From: Jean-Philippe ARGAUD Date: Tue, 23 Feb 2021 20:08:31 +0000 (+0100) Subject: Documentation and references update for variants X-Git-Tag: V9_7_0b1~25 X-Git-Url: http://git.salome-platform.org/gitweb/?a=commitdiff_plain;h=263da11497db7bcb907aab8e00cacf81b8d6f7f7;p=modules%2Fadao.git Documentation and references update for variants --- diff --git a/doc/en/bibliography.rst b/doc/en/bibliography.rst index 0626edc..f57c677 100644 --- a/doc/en/bibliography.rst +++ b/doc/en/bibliography.rst @@ -47,6 +47,10 @@ Bibliography .. [Chakraborty08] Chakraborty U.K., *Advances in differential evolution*, Studies in computational intelligence, Vol.143, Springer, 2008 +.. [Cohn98] Cohn S. E., Da Silva A., Guo J., Sienkiewicz M., Lamich D., *Assessing the effects of data selection with the DAO Physical-space Statistical Analysis System*, Monthly Weather Review, 126, pp.2913–2926, 1998 + +.. [Courtier94] Courtier P., Thépaut J.-N., Hollingsworth A., *A strategy for operational implementation of 4D-Var, using an incremental approach*, Quarterly Journal of the Royal Meteorological Society, 120, pp.1367–1387, 1994 + .. [Das11] Das S., Suganthan P. N., *Differential Evolution: A Survey of the State-of-the-art*, IEEE Transactions on Evolutionary Computation, 15(1), pp.4-31, 2011 .. [Das16] Das S., Mullick S. S., Suganthan P. N., *Recent Advances in Differential Evolution - An Updated Survey*, Swarm and Evolutionary Computation, 27, pp.1-30, 2016 @@ -69,6 +73,12 @@ Bibliography .. [Koenker01] Koenker R., Hallock K. F., *Quantile Regression*, Journal of Economic Perspectives, 15(4), pp.143-156, 2001 +.. [LeDimet86] Le Dimet F.-X., Talagrand 0., *Variational algorithms for analysis and assimilation of meteorological observations*, Tellus, 38A, pp.97-110, 1986 + +.. [Lorenc86] Lorenc A. C., *Analysis methods for numerical weather prediction*, Quarterly Journal of the Royal Meteorological Society, 112, pp.1177-1194, 1986 + +.. [Lorenc88] Lorenc A. C., *Optimal nonlinear objective analysis*, Quarterly Journal of the Royal Meteorological Society, 114, pp.205–240, 1988 + .. [Morales11] Morales J. L., Nocedal J., *L-BFGS-B: Remark on Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained optimization*, ACM Transactions on Mathematical Software, 38(1), 2011 .. [Nelder65] Nelder J. A., Mead R., *A simplex method for function minimization*, The Computer Journal, 7, pp.308-313, 1965 diff --git a/doc/en/ref_algorithm_3DVAR.rst b/doc/en/ref_algorithm_3DVAR.rst index cca9e82..de5b597 100644 --- a/doc/en/ref_algorithm_3DVAR.rst +++ b/doc/en/ref_algorithm_3DVAR.rst @@ -121,6 +121,8 @@ StoreSupplementaryCalculations Example : ``{"StoreSupplementaryCalculations":["BMA", "CurrentState"]}`` +.. include:: snippets/Variant_3DVAR.rst + .. ------------------------------------ .. .. include:: snippets/Header2Algo04.rst @@ -217,6 +219,11 @@ StoreSupplementaryCalculations .. include:: snippets/Header2Algo07.rst - [Byrd95]_ +- [Cohn98]_ +- [Courtier94]_ +- [LeDimet86]_ +- [Lorenc86]_ +- [Lorenc88]_ - [Morales11]_ - [Talagrand97]_ - [Zhu97]_ diff --git a/doc/en/ref_algorithm_EnsembleKalmanFilter.rst b/doc/en/ref_algorithm_EnsembleKalmanFilter.rst index afc1066..d09c850 100644 --- a/doc/en/ref_algorithm_EnsembleKalmanFilter.rst +++ b/doc/en/ref_algorithm_EnsembleKalmanFilter.rst @@ -31,9 +31,9 @@ Calculation algorithm "*EnsembleKalmanFilter*" .. include:: snippets/Header2Algo01.rst This algorithm realizes an estimation of the state of a dynamic system by a -Ensemble Kalman Filter (EnKF), avoiding to have to perform the tangent and +Ensemble Kalman Filter (EnKF), avoiding to have to perform the tangent or adjoint operators for the observation and evolution operators, as in the simple -or extended Kalman filter. +or extended Kalman filters. It applies to non-linear observation and incremental evolution (process) operators with excellent robustness and performance qualities. It can be @@ -52,6 +52,16 @@ In case of linear of "slightly" non-linear operators, one can easily use the to evaluate on small systems. One can verify the linearity of the operators with the help of the :ref:`section_ref_algorithm_LinearityTest`. +There exists a lot of variants of this algorithm. Stable and robust ones are proposed here: + +- "EnKF" (Ensemble Kalman Filter), original stochastic algorithm, allowing consistent treatment of non-linear evolution operator, +- "ETKF" (Ensemble-Transform Kalman Filter), deterministic EnKF algorithm, allowing treatment of non-linear evolution operator with a lot less members (one recommends to use a number of members on the order of 10 or even sometimes less), +- "ETKF-N" (Ensemble-Transform Kalman Filter of finite size N), ETKF algorithm of "finite size N", yhat doesn't need inflation that is often required with the other algorithms, +- "MLEF" (Maximum Likelihood Kalman Filter), deterministic EnKF scheme, allowing in addition the consistent treament of non-linear observation operator, +- "IEnKF" (Iterative_EnKF), deterministic EnKF scheme, improving treament of operators non-linearities. + +Without being a universal recommandation, one recommend to use EnKF as a reference algorithm, and the other algorithms (in this order) as means to obtain less costly data assimilation with hopefully the same quality. + .. ------------------------------------ .. .. include:: snippets/Header2Algo02.rst @@ -114,6 +124,8 @@ StoreSupplementaryCalculations Example : ``{"StoreSupplementaryCalculations":["BMA", "CurrentState"]}`` +.. include:: snippets/Variant_EnKF.rst + .. ------------------------------------ .. .. include:: snippets/Header2Algo04.rst diff --git a/doc/en/snippets/Minimizer_DE.rst b/doc/en/snippets/Minimizer_DE.rst index b85bf18..d2e9229 100644 --- a/doc/en/snippets/Minimizer_DE.rst +++ b/doc/en/snippets/Minimizer_DE.rst @@ -1,4 +1,15 @@ -.. index:: single: Minimizer +.. index:: + single: Minimizer + pair: Minimizer ; BEST1BIN", + pair: Minimizer ; BEST1EXP", + pair: Minimizer ; RAND1EXP", + pair: Minimizer ; RANDTOBEST1EXP + pair: Minimizer ; BEST2EXP + pair: Minimizer ; RAND2EXP + pair: Minimizer ; RANDTOBEST1BIN + pair: Minimizer ; BEST2BIN + pair: Minimizer ; RAND2BIN + pair: Minimizer ; RAND1BIN Minimizer *Predefined name*. This key allows to choose the optimization strategy for @@ -6,15 +17,15 @@ Minimizer the multiples crossover and mutation strategies, are "BEST1BIN", "BEST1EXP", - "RAND1EXP", - "RANDTOBEST1EXP", + "BEST2BIN", "BEST2EXP", + "RAND1BIN", + "RAND1EXP", + "RAND2BIN", "RAND2EXP", "RANDTOBEST1BIN", - "BEST2BIN", - "RAND2BIN", - "RAND1BIN". - It is greatly recommanded to keep the default value. + "RANDTOBEST1EXP". + It is highly recommended to keep the default value. Example: ``{"Minimizer":"BEST1BIN"}`` diff --git a/doc/en/snippets/Minimizer_DFO.rst b/doc/en/snippets/Minimizer_DFO.rst index ae8fba2..4c103b7 100644 --- a/doc/en/snippets/Minimizer_DFO.rst +++ b/doc/en/snippets/Minimizer_DFO.rst @@ -1,14 +1,21 @@ -.. index:: single: Minimizer +.. index:: + single: Minimizer + pair: Minimizer ; BOBYQA + pair: Minimizer ; COBYLA + pair: Minimizer ; NEWUOA + pair: Minimizer ; POWELL + pair: Minimizer ; SIMPLEX + pair: Minimizer ; SUBPLEX Minimizer *Predefined name*. This key allows to choose the optimization minimizer. The default choice is "BOBYQA", and the possible ones are - "BOBYQA" (minimization with or without constraints by quadratic approximation [Powell09]_), - "COBYLA" (minimization with or without constraints by linear approximation [Powell94]_ [Powell98]_). - "NEWUOA" (minimization with or without constraints by iterative quadratic approximation [Powell04]_), - "POWELL" (minimization unconstrained using conjugate directions [Powell64]_), - "SIMPLEX" (minimization with or without constraints using Nelder-Mead simplex algorithm [Nelder65]_), - "SUBPLEX" (minimization with or without constraints using Nelder-Mead on a sequence of subspaces [Rowan90]_). + "BOBYQA" (minimization with or without constraints by quadratic approximation, see [Powell09]_), + "COBYLA" (minimization with or without constraints by linear approximation, see [Powell94]_ [Powell98]_). + "NEWUOA" (minimization with or without constraints by iterative quadratic approximation, see [Powell04]_), + "POWELL" (minimization unconstrained using conjugate directions, see [Powell64]_), + "SIMPLEX" (minimization with or without constraints using Nelder-Mead simplex algorithm, see [Nelder65]_), + "SUBPLEX" (minimization with or without constraints using Nelder-Mead on a sequence of subspaces, see [Rowan90]_). Remark: the "POWELL" method perform a dual outer/inner loops optimization, leading then to less control on the cost function evaluation number because it is the outer loop limit than is controlled. If precise control on the diff --git a/doc/en/snippets/Minimizer_xDVAR.rst b/doc/en/snippets/Minimizer_xDVAR.rst index ed4f589..c412afa 100644 --- a/doc/en/snippets/Minimizer_xDVAR.rst +++ b/doc/en/snippets/Minimizer_xDVAR.rst @@ -1,9 +1,15 @@ - .. index:: single: Minimizer +.. index:: + single: Minimizer + pair: Minimizer ; LBFGSB + pair: Minimizer ; TNC + pair: Minimizer ; CG + pair: Minimizer ; BFGS + pair: Minimizer ; NCG Minimizer *Predefined name*. This key allows to choose the optimization minimizer. The default choice is "LBFGSB", and the possible ones are - "LBFGSB" (nonlinear constrained minimizer, see [Byrd95]_, [Morales11]_ and [Zhu97]_), + "LBFGSB" (nonlinear constrained minimizer, see [Byrd95]_, [Morales11]_, [Zhu97]_), "TNC" (nonlinear constrained minimizer), "CG" (nonlinear unconstrained minimizer), "BFGS" (nonlinear unconstrained minimizer), @@ -12,4 +18,3 @@ Minimizer Example : ``{"Minimizer":"LBFGSB"}`` - diff --git a/doc/en/snippets/Variant_3DVAR.rst b/doc/en/snippets/Variant_3DVAR.rst new file mode 100644 index 0000000..f23494e --- /dev/null +++ b/doc/en/snippets/Variant_3DVAR.rst @@ -0,0 +1,19 @@ +.. index:: + single: Variant + pair: Variant ; 3DVAR + pair: Variant ; 3DVAR-VAN + pair: Variant ; 3DVAR-Incr + pair: Variant ; 3DVAR-PSAS + +Variant + *Predifined name*. This key allows to choose one of the possible variants + for the main algorithm. The default variant is the original "3DVAR", and the + possible ones are + "3DVAR" (3D Variational analysis, see [Lorenc86]_, [LeDimet86]_, [Talagrand97]_), + "3DVAR-VAN" (3D Variational Analysis with No inversion of B, see [Lorenc88]_), + "3DVAR-Incr" (Incremental 3DVAR, see [Courtier94]_), + "3DVAR-PSAS" (Physical-space Statistical Analysis Scheme for 3DVAR, see [Cohn98]_), + It is highly recommended to keep the default value. + + Exemple : + ``{"Variant":"3DVAR"}`` diff --git a/doc/en/snippets/Variant_EnKF.rst b/doc/en/snippets/Variant_EnKF.rst new file mode 100644 index 0000000..82a6473 --- /dev/null +++ b/doc/en/snippets/Variant_EnKF.rst @@ -0,0 +1,23 @@ +.. index:: + single: Variant + pair: Variant ; EnKF + pair: Variant ; ETKF + pair: Variant ; ETKF-N + pair: Variant ; MLEF + pair: Variant ; IEnKF + +Variant + *Predifined name*. This key allows to choose one of the possible variants + for the main algorithm. The default variant is the original "EnKF", and the + possible ones are + "EnKF" (Ensemble Kalman Filter), + "ETKF" (Ensemble-Transform Kalman Filter), + "ETKF-N" (Ensemble-Transform Kalman Filter), + "MLEF" (Maximum Likelihood Kalman Filter), + "IEnKF" (Iterative_EnKF). + One recommends to try the "ETKF-N" or "IEnKF" variants, and to reduce the + number of members to about 10 or less for all variants other then the + original "EnKF". + + Example : + ``{"Variant":"EnKF"}`` diff --git a/doc/fr/bibliography.rst b/doc/fr/bibliography.rst index 76f5dfe..a73cae8 100644 --- a/doc/fr/bibliography.rst +++ b/doc/fr/bibliography.rst @@ -47,6 +47,10 @@ Bibliographie .. [Chakraborty08] Chakraborty U.K., *Advances in differential evolution*, Studies in computational intelligence, Vol.143, Springer, 2008 +.. [Cohn98] Cohn S. E., Da Silva A., Guo J., Sienkiewicz M., Lamich D., *Assessing the effects of data selection with the DAO Physical-space Statistical Analysis System*, Monthly Weather Review, 126, pp.2913–2926, 1998 + +.. [Courtier94] Courtier P., Thépaut J.-N., Hollingsworth A., *A strategy for operational implementation of 4D-Var, using an incremental approach*, Quarterly Journal of the Royal Meteorological Society, 120, pp.1367–1387, 1994 + .. [Das11] Das S., Suganthan P. N., *Differential Evolution: A Survey of the State-of-the-art*, IEEE Transactions on Evolutionary Computation, 15(1), pp.4-31, 2011 .. [Das16] Das S., Mullick S. S., Suganthan P. N., *Recent Advances in Differential Evolution - An Updated Survey*, Swarm and Evolutionary Computation, 27, pp.1-30, 2016 @@ -69,6 +73,12 @@ Bibliographie .. [Koenker01] Koenker R., Hallock K. F., *Quantile Regression*, Journal of Economic Perspectives, 15(4), pp.143-156, 2001 +.. [LeDimet86] Le Dimet F.-X., Talagrand 0., *Variational algorithms for analysis and assimilation of meteorological observations*, Tellus, 38A, pp.97-110, 1986 + +.. [Lorenc86] Lorenc A. C., *Analysis methods for numerical weather prediction*, Quarterly Journal of the Royal Meteorological Society, 112, pp.1177-1194, 1986 + +.. [Lorenc88] Lorenc A. C., *Optimal nonlinear objective analysis*, Quarterly Journal of the Royal Meteorological Society, 114, pp.205–240, 1988 + .. [Morales11] Morales J. L., Nocedal J., *L-BFGS-B: Remark on Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained optimization*, ACM Transactions on Mathematical Software, 38(1), 2011 .. [Nelder65] Nelder J. A., Mead R., *A simplex method for function minimization*, The Computer Journal, 7, pp.308-313, 1965 diff --git a/doc/fr/ref_algorithm_3DVAR.rst b/doc/fr/ref_algorithm_3DVAR.rst index 641a25d..2bb864e 100644 --- a/doc/fr/ref_algorithm_3DVAR.rst +++ b/doc/fr/ref_algorithm_3DVAR.rst @@ -124,6 +124,8 @@ StoreSupplementaryCalculations Exemple : ``{"StoreSupplementaryCalculations":["BMA", "CurrentState"]}`` +.. include:: snippets/Variant_3DVAR.rst + .. ------------------------------------ .. .. include:: snippets/Header2Algo04.rst @@ -220,6 +222,11 @@ StoreSupplementaryCalculations .. include:: snippets/Header2Algo07.rst - [Byrd95]_ +- [Cohn98]_ +- [Courtier94]_ +- [LeDimet86]_ +- [Lorenc86]_ +- [Lorenc88]_ - [Morales11]_ - [Talagrand97]_ - [Zhu97]_ diff --git a/doc/fr/ref_algorithm_EnsembleKalmanFilter.rst b/doc/fr/ref_algorithm_EnsembleKalmanFilter.rst index ba788a5..ab48488 100644 --- a/doc/fr/ref_algorithm_EnsembleKalmanFilter.rst +++ b/doc/fr/ref_algorithm_EnsembleKalmanFilter.rst @@ -53,12 +53,15 @@ l':ref:`section_ref_algorithm_KalmanFilter`, qui sont souvent largement moins coûteux en évaluations sur de petits systèmes. On peut vérifier la linéarité des opérateurs à l'aide de l':ref:`section_ref_algorithm_LinearityTest`. -Les variantes de cette algorithme sont nombreuses, et on propose ici les versions stables et robustes suivantes : -- "EnKF" (Ensemble Kalman Filter), algorithme stochastique, permettant de traiter de manière consistante un opérateur d'évolution non-linéaire, -- "ETKF" (Ensemble-Transform Kalman Filter), algorithme déterministe d'EnKF, permettant de traiter un opérateur d'évolution non-linéaire avec beaucoup moins de membres, -- "ETKF-N" (Ensemble-Transform Kalman Filter of finite size N), algorithme d'ETKF dit de "taille finie N", évitant de recourir l'inflation -- "MLEF" (Maximum Likelihood Kalman Filter), schéma déterministe d'EnKF permettant en plus de traiter de manière consistante un opérateur d'observation non-linéaire), -- "IEnKF" (Iterative_EnKF), +Il existe de nombreuses variantes de cet algorithme. On propose ici des formes stables et robustes suivantes : + +- "EnKF" (Ensemble Kalman Filter), algorithme stochastique original, permettant de traiter de manière consistante un opérateur d'évolution non-linéaire, +- "ETKF" (Ensemble-Transform Kalman Filter), algorithme déterministe d'EnKF, permettant de traiter un opérateur d'évolution non-linéaire avec beaucoup moins de membres (on recommande d'utiliser un nombre de membres de l'ordre de 10 ou même parfois moins), +- "ETKF-N" (Ensemble-Transform Kalman Filter of finite size N), algorithme d'ETKF dit de "taille finie N", évitant de recourir à une inflation souvent nécessaire avec les autres algorithms +- "MLEF" (Maximum Likelihood Kalman Filter), schéma déterministe d'EnKF, permettant en plus de traiter de manière consistante un opérateur d'observation non-linéaire), +- "IEnKF" (Iterative_EnKF), schéma déterministe d'EnKF, améliorant le traitement des non-linéarités des opérateurs. + +Sans pouvoir prétendre à l'universalité, on recommande d'utiliser l'EnKF comme référence, et les autres algorithmes (dans l'ordre) comme des moyens pour obtenir une assimilation de données plus économique et de qualité éventuellement similaire. .. ------------------------------------ .. .. include:: snippets/Header2Algo02.rst @@ -123,6 +126,8 @@ StoreSupplementaryCalculations Exemple : ``{"StoreSupplementaryCalculations":["BMA", "CurrentState"]}`` +.. include:: snippets/Variant_EnKF.rst + .. ------------------------------------ .. .. include:: snippets/Header2Algo04.rst diff --git a/doc/fr/snippets/Minimizer_DE.rst b/doc/fr/snippets/Minimizer_DE.rst index 0134316..74132e3 100644 --- a/doc/fr/snippets/Minimizer_DE.rst +++ b/doc/fr/snippets/Minimizer_DE.rst @@ -1,4 +1,15 @@ -.. index:: single: Minimizer +.. index:: + single: Minimizer + pair: Minimizer ; BEST1BIN", + pair: Minimizer ; BEST1EXP", + pair: Minimizer ; RAND1EXP", + pair: Minimizer ; RANDTOBEST1EXP + pair: Minimizer ; BEST2EXP + pair: Minimizer ; RAND2EXP + pair: Minimizer ; RANDTOBEST1BIN + pair: Minimizer ; BEST2BIN + pair: Minimizer ; RAND2BIN + pair: Minimizer ; RAND1BIN Minimizer *Nom prédéfini*. Cette clé permet de changer la stratégie de minimisation @@ -7,14 +18,14 @@ Minimizer décrites par les clés "BEST1BIN", "BEST1EXP", - "RAND1EXP", - "RANDTOBEST1EXP", + "BEST2BIN", "BEST2EXP", + "RAND1BIN", + "RAND1EXP", + "RAND2BIN", "RAND2EXP", "RANDTOBEST1BIN", - "BEST2BIN", - "RAND2BIN", - "RAND1BIN". + "RANDTOBEST1EXP". Il est fortement conseillé de conserver la valeur par défaut. Exemple : diff --git a/doc/fr/snippets/Minimizer_DFO.rst b/doc/fr/snippets/Minimizer_DFO.rst index ebbc374..db11d59 100644 --- a/doc/fr/snippets/Minimizer_DFO.rst +++ b/doc/fr/snippets/Minimizer_DFO.rst @@ -1,14 +1,21 @@ -.. index:: single: Minimizer +.. index:: + single: Minimizer + pair: Minimizer ; BOBYQA + pair: Minimizer ; COBYLA + pair: Minimizer ; NEWUOA + pair: Minimizer ; POWELL + pair: Minimizer ; SIMPLEX + pair: Minimizer ; SUBPLEX Minimizer *Nom prédéfini*. Cette clé permet de changer le minimiseur pour l'optimiseur. Le choix par défaut est "BOBYQA", et les choix possibles sont - "BOBYQA" (minimisation avec ou sans contraintes par approximation quadratique [Powell09]_), - "COBYLA" (minimisation avec ou sans contraintes par approximation linéaire [Powell94]_ [Powell98]_). - "NEWUOA" (minimisation avec ou sans contraintes par approximation quadratique itérative [Powell04]_), - "POWELL" (minimisation sans contraintes de type directions conjuguées [Powell64]_), + "BOBYQA" (minimisation avec ou sans contraintes par approximation quadratique, voir [Powell09]_), + "COBYLA" (minimisation avec ou sans contraintes par approximation linéaire, voir [Powell94]_ [Powell98]_). + "NEWUOA" (minimisation avec ou sans contraintes par approximation quadratique itérative, voir [Powell04]_), + "POWELL" (minimisation sans contraintes de type directions conjuguées, voir [Powell64]_), "SIMPLEX" (minimisation avec ou sans contraintes de type simplexe ou Nelder-Mead, voir [Nelder65]_), - "SUBPLEX" (minimisation avec ou sans contraintes de type simplexe sur une suite de sous-espaces [Rowan90]_). + "SUBPLEX" (minimisation avec ou sans contraintes de type simplexe sur une suite de sous-espaces, voir [Rowan90]_). Remarque : la méthode "POWELL" effectue une optimisation par boucles imbriquées interne/externe, conduisant ainsi à un contrôle relaché du nombre d'évaluations de la fonctionnelle à optimiser. Si un contrôle précis du diff --git a/doc/fr/snippets/Minimizer_xDVAR.rst b/doc/fr/snippets/Minimizer_xDVAR.rst index 2ab061b..6eec587 100644 --- a/doc/fr/snippets/Minimizer_xDVAR.rst +++ b/doc/fr/snippets/Minimizer_xDVAR.rst @@ -1,9 +1,15 @@ -.. index:: single: Minimizer +.. index:: + single: Minimizer + pair: Minimizer ; LBFGSB + pair: Minimizer ; TNC + pair: Minimizer ; CG + pair: Minimizer ; BFGS + pair: Minimizer ; NCG Minimizer *Nom prédéfini*. Cette clé permet de changer le minimiseur pour l'optimiseur. Le choix par défaut est "LBFGSB", et les choix possibles sont - "LBFGSB" (minimisation non linéaire sous contraintes, voir [Byrd95]_, [Morales11]_ et [Zhu97]_), + "LBFGSB" (minimisation non linéaire sous contraintes, voir [Byrd95]_, [Morales11]_, [Zhu97]_), "TNC" (minimisation non linéaire sous contraintes), "CG" (minimisation non linéaire sans contraintes), "BFGS" (minimisation non linéaire sans contraintes), diff --git a/doc/fr/snippets/Variant_3DVAR.rst b/doc/fr/snippets/Variant_3DVAR.rst new file mode 100644 index 0000000..d3a58e3 --- /dev/null +++ b/doc/fr/snippets/Variant_3DVAR.rst @@ -0,0 +1,19 @@ +.. index:: + single: Variant + pair: Variant ; 3DVAR + pair: Variant ; 3DVAR-VAN + pair: Variant ; 3DVAR-Incr + pair: Variant ; 3DVAR-PSAS + +Variant + *Nom prédéfini*. Cette clé permet de choisir l'une des variantes possibles + pour l'algorithme principal. La variante par défaut est le "3DVAR" d'origine, + et les choix possibles sont + "3DVAR" (3D Variational analysis, voir [Lorenc86]_, [LeDimet86]_, [Talagrand97]_), + "3DVAR-VAN" (3D Variational Analysis with No inversion of B, voir [Lorenc88]_), + "3DVAR-Incr" (Incremental 3DVAR, voir [Courtier94]_), + "3DVAR-PSAS" (Physical-space Statistical Analysis Scheme for 3DVAR, voir [Cohn98]_), + Il est fortement conseillé de conserver la valeur par défaut. + + Exemple : + ``{"Variant":"3DVAR"}`` diff --git a/doc/fr/snippets/Variant_EnKF.rst b/doc/fr/snippets/Variant_EnKF.rst new file mode 100644 index 0000000..689c90b --- /dev/null +++ b/doc/fr/snippets/Variant_EnKF.rst @@ -0,0 +1,23 @@ +.. index:: + single: Variant + pair: Variant ; EnKF + pair: Variant ; ETKF + pair: Variant ; ETKF-N + pair: Variant ; MLEF + pair: Variant ; IEnKF + +Variant + *Nom prédéfini*. Cette clé permet de choisir l'une des variantes possibles + pour l'algorithme principal. La variante par défaut est l'"EnKF" d'origine, + et les choix possibles sont + "EnKF" (Ensemble Kalman Filter), + "ETKF" (Ensemble-Transform Kalman Filter), + "ETKF-N" (Ensemble-Transform Kalman Filter), + "MLEF" (Maximum Likelihood Kalman Filter), + "IEnKF" (Iterative_EnKF). + Il est conseillé d'essayer les variantes "ETKF-N" ou "IEnKF", et de réduire + le nombre de membres à une dizaine ou moins pour toutes les variantes autres + que l'"EnKF" original. + + Exemple : + ``{"Variant":"EnKF"}``