--- /dev/null
+.. _section_bibliography:
+
+================================================================================
+Bibliography
+================================================================================
+
+.. [Argaud09] Argaud J.-P., Bouriquet B., Hunt J., *Data Assimilation from Operational and Industrial Applications to Complex Systems*, Mathematics Today, pp.150-152, October 2009
+
+.. [Bouttier99] Bouttier B., Courtier P., *Data assimilation concepts and methods*, Meteorological Training Course Lecture Series, ECMWF, 1999, http://www.ecmwf.int/newsevents/training/rcourse_notes/pdf_files/Assim_concepts.pdf
+
+.. [Bocquet04] Bocquet M., *Introduction aux principes et méthodes de l'assimilation de données en géophysique*, Lecture Notes, 2004-2008, http://cerea.enpc.fr/HomePages/bocquet/assim.pdf
+
+.. [Byrd95] Byrd R. H., Lu P., Nocedal J., *A Limited Memory Algorithm for Bound Constrained Optimization*, SIAM Journal on Scientific and Statistical Computing, 16(5), pp.1190-1208, 1995
+
+.. [Ide97] Ide K., Courtier P., Ghil M., Lorenc A. C., *Unified notation for data assimilation: operational, sequential and variational*, Journal of the Meteorological Society of Japan, 75(1B), pp.181-189, 1997
+
+.. [Kalnay03] Kalnay E., *Atmospheric Modeling, Data Assimilation and Predictability*, Cambridge University Press, 2003
+
+.. [Tarantola87] Tarantola A., *Inverse Problem: Theory Methods for Data Fitting and Parameter Estimation*, Elsevier, 1987
+
+.. [Talagrand97] Talagrand O., *Assimilation of Observations, an Introduction*, Journal of the Meteorological Society of Japan, 75(1B), pp.191-209, 1997
+
+.. [WikipediaDA] Wikipedia/Data_assimilation: http://en.wikipedia.org/wiki/Data_assimilation
+
+.. [Zhu97] Zhu C., Byrd R. H., Nocedal J., *L-BFGS-B: Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained optimization*, ACM Transactions on Mathematical Software, Vol 23(4), pp.550-560, 1997
definition in the ADAO GUI and implicit data definition by external files. The
present script looks like::
- #-*-coding:iso-8859-1-*-
- #
import numpy
#
# Definition of the Background as a vector
dictionary. All the keys inside the dictionary are optional, they all have
default values, and can exist without being used. For example::
- #-*-coding:iso-8859-1-*-
- #
AlgorithmParameters = {
"Minimizer" : "CG", # Possible choice : "LBFGSB", "TNC", "CG", "BFGS"
"MaximumNumberOfSteps" : 10,
The objective is to set up the input and output definitions of a physical case
by external python scripts, using a general non-linear operator, adding control
on parameters and so on... The complete framework scripts can be found in the
-ADAO examples standard directory.
+ADAO skeletons examples directory under the name
+"*External_data_definition_by_scripts*".
Experimental set up
+++++++++++++++++++
knowledge of the true state, is build as a normal random perturbation of 20% the
true state :math:`\mathbf{x}^t` for each component, which is:
- ``Xb = Xt + normal(0,20%*Xt)``
+ ``Xb = Xt + normal(0, 20%*Xt)``
To describe the background error covariances matrix :math:`\mathbf{B}`, we make
as previously the hypothesis of uncorrelated errors (that is, a diagonal matrix,
building function, in a Python script file named
``Physical_data_and_covariance_matrices.py``::
- #-*-coding:iso-8859-1-*-
- #
import numpy
#
def True_state():
of a *required ADAO variable* in order to export the defined value. It is done
in a Python script file named ``Script_Background_xb.py``::
- #-*-coding:iso-8859-1-*-
- #
- import sys ; sys.path.insert(0,"# INSERT PHYSICAL SCRIPT PATH")
from Physical_data_and_covariance_matrices import True_state
import numpy
#
background of the true state, using the convenient function already defined. It
is done in a Python script file named ``Script_BackgroundError_B.py``::
- #-*-coding:iso-8859-1-*-
- #
- import sys ; sys.path.insert(0,"# INSERT PHYSICAL SCRIPT PATH")
from Physical_data_and_covariance_matrices import True_state, Simple_Matrix
#
xt, names = True_state()
non-linear direct physical simulation exists. The script is partly reproduced
here for convenience::
- #-*-coding:iso-8859-1-*-
- #
def FunctionH( XX ):
""" Direct non-linear simulation operator """
#
The following example Python script file named
``Script_ObservationOperator_H.py``, illustrates the case::
- #-*-coding:iso-8859-1-*-
- #
- import sys ; sys.path.insert(0,"# INSERT PHYSICAL SCRIPT PATH")
import Physical_simulation_functions
import numpy, logging
#
error covariances matrix :math:`\mathbf{R}` can be generated. It is done in two
Python script files, the first one being named ``Script_Observation_yo.py``::
- #-*-coding:iso-8859-1-*-
- #
- import sys ; sys.path.insert(0,"# INSERT PHYSICAL SCRIPT PATH")
from Physical_data_and_covariance_matrices import True_state
from Physical_simulation_functions import FunctionH
#
and the second one named ``Script_ObservationError_R.py``::
- #-*-coding:iso-8859-1-*-
- #
- import sys ; sys.path.insert(0,"# INSERT PHYSICAL SCRIPT PATH")
from Physical_data_and_covariance_matrices import True_state, Simple_Matrix
from Physical_simulation_functions import FunctionH
#
following parameters can be defined in a Python script file named
``Script_AlgorithmParameters.py``::
- #-*-coding:iso-8859-1-*-
- #
# Creating the required ADAO variable
# -----------------------------------
AlgorithmParameters = {
following example Python script file named ``Script_UserPostAnalysis.py``,
illustrates the fact::
- #-*-coding:iso-8859-1-*-
- #
- import sys ; sys.path.insert(0,"# INSERT PHYSICAL SCRIPT PATH")
from Physical_data_and_covariance_matrices import True_state
import numpy
#
using
examples
advanced
+ bibliography
Indices and tables
================================================================================
* :ref:`genindex`
* :ref:`search`
-
-.. [Argaud09] Argaud J.-P., Bouriquet B., Hunt J., *Data Assimilation from Operational and Industrial Applications to Complex Systems*, Mathematics Today, pp.150-152, October 2009
-
-.. [Bouttier99] Bouttier B., Courtier P., *Data assimilation concepts and methods*, Meteorological Training Course Lecture Series, ECMWF, 1999, http://www.ecmwf.int/newsevents/training/rcourse_notes/pdf_files/Assim_concepts.pdf
-
-.. [Bocquet04] Bocquet M., *Introduction aux principes et méthodes de l'assimilation de données en géophysique*, Lecture Notes, 2004-2008, http://cerea.enpc.fr/HomePages/bocquet/assim.pdf
-
-.. [Byrd95] Byrd R. H., Lu P., Nocedal J., *A Limited Memory Algorithm for Bound Constrained Optimization*, SIAM Journal on Scientific and Statistical Computing, 16(5), pp.1190-1208, 1995
-
-.. [Ide97] Ide K., Courtier P., Ghil M., Lorenc A. C., *Unified notation for data assimilation: operational, sequential and variational*, Journal of the Meteorological Society of Japan, 75(1B), pp.181-189, 1997
-
-.. [Kalnay03] Kalnay E., *Atmospheric Modeling, Data Assimilation and Predictability*, Cambridge University Press, 2003
-
-.. [Tarantola87] Tarantola A., *Inverse Problem: Theory Methods for Data Fitting and Parameter Estimation*, Elsevier, 1987
-
-.. [Talagrand97] Talagrand O., *Assimilation of Observations, an Introduction*, Journal of the Meteorological Society of Japan, 75(1B), pp.191-209, 1997
-
-.. [WikipediaDA] Wikipedia/Data_assimilation: http://en.wikipedia.org/wiki/Data_assimilation
-
-.. [Zhu97] Zhu C., Byrd R. H., Nocedal J., *L-BFGS-B: Algorithm 778: L-BFGS-B, FORTRAN routines for large scale bound constrained optimization*, ACM Transactions on Mathematical Software, Vol 23(4), pp.550-560, 1997
