector (1,...,1)^T PDAF (experimental; for random transformations, 1 is recommended) PDAF param_int(7) type_sqrt PDAF Type of transformation matrix square root; optional PDAF (Only relevant for subtype/=11) PDAF 0: symmetric square root (default) PDAF 1: Cholesky decomposition PDAF param_int(8): observe_ens PDAF Application of observation operator H, optional PDAF 0: Apply H to ensemble mean to compute innovation PDAF 1: Apply H to ensemble states; then compute innovation from their mean (default) PDAF param_int(8)=1 is the recomended choice for nonlinear H PDAF param_int(9): type_obs_init PDAF Initialize observations before or after call to prepoststep_pdaf PDAF 0: Initialize observations before call to prepoststep_pdaf PDAF 1: Initialize observations after call to prepoststep_pdaf (default) PDAF --- Floating point parameters (Array param_real) --- PDAF param_real(1): forget PDAF Forgetting factor (usually >0 and <=1), required PDAF --- Further parameters --- PDAF n_modeltasks: Number of parallel model integration tasks PDAF >=1 for subtypes 0, 1 and 2; not larger than total number of processors PDAF =1 required for subtypes 10 and 11 PDAF screen: Control verbosity of PDAF PDAF 0: no outputs PDAF 1: basic output (default) PDAF 2: 1 plus timing output PDAF 3: 2 plus debug output PDAF --- Internal parameter (defined inside PDAF) --- PDAF Nm1vsN: Normalization of covariance matrix; default: 1 PDAF 0: normalization with 1/(Ensemble size) PDAF (original SEIK, mainly for compatibility with older studies) PDAF 1: normalization with 1/(Ensemble size - 1) PDAF (sample covariance matrix consistent with other EnKFs) PDAF +++++++++ End of option overview for the SEIK filter ++++++++++
Option outputs using subtype=-1 - Nonlinear DA Methods
NETF (filtertype=PDAF_DA_NETF=9)
PDAF Available options for NETF: PDAF --- Sub-types (Parameter subtype) --- PDAF 0: Standard implementation with ensemble integration PDAF --- Integer parameters (Array param_int) --- PDAF param_int(1): dim_p PDAF Dimension of state vector (>0), required PDAF param_int(2): dim_ens PDAF Ensemble size (>0), required PDAF param_int(3): dim_lag PDAF Size of smoothing lag (>=0), optional PDAF 0: no smoothing (default) PDAF >0: apply smoother up to specified lag PDAF param_int(4): type_noise PDAF Type of ensemble perturbations, optional PDAF 0: no perturbations (default) PDAF 1: constant standard deviation PDAF 2: relative to ensemble standard deviation PDAF param_int(5) type_forget PDAF Type of forgetting factor; optional PDAF 0: forgetting factor on forecast ensemble (default) PDAF 2: forgetting factor on analysis ensemble PDAF param_int(6) type_trans PDAF Type of ensemble transformation matrix; optional PDAF 0: random orthonormal matrix orthogonal to (1,...,1)^T (default) PDAF 1: deterministic transformation PDAF param_int(7): type_winf PDAF Type of weights inflation; optional PDAF 0: no weights inflation (default) PDAF 1: inflate so that N_eff/N > param_real(2) PDAF param_int(8): observe_ens PDAF Application of observation operator H, optional PDAF Note: This parameter has no influence on the NETF assimilation result PDAF 0: Apply H to ensemble mean to compute innovation PDAF 1: Apply H to ensemble states; then compute innovation from their mean (default) PDAF param_int(8)=1 is the recomended choice for nonlinear H PDAF param_int(9): type_obs_init PDAF Initialize observations before or after call to prepoststep_pdaf PDAF 0: Initialize observations before call to prepoststep_pdaf PDAF 1: Initialize observations after call to prepoststep_pdaf (default) PDAF --- Floating point parameters (Array param_real) --- PDAF param_real(1): forget PDAF Forgetting factor (usually >0 and <=1), required PDAF param_real(2): limit_winf PDAF Limit for weigts inflation N_eff/N > param_real(2), optional, default=0.0 PDAF param_real(3): noise_amp PDAF Ensemble perturbation level (>0), required, only used if param_int(4)>0 PDAF --- Further parameters --- PDAF n_modeltasks: Number of parallel model integration tasks PDAF >=1; not larger than total number of processors PDAF screen: Control verbosity of PDAF PDAF 0: no outputs PDAF 1: basic output (default) PDAF 2: 1 plus timing output PDAF 3: 2 plus debug output PDAF +++++++++ End of option overview for the NETF ++++++++++
LNETF (filtertype=PDAF_DA_LNETF=10)
PDAF Available options for LNETF: PDAF --- Sub-types (Parameter subtype) --- PDAF 0: Standard implementation with ensemble integration PDAF --- Integer parameters (Array param_int) --- PDAF param_int(1): dim_p PDAF Dimension of state vector (>0), required PDAF param_int(2): dim_ens PDAF Ensemble size (>0), required PDAF param_int(3): dim_lag PDAF Size of smoothing lag (>=0), optional PDAF 0: no smoothing (default) PDAF >0: apply smoother up to specified lag PDAF param_int(4): type_noise PDAF Type of ensemble perturbations, optional PDAF 0: no perturbations (default) PDAF 1: constant standard deviation PDAF 2: relative to ensemble standard deviation PDAF param_int(5) type_forget PDAF Type of forgetting factor; optional PDAF 0: forgetting factor on forecast ensemble (default) PDAF 1: forgetting factor on forecast ensemble only observed domains PDAF 2: forgetting factor on analysis ensemble PDAF 3: forgetting factor on analysis ensemble only observed domains PDAF param_int(6) type_trans PDAF Type of ensemble transformation matrix; optional PDAF 0: random orthonormal matrix orthogonal to (1,...,1)^T (default) PDAF 1: deterministic transformation PDAF param_int(7): type_winf PDAF Type of weights inflation; optional PDAF 0: no weights inflation (default) PDAF 1: inflate so that N_eff/N > param_real(2) PDAF param_int(8): observe_ens PDAF Application of observation operator H, optional PDAF Note: This parameter has no influence on the LNETF assimilation result PDAF 0: Apply H to ensemble mean to compute innovation PDAF 1: Apply H to ensemble states; then compute innovation from their mean (default) PDAF param_int(8)=1 is the recomended choice for nonlinear H PDAF param_int(9): type_obs_init PDAF Initialize observations before or after call to prepoststep_pdaf PDAF 0: Initialize observations before call to prepoststep_pdaf PDAF 1: Initialize observations after call to prepoststep_pdaf (default) PDAF --- Floating point parameters (Array param_real) --- PDAF param_real(1): forget PDAF Forgetting factor (usually >0 and <=1), required PDAF param_real(2): limit_winf PDAF Limit for weigts inflation N_eff/N > param_real(2), optional, default=0.0 PDAF param_real(3): noise_amp PDAF Ensemble perturbation level (>0), required, only used if param_int(4)>0 PDAF --- Further parameters --- PDAF n_modeltasks: Number of parallel model integration tasks PDAF >=1; not larger than total number of processors PDAF screen: Control verbosity of PDAF PDAF 0: no outputs PDAF 1: basic output (default) PDAF 2: 1 plus timing output PDAF 3: 2 plus debug output PDAF +++++++++ End of option overview for the LNETF ++++++++++
LKNETF (filtertype=PDAF_DA_LKNETF=11)
PDAF Available options for LKNETF: PDAF --- Sub-types (Parameter subtype) --- PDAF 0: HNK: 2-step LKNETF with NETF before LETKF PDAF 1: HKN: 2-step LKNETF with LETKF before NETF PDAF 2: HSync: LKNETF synchronous PDAF --- Integer parameters (Array param_int) --- PDAF param_int(1): dim_p PDAF Dimension of state vector (>0), required PDAF param_int(2): dim_ens PDAF Ensemble size (>0), required PDAF param_int(3): not used PDAF param_int(4): type_hyb PDAF Type of hybrid weight; optional PDAF 0: fixed value PDAF 1: gamma_lin: (1 - N_eff/N_e)*param_real(2) (default) PDAF 2: gamma_alpha: hybrid weight from N_eff/N>=param_real(2) PDAF 3: gamma_ska: 1 - min(s,k)/sqrt(param_real(3)) with N_eff/N>=param_real(2) PDAF 4: gamma_sklin: 1 - min(s,k)/sqrt(param_real(3)) >= 1-N_eff/N>=param_real(2) PDAF param_int(5): type_forget PDAF Type of forgetting factor; optional PDAF 0: inflate forecast ensemble by 1/forget (default) PDAF 1: inflate forecast ensemble by 1/forget only observed domains PDAF 2: inflate analysis ensemble by 1/forget PDAF 3: inflate analysis ensemble by 1/forget only observed domains PDAF 5: adaptive forgetting factor for full domain in LETKF part PDAF 6: locally adaptive forgetting factor in LETKF part PDAF param_int(6): type_trans PDAF Type of ensemble transformation matrix; optional PDAF 0: random orthonormal matrix orthogonal to (1,...,1)^T (default) PDAF 1: deterministic transformation PDAF param_int(7): not used PDAF --- Floating point parameters (Array param_real) --- PDAF param_real(1): forget PDAF Forgetting factor (usually >0 and <=1), required PDAF param_real(2): hyb_g PDAF prescribed hybrid weight gamma (usually >0 and <=1), optional, default=0.95 PDAF param_real(3): hyb_k PDAF hybrid norm kappa (>0), optional, default=dim_ens PDAF --- Further parameters --- PDAF n_modeltasks: Number of parallel model integration tasks PDAF >=1; not larger than total number of processors PDAF screen: Control verbosity of PDAF PDAF 0: no outputs PDAF 1: basic output (default) PDAF 2: 1 plus timing output PDAF 3: 2 plus debug output PDAF +++++++++ End of option overview for the LKNETF ++++++++++
PF (filtertype=PDAF_DA_PF=12)
PDAF Available options for PF: PDAF --- Sub-types (Parameter subtype) --- PDAF 0: Standard implementation with ensemble integration PDAF --- Integer parameters (Array param_int) --- PDAF param_int(1): dim_p PDAF Dimension of state vector (>0), required PDAF param_int(2): dim_ens PDAF Ensemble size (>0), required PDAF param_int(3): type_resample PDAF Resampling type, optional PDAF 1: probabilistic resamping (default) PDAF 2: stochastic universal resampling PDAF 3: residual resampling PDAF param_int(4): type_noise PDAF Type of ensemble perturbations, optional PDAF 0: no perturbations (default) PDAF 1: constant standard deviation PDAF 2: relative to ensemble standard deviation PDAF param_int(5) type_forget PDAF Type of forgetting factor; optional PDAF 0: forgetting factor on forecast ensemble (default) PDAF 2: forgetting factor on analysis ensemble PDAF param_int(6): not used PDAF param_int(7): type_winf PDAF Type of weights inflation; optional PDAF 0: no weights inflation (default) PDAF 1: inflate so that N_eff/N > param_real(2) PDAF param_int(8): observe_ens PDAF Application of observation operator H, optional PDAF Note: This parameter has no influence on the PF assimilation result PDAF 0: Apply H to ensemble mean to compute innovation PDAF 1: Apply H to ensemble states; then compute innovation from their mean (default) PDAF param_int(8)=1 is the recomended choice for nonlinear H PDAF param_int(9): type_obs_init PDAF Initialize observations before or after call to prepoststep_pdaf PDAF 0: Initialize observations before call to prepoststep_pdaf PDAF 1: Initialize observations after call to prepoststep_pdaf (default) PDAF --- Floating point parameters (Array param_real) --- PDAF param_real(1): forget PDAF Forgetting factor (usually >0 and <=1), required PDAF param_real(2): limit_winf PDAF Limit for weigts inflation N_eff/N > param_real(2), optional, default=0.0 PDAF param_real(3): noise_amp PDAF Ensemble perturbation level (>0), required, only used if param_int(4)>0 PDAF --- Further parameters --- PDAF n_modeltasks: Number of parallel model integration tasks PDAF >=1; not larger than total number of processors PDAF screen: Control verbosity of PDAF PDAF 0: no outputs PDAF 1: basic output (default) PDAF 2: 1 plus timing output PDAF 3: 2 plus debug output PDAF +++++++++ End of option overview for the PF ++++++++++
Option outputs using subtype=-1 - Observation Geneeration
GENOBS (filtertype=PDAF_DA_GENOBS=100)
PDAF Available options for GENOBS: PDAF --- Sub-types (Parameter subtype) --- PDAF 0: Standard implementation with ensemble integration PDAF --- Integer parameters (Array param_int) --- PDAF param_int(1): dim_p PDAF Dimension of state vector (>0), required PDAF param_int(2): dim_ens PDAF Ensemble size (>0), required PDAF param_int(3): seedset PDAF seed set index for random number generator, optional PDAF valid are values between 1 and 20; default=1 PDAF --- Floating point parameters (Array param_real) --- PDAF param_real(1): Forgetting factor (usually >0 and <=1), required, but not used PDAF --- Further parameters --- PDAF n_modeltasks: Number of parallel model integration tasks PDAF =1 for GENOBS; not larger than total number of processors PDAF =1 required for subtypes 10 and 11 PDAF screen: Control verbosity of PDAF PDAF 0: no outputs PDAF 1: basic output (default) PDAF 2: 1 plus timing output PDAF 3: 2 plus debug output PDAF +++++++++ End of option overview for GENOBS ++++++++++
Option outputs using subtype=-1 - 3D-Var Methods
3D-Var (filtertype=PDAF_DA_3DVAR=200)
PDAF Available options for 3D-Var: PDAF --- Sub-types (Parameter subtype) --- PDAF 0: incremental 3D-Var with parameterized covariance matrix PDAF 1: 3D ensemble Var using LESTKF for ensemble transformation PDAF 2: 3D ensemble Var using ESTKF for ensemble transformation PDAF 3: hybrid 3D-Var using LESTKF for ensemble transformation PDAF 4: hybrid 3D-Var using ESTKF for ensemble transformation PDAF --- Integer parameters (Array param_int) --- PDAF param_int(1): dim_p PDAF Dimension of state vector (>0), required PDAF param_int(2): dim_ens PDAF Ensemble size (>0), required PDAF param_int(3): type_opt PDAF Select optimization method (solver), required PDAF 1: LBFGS (default) PDAF 2: CG+ PDAF 3: direct implementation of CG PDAF 12: CG+ with decomposed control vector PDAF 13: direct implementation of CG with decomposed control vector PDAF param_int(4): size of parameterized control vector (for 3D-Var and hybrid 3D-Var), required PDAF param_int(5): size of ensemble control vector (required for ensemble and hybrid 3D-Var), PDAF param_int(6): Solver-specific parameter, optional PDAF LBFGS: parameter m (default=5) PDAF Number of corrections used in limited memory matrix; 3<=m<=20 PDAF CG+: parameter method (default=2) PDAF (1) Fletcher-Reeves, (2) Polak-Ribiere, (3) positive Polak-Ribiere PDAF CG: maximum number of iterations (default=200) PDAF param_int(7): Solver-specific parameter, optional PDAF CG+: parameter irest (default=1) PDAF (0) no restarts; (n>0) restart every n steps PDAF param_int(8): observe_ens PDAF Application of observation operator H, optional PDAF 0: Apply H to ensemble mean to compute innovation PDAF 1: Apply H to ensemble states; then compute innovation from their mean (default) PDAF param_int(8)=1 is the recomended choice for nonlinear H PDAF param_int(9): type_obs_init PDAF Initialize observations before or after call to prepoststep_pdaf PDAF 0: Initialize observations before call to prepoststep_pdaf PDAF 1: Initialize observations after call to prepoststep_pdaf (default) PDAF param_int(10): not used PDAF ___Options for ESTKF/LESTKF for En3DVar/hyb3DVar___ PDAF param_int(11) type_forget PDAF Type of forgetting factor; optional PDAF 0: fixed forgetting factor (default) PDAF 1: adaptive forgetting factor (experimental) PDAF 2: locally adaptive forgetting factor (experimental) PDAF param_int(12) type_trans PDAF Type of ensemble transformation matrix; optional PDAF 0: deterministic Omega (default) PDAF 1: random orthonormal Omega orthogonal to (1,...,1)^T PDAF 2: use product of 0 with random orthonomal matrix with eigenvector (1,...,1)^T PDAF (experimental; for random transformations, 0 or 1 are recommended) PDAF param_int(13) type_sqrt PDAF Type of transformation matrix square root; optional PDAF 0: symmetric square root (default) PDAF 1: Cholesky decomposition PDAF --- Floating point parameters (Array param_real) --- PDAF param_real(1): Forgetting factor (usually >0 and <=1), required; PDAF (only used for ensemble and hybrid 3D-Var) PDAF param_real(2): hybrid weight beta, optional (only for hybrid 3D-Var) PDAF range >=0.0 and <=1.0, =1.0 for pure ensemble 3D-var (default=0.5) PDAF param_real(3): Solver-specific parameter, optional PDAF LBFGS: Limit for stopping iterations (pgtol, default=1.0e-5) PDAF CG+: convergence parameter eps (default=1.0e-5) PDAF CG: convergence parameter eps (default=1.0e-6) PDAF param_real(4): Solver-specific parameter, optional PDAF LBFGS: Tolerance in termination test (factr, default=1.0e+7) PDAF --- Further parameters --- PDAF n_modeltasks: Number of parallel model integration tasks PDAF >=1 for subtypes >0; not larger than total number of processors PDAF =1 required for subtype 0 PDAF screen: Control verbosity of PDAF PDAF 0: no outputs PDAF 1: basic output (default) PDAF 2: 1 plus timing output PDAF 3: 2 plus debug output PDAF +++++++++ End of option overview for 3DVAR ++++++++++
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