Observer norm complement and documentation

[modules/adao.git] / doc / en / ref_algorithm_3DVAR.rst

diff --git a/doc/en/ref_algorithm_3DVAR.rst b/doc/en/ref_algorithm_3DVAR.rst
index 3cce8182e7177304849c2a6bdf9d787445566ec3..430b0f47f8a7366ec9a57a659137540b332430b3 100644 (file)
--- a/doc/en/ref_algorithm_3DVAR.rst
+++ b/doc/en/ref_algorithm_3DVAR.rst
@@ -165,8 +165,9 @@ The options of the algorithm are the following:
     these variables being calculated and stored by default. The possible names
     are in the following list: ["APosterioriCorrelations",
     "APosterioriCovariance", "APosterioriStandardDeviations",
-    "APosterioriVariances", "BMA", "CostFunctionJ", "CurrentOptimum",
-    "CurrentState", "IndexOfOptimum", "Innovation", "InnovationAtCurrentState",
+    "APosterioriVariances", "BMA", "CostFunctionJ",
+    "CostFunctionJAtCurrentOptimum", "CurrentOptimum", "CurrentState",
+    "IndexOfOptimum", "Innovation", "InnovationAtCurrentState",
     "MahalanobisConsistency", "OMA", "OMB", "SigmaObs2",
     "SimulatedObservationAtBackground", "SimulatedObservationAtCurrentOptimum",
     "SimulatedObservationAtCurrentState", "SimulatedObservationAtOptimum",
@@ -283,6 +284,27 @@ The conditional outputs of the algorithm are the following:
 
     Example : ``bma = ADD.get("BMA")[-1]``
 
+  CostFunctionJAtCurrentOptimum
+    *List of values*. Each element is a value of the error function :math:`J`.
+    At each step, the value corresponds to the optimal state found from the
+    beginning.
+
+    Example : ``JACO = ADD.get("CostFunctionJAtCurrentOptimum")[:]``
+
+  CostFunctionJbAtCurrentOptimum
+    *List of values*. Each element is a value of the error function :math:`J^b`,
+    that is of the background difference part. At each step, the value
+    corresponds to the optimal state found from the beginning.
+
+    Example : ``JbACO = ADD.get("CostFunctionJbAtCurrentOptimum")[:]``
+
+  CostFunctionJoAtCurrentOptimum
+    *List of values*. Each element is a value of the error function :math:`J^o`,
+    that is of the observation difference part. At each step, the value
+    corresponds to the optimal state found from the beginning.
+
+    Example : ``JoACO = ADD.get("CostFunctionJoAtCurrentOptimum")[:]``
+
   CurrentOptimum
     *List of vectors*. Each element is the optimal state obtained at the current
     step of the optimization algorithm. It is not necessarely the last state.