# 14.9. Checking algorithm “ObservationSimulationComparisonTest”¶

## 14.9.1. Description¶

This algorithm allows to analyze in a simple way the stability of the difference between measures and an operator during its execution. The operator is any operator, so it can be the observation operator as well as the evolution operator , as long as it is provided in each case according to the Requirements for functions describing an operator. The operator is considered as depending on a vector variable and returning another vector variable .

The algorithm verifies that the difference is stable, that the operator is working correctly and that its call is compatible with its usage in ADAO algorithms. In practice, it allows to call one or several times the operator, activating or not the “debug” mode during execution. It is very similar in its current behavior to a Checking algorithm “FunctionTest” but it tests the stability of the measurement-calculation difference.

Statistics on input and output vectors, and potentially on the classical data assimilation error function , are given for each execution of operator, and an another global statistic is given at the end. The precision of printed outputs can be controlled to facilitate automatic tests of operator. It may also be useful to check the entries themselves beforehand with the intended test Checking algorithm “InputValuesTest”.

## 14.9.2. Some noteworthy properties of the implemented methods¶

To complete the description, we summarize here a few notable properties of the algorithm methods or of their implementations. These properties may have an influence on how it is used or on its computational performance. For further information, please refer to the more comprehensive references given at the end of this algorithm description.

• The methods proposed by this algorithm do not require derivation of the objective function or of one of the operators, thus avoiding this additional cost when derivatives are calculated numerically by multiple evaluations.

## 14.9.3. Optional and required commands¶

The general required commands, available in the editing user graphical or textual interface, are the following:

CheckingPoint

Vector. The variable indicates the vector used as the state around which to perform the required check, noted and similar to the background . It is defined as a “Vector” or “VectorSerie” type object. Its availability in output is conditioned by the boolean “Stored” associated with input.

BackgroundError

Matrix. This indicates the background error covariance matrix, previously noted as . Its value is defined as a “Matrix” type object, a “ScalarSparseMatrix” type object, or a “DiagonalSparseMatrix” type object, as described in detail in the section Requirements to describe covariance matrices. Its availability in output is conditioned by the boolean “Stored” associated with input.

Observation

List of vectors. The variable indicates the observation vector used for data assimilation or optimization, and usually noted . Its value is defined as an object of type “Vector” if it is a single observation (temporal or not) or “VectorSeries” if it is a succession of observations. Its availability in output is conditioned by the boolean “Stored” associated in input.

ObservationError

Matrix. The variable indicates the observation error covariance matrix, usually noted as . It is defined as a “Matrix” type object, a “ScalarSparseMatrix” type object, or a “DiagonalSparseMatrix” type object, as described in detail in the section Requirements to describe covariance matrices. Its availability in output is conditioned by the boolean “Stored” associated with input.

ObservationOperator

Operator. The variable indicates the observation operator, usually noted as , which transforms the input parameters to results to be compared to observations . Its value is defined as a “Function” type object or a “Matrix” type one. In the case of “Function” type, different functional forms can be used, as described in the section Requirements for functions describing an operator. If there is some control included in the observation, the operator has to be applied to a pair .

The general optional commands, available in the editing user graphical or textual interface, are indicated in List of commands and keywords for an ADAO checking case. Moreover, the parameters of the command “AlgorithmParameters” allow to choose the specific options, described hereafter, of the algorithm. See Description of options of an algorithm by “AlgorithmParameters” for the good use of this command.

The options are the following:

NumberOfPrintedDigits

Integer value. This key indicates the number of digits of precision for floating point printed output. The default is 5, with a minimum of 0.

Example: {"NumberOfPrintedDigits":5}

NumberOfRepetition

Integer value. This key indicates the number of time to repeat the function evaluation. The default is 1.

Example: {"NumberOfRepetition":3}

SetDebug

Boolean value. This variable leads to the activation, or not, of the debug mode during the function or operator evaluation. The default is “False”, the choices are “True” or “False”.

Example: {"SetDebug":False}

ShowElementarySummary

Boolean value. This variable leads to the activation, or not, of the calculation and display of a summary at each elementary evaluation of the test. The default value is “True”, the choices are “True” or “False”.

Example : {"ShowElementarySummary":False}

StoreSupplementaryCalculations

List of names. This list indicates the names of the supplementary variables, that can be available during or at the end of the algorithm, if they are initially required by the user. Their availability involves, potentially, costly calculations or memory consumptions. The default is then a void list, none of these variables being calculated and stored by default (excepted the unconditional variables). The possible names are in the following list (the detailed description of each named variable is given in the following part of this specific algorithmic documentation, in the sub-section “Information and variables available at the end of the algorithm”): [ “CostFunctionJ”, “CostFunctionJb”, “CostFunctionJo”, “CurrentState”, “Innovation”, “InnovationAtCurrentState”, “OMB”, “SimulatedObservationAtCurrentState”, ].

Example : {"StoreSupplementaryCalculations":["CurrentState", "Residu"]}

## 14.9.4. Information and variables available at the end of the algorithm¶

At the output, after executing the algorithm, there are information and variables originating from the calculation. The description of Variables and information available at the output show the way to obtain them by the method named get, of the variable “ADD” of the post-processing in graphical interface, or of the case in textual interface. The input variables, available to the user at the output in order to facilitate the writing of post-processing procedures, are described in the Inventory of potentially available information at the output.

Permanent outputs (non conditional)

The unconditional outputs of the algorithm are the following:

None

Set of on-demand outputs (conditional or not)

The whole set of algorithm outputs (conditional or not), sorted by alphabetical order, is the following:

CostFunctionJ

List of values. Each element is a value of the chosen error function .

Example: J = ADD.get("CostFunctionJ")[:]

CostFunctionJb

List of values. Each element is a value of the error function , that is of the background difference part. If this part does not exist in the error function, its value is zero.

Example: Jb = ADD.get("CostFunctionJb")[:]

CostFunctionJo

List of values. Each element is a value of the error function , that is of the observation difference part.

Example: Jo = ADD.get("CostFunctionJo")[:]

CurrentState

List of vectors. Each element is a usual state vector used during the iterative algorithm procedure.

Example: xs = ADD.get("CurrentState")[:]

Innovation

List of vectors. Each element is an innovation vector, which is in static the difference between the optimal and the background, and in dynamic the evolution increment.

Example: d = ADD.get("Innovation")[-1]

InnovationAtCurrentState

List of vectors. Each element is an innovation vector at current state before analysis.

Example: ds = ADD.get("InnovationAtCurrentState")[-1]

OMB

List of vectors. Each element is a vector of difference between the observation and the background state in the observation space.

Example: omb = ADD.get("OMB")[-1]

SimulatedObservationAtCurrentState

List of vectors. Each element is an observed vector simulated by the observation operator from the current state, that is, in the observation space.

Example: hxs = ADD.get("SimulatedObservationAtCurrentState")[-1]

## 14.9.5. Python (TUI) use examples¶

Here is one or more very simple examples of the proposed algorithm and its parameters, written in [DocR] Textual User Interface for ADAO (TUI/API). Moreover, when it is possible, the information given as input also allows to define an equivalent case in [DocR] Graphical User Interface for ADAO (GUI/EFICAS).

This example analyzes the (repeated) running of a simulation operator explicitly given in matrix form (described for the test by the observation command “ObservationOperator”), applied to a particular state :math: mathbf{x} on which to test (described for the test by the “CheckingPoint” command), compared to measurements (described for the test by the “Observation” command) by the difference OMB = y - F(x) (Observation minus evaluation at Background) and the standard data assimilation cost function J.

The test is repeated a configurable number of times, and a final statistic allows to quickly check the good behavior of the operator. The simplest diagnostic consists in checking, at the very end of the display, the order of magnitude of the variations of the values indicated as the average of the differences between the repeated outputs and their average, under the part here entitled “Launching statistical summary calculation for 5 states”. For a satisfactory operator, the values of differences from the mean and the standard deviations should be close to the numerical zero.

# -*- coding: utf-8 -*-
#
from numpy import array, eye, ones
case.set("CheckingPoint",       Vector = array([0., 1., 2.]) )
case.set("Observation",         Vector = ones(3) )
case.set("ObservationOperator", Matrix = 1/3 * eye(3) )
case.setAlgorithmParameters(
Algorithm='ObservationSimulationComparisonTest',
Parameters={
'NumberOfRepetition' : 5,
'NumberOfPrintedDigits' : 2,
'ShowElementarySummary':False,
'StoreSupplementaryCalculations': [
'CostFunctionJ',
],
},
)
case.execute()


The execution result is the following:


OBSERVATIONSIMULATIONCOMPARISONTEST
===================================

This test allows to analyze the (repetition of the) launch of some
given simulation operator F, applied to one single vector argument x,
and its comparison to observations or measures y through the innovation
difference OMB = y - F(x) (Observation minus evaluation at Background)
and (if required) the data assimilation standard cost function J.
The output shows simple statistics related to its successful execution,
or related to the similarities of repetition of its execution.

===> Information before launching:
-----------------------------

Characteristics of input vector X, internally converted:
Type...............: <class 'numpy.ndarray'>
Length of vector...: 3
Minimum value......: 0.00e+00
Maximum value......: 2.00e+00
Mean of vector.....: 1.00e+00
Standard error.....: 8.16e-01
L2 norm of vector..: 2.24e+00

Characteristics of input vector of observations Yobs, internally converted:
Type...............: <class 'numpy.ndarray'>
Length of vector...: 3
Minimum value......: 1.00e+00
Maximum value......: 1.00e+00
Mean of vector.....: 1.00e+00
Standard error.....: 0.00e+00
L2 norm of vector..: 1.73e+00

---------------------------------------------------------------------------

===> Beginning of repeated evaluation, without activating debug

---------------------------------------------------------------------------

===> End of repeated evaluation, without deactivating debug

---------------------------------------------------------------------------

===> Launching statistical summary calculation for 5 states

---------------------------------------------------------------------------

===> Statistical analysis of the outputs obtained through sequential repeated evaluations

(Remark: numbers that are (about) under 2e-16 represent 0 to machine precision)

Number of evaluations...........................: 5

Characteristics of the whole set of outputs Y:
Size of each of the outputs...................: 3
Minimum value of the whole set of outputs.....: 0.00e+00
Maximum value of the whole set of outputs.....: 6.67e-01
Mean of vector of the whole set of outputs....: 3.33e-01
Standard error of the whole set of outputs....: 2.72e-01

Characteristics of the vector Ym, mean of the outputs Y:
Size of the mean of the outputs...............: 3
Minimum value of the mean of the outputs......: 0.00e+00
Maximum value of the mean of the outputs......: 6.67e-01
Mean of the mean of the outputs...............: 3.33e-01
Standard error of the mean of the outputs.....: 2.72e-01

Characteristics of the mean of the differences between the outputs Y and their mean Ym:
Size of the mean of the differences...........: 3
Minimum value of the mean of the differences..: 0.00e+00
Maximum value of the mean of the differences..: 0.00e+00
Mean of the mean of the differences...........: 0.00e+00
Standard error of the mean of the differences.: 0.00e+00

---------------------------------------------------------------------------

===> Statistical analysis of the OMB differences obtained through sequential repeated evaluations

(Remark: numbers that are (about) under 2e-16 represent 0 to machine precision)

Number of evaluations...........................: 5

Characteristics of the whole set of OMB differences:
Size of each of the outputs...................: 3
Minimum value of the whole set of differences.: 3.33e-01
Maximum value of the whole set of differences.: 1.00e+00
Mean of vector of the whole set of differences: 6.67e-01
Standard error of the whole set of differences: 2.72e-01

Characteristics of the vector Dm, mean of the OMB differences:
Size of the mean of the differences...........: 3
Minimum value of the mean of the differences..: 3.33e-01
Maximum value of the mean of the differences..: 1.00e+00
Mean of the mean of the differences...........: 6.67e-01
Standard error of the mean of the differences.: 2.72e-01

Characteristics of the mean of the differences between the OMB differences and their mean Dm:
Size of the mean of the differences...........: 3
Minimum value of the mean of the differences..: 0.00e+00
Maximum value of the mean of the differences..: 0.00e+00
Mean of the mean of the differences...........: 0.00e+00
Standard error of the mean of the differences.: 0.00e+00

---------------------------------------------------------------------------

===> Statistical analysis of the cost function J values obtained through sequential repeated evaluations

Number of evaluations...........................: 5

Characteristics of the whole set of data assimilation cost function J values:
Minimum value of the whole set of J...........: 7.78e-01
Maximum value of the whole set of J...........: 7.78e-01
Mean of vector of the whole set of J..........: 7.78e-01
Standard error of the whole set of J..........: 0.00e+00
(Remark: variations of the cost function J only come from the observation part Jo of J)

---------------------------------------------------------------------------

End of the "OBSERVATIONSIMULATIONCOMPARISONTEST" verification

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