-
================================================================================
ADAO module documentation
================================================================================
-The ADAO module provides **data assimilation** features in SALOME context. It is
-based on usage on other SALOME modules, namely YACS and EFICAS, and on an
-extensible generic underlying data assimilation library.
+.. image:: images/ADAO_logo.png
+ :align: center
+ :width: 20%
+
+The ADAO module provides **data assimilation and optimization** features in
+SALOME context. It is based on usage of other SALOME modules, namely YACS and
+EFICAS, and on usage of a generic underlying data assimilation library.
Briefly stated, Data Assimilation is a methodological framework to compute the
optimal estimate of the inaccessible true value of a system state over time. It
uses information coming from experimental measurements or observations, and from
numerical *a priori* models, including information about their errors. Parts of
-the framework are also known as *parameter estimation*, *inverse problems*,
-*bayesian estimation*, *optimal interpolation*, etc. More details can be found
-in the section :ref:`section_theory`.
-
-The documentation of this module is divided in 5 parts, the first one being an
-introduction. The second part briefly introduces data assimilation and concepts.
-The third part describes how to use the module ADAO. The fourth part gives
-examples on ADAO usage. Users interested in quick use of the module can jump to
-this section :ref:`section_examples`, but a valuable use of the module requires
-to read and come back regularly to the section :ref:`section_using`. The last
-part focuses on advanced usages of the module, how to get more information, or
-how to use it without the graphical user interface (GUI).
-
-In all this documentation, we use standard notations of data assimilation, as
-described in [Ide97]. Moreover, vectors are written horizontally or vertically
-without making difference. Matrices are written either normally, or with a
-condensed notation, consisting in the use of a space to separate values and a
-"``;``" to separate the rows, in a continuous line.
+the framework are also known under the names of *parameter estimation*, *inverse
+problems*, *Bayesian estimation*, *optimal interpolation*, etc. More details can
+be found in the section :ref:`section_theory`.
+
+The documentation of this module is divided in parts. The first one
+:ref:`section_intro` is an introduction. The second part :ref:`section_theory`
+briefly introduces data assimilation, optimization and concepts. The third part
+:ref:`section_using` describes how to use the module ADAO. The fourth part
+:ref:`section_reference` gives a detailed description of all the ADAO commands
+and keywords. The fifth part :ref:`section_examples` gives examples on ADAO
+usage. Users interested in quick use of the module can jump to this section, but
+a valuable use of the module requires to read and come back regularly to the
+third and fourth ones. The last part :ref:`section_advanced` focuses on advanced
+usages of the module, how to get more information, or how to use it by
+scripting, without the graphical user interface (GUI).
+
+In all this documentation, we use standard notations of linear algebra, data
+assimilation (as described in [Ide97]_) and optimization. In particular, vectors
+are written horizontally or vertically without making difference. Matrices are
+written either normally, or with a condensed notation, consisting in the use of
+a space to separate values and a "``;``" to separate the rows, in a continuous
+line.
+
+Table of contents
+-----------------
.. toctree::
:maxdepth: 2
intro
theory
using
+ reference
examples
advanced
+ licence
+ 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
+* :ref:`section_glossary`
