ASTM D6589-05(2015)
(Guide)Standard Guide for Statistical Evaluation of Atmospheric Dispersion Model Performance
Standard Guide for Statistical Evaluation of Atmospheric Dispersion Model Performance
SIGNIFICANCE AND USE
5.1 Guidance is provided on designing model evaluation performance procedures and on the difficulties that arise in statistical evaluation of model performance caused by the stochastic nature of dispersion in the atmosphere. It is recognized there are examples in the literature where, knowingly or unknowingly, models were evaluated on their ability to describe something which they were never intended to characterize. This guide is attempting to heighten awareness, and thereby, to reduce the number of “unknowing” comparisons. A goal of this guide is to stimulate development and testing of evaluation procedures that accommodate the effects of natural variability. A technique is illustrated to provide information from which subsequent evaluation and standardization can be derived.
SCOPE
1.1 This guide provides techniques that are useful for the comparison of modeled air concentrations with observed field data. Such comparisons provide a means for assessing a model's performance, for example, bias and precision or uncertainty, relative to other candidate models. Methodologies for such comparisons are yet evolving; hence, modifications will occur in the statistical tests and procedures and data analysis as work progresses in this area. Until the interested parties agree upon standard testing protocols, differences in approach will occur. This guide describes a framework, or philosophical context, within which one determines whether a model's performance is significantly different from other candidate models. It is suggested that the first step should be to determine which model's estimates are closest on average to the observations, and the second step would then test whether the differences seen in the performance of the other models are significantly different from the model chosen in the first step. An example procedure is provided in Appendix X1 to illustrate an existing approach for a particular evaluation goal. This example is not intended to inhibit alternative approaches or techniques that will produce equivalent or superior results. As discussed in Section 6, statistical evaluation of model performance is viewed as part of a larger process that collectively is referred to as model evaluation.
1.2 This guide has been designed with flexibility to allow expansion to address various characterizations of atmospheric dispersion, which might involve dose or concentration fluctuations, to allow development of application-specific evaluation schemes, and to allow use of various statistical comparison metrics. No assumptions are made regarding the manner in which the models characterize the dispersion.
1.3 The focus of this guide is on end results, that is, the accuracy of model predictions and the discernment of whether differences seen between models are significant, rather than operational details such as the ease of model implementation or the time required for model calculations to be performed.
1.4 This guide offers an organized collection of information or a series of options and does not recommend a specific course of action. This guide cannot replace education or experience and should be used in conjunction with professional judgment. Not all aspects of this guide may be applicable in all circumstances. This guide is not intended to represent or replace the standard of care by which the adequacy of a given professional service must be judged, nor should it be applied without consideration of a project's many unique aspects. The word “Standard” in the title of this guide means only that the document has been approved through the ASTM consensus process.
1.5 The values stated in SI units are to be regarded as standard. No other units of measurement are included in this guide.
1.6 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility of the user of this standard to establish appropriate safety and health ...
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Designation: D6589 − 05 (Reapproved 2015)
Standard Guide for
Statistical Evaluation of Atmospheric Dispersion Model
Performance
This standard is issued under the fixed designation D6589; the number immediately following the designation indicates the year of
original adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. A
superscript epsilon (´) indicates an editorial change since the last revision or reapproval.
1. Scope operational details such as the ease of model implementation or
the time required for model calculations to be performed.
1.1 This guide provides techniques that are useful for the
1.4 This guide offers an organized collection of information
comparison of modeled air concentrations with observed field
or a series of options and does not recommend a specific course
data. Such comparisons provide a means for assessing a
of action. This guide cannot replace education or experience
model’s performance, for example, bias and precision or
and should be used in conjunction with professional judgment.
uncertainty, relative to other candidate models. Methodologies
Not all aspects of this guide may be applicable in all circum-
for such comparisons are yet evolving; hence, modifications
stances. This guide is not intended to represent or replace the
will occur in the statistical tests and procedures and data
standard of care by which the adequacy of a given professional
analysis as work progresses in this area. Until the interested
service must be judged, nor should it be applied without
parties agree upon standard testing protocols, differences in
consideration of a project’s many unique aspects. The word
approach will occur. This guide describes a framework, or
“Standard” in the title of this guide means only that the
philosophical context, within which one determines whether a
document has been approved through the ASTM consensus
model’s performance is significantly different from other
process.
candidate models. It is suggested that the first step should be to
determine which model’s estimates are closest on average to
1.5 The values stated in SI units are to be regarded as
the observations, and the second step would then test whether
standard. No other units of measurement are included in this
the differences seen in the performance of the other models are
guide.
significantly different from the model chosen in the first step.
1.6 This standard does not purport to address all of the
An example procedure is provided in Appendix X1 to illustrate
safety concerns, if any, associated with its use. It is the
an existing approach for a particular evaluation goal. This
responsibility of the user of this standard to establish appro-
example is not intended to inhibit alternative approaches or
priate safety, health, and environmental practices and deter-
techniques that will produce equivalent or superior results. As
mine the applicability of regulatory limitations prior to use.
discussed in Section 6, statistical evaluation of model perfor-
1.7 This international standard was developed in accor-
mance is viewed as part of a larger process that collectively is
dance with internationally recognized principles on standard-
referred to as model evaluation.
ization established in the Decision on Principles for the
1.2 This guide has been designed with flexibility to allow Development of International Standards, Guides and Recom-
expansion to address various characterizations of atmospheric mendations issued by the World Trade Organization Technical
dispersion, which might involve dose or concentration Barriers to Trade (TBT) Committee.
fluctuations, to allow development of application-specific
2. Referenced Documents
evaluation schemes, and to allow use of various statistical
comparison metrics. No assumptions are made regarding the
2.1 ASTM Standards:
manner in which the models characterize the dispersion.
D1356 Terminology Relating to Sampling and Analysis of
Atmospheres
1.3 The focus of this guide is on end results, that is, the
accuracy of model predictions and the discernment of whether
3. Terminology
differences seen between models are significant, rather than
3.1 Definitions—For definitions of terms used in this guide,
refer to Terminology D1356.
This guide is under the jurisdiction of ASTM Committee D22 on Air Quality
and is the direct responsibility of Subcommittee D22.11 on Meteorology. For referenced ASTM standards, visit the ASTM website, www.astm.org, or
Current edition approved April 1, 2015. Published April 2015. Originally contact ASTM Customer Service at service@astm.org. For Annual Book of ASTM
ε1
approved in 2000. Last previous edition approved in 2010 as D6589 – 05 (2010) . Standards volume information, refer to the standard’s Document Summary page on
DOI: 10.1520/D6589-05R15. the ASTM website.
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States
D6589 − 05 (2015)
3.2 Definitions of Terms Specific to This Standard: collectively is called model evaluation. Section 6 discusses the
3.2.1 atmospheric dispersion model, n—an idealization of components of model evaluation.
atmospheric physics and processes to calculate the magnitude
4.2 To statistically assess model performance, one must
and location of pollutant concentrations based on fate,
define an overall evaluation goal or purpose. This will suggest
transport, and dispersion in the atmosphere. This may take the
features (evaluation objectives) within the observed and mod-
form of an equation, algorithm, or series of equations/
eled concentration patterns to be compared, for example,
algorithms used to calculate average or time-varying concen-
maximum surface concentrations, lateral extent of a dispersing
tration. The model may involve numerical methods for solu-
plume. The selection and definition of evaluation objectives
tion.
typically are tailored to the model’s capabilities and intended
3.2.2 dispersion, absolute, n—the characterization of the
uses. The very nature of the problem of characterizing air
spreading of material released into the atmosphere based on a
quality and the way models are applied make one single or
coordinate system fixed in space.
absolute evaluation objective impossible to define that is
suitable for all purposes. The definition of the evaluation
3.2.3 dispersion, relative, n—the characterization of the
objectives will be restricted by the limited range conditions
spreading of material released into the atmosphere based on a
experienced in the available comparison data suitable for use.
coordinate system that is relative to the local median position
For each evaluation objective, a procedure will need to be
of the dispersing material.
defined that allows definition of the evaluation objective from
3.2.4 evaluation objective, n—a feature or characteristic,
the available observations of concentration values.
which can be defined through an analysis of the observed
concentration pattern, for example, maximum centerline con-
4.3 In assessing the performance of air quality models to
centration or lateral extent of the average concentration pattern
characterize a particular evaluation objective, one should
as a function of downwind distance, which one desires to
consider what the models are capable of providing. As dis-
assess the skill of the models to reproduce.
cussed in Section 7, most models attempt to characterize the
ensemble average concentration pattern. If such models should
3.2.5 evaluation procedure, n—the analysis steps to be
provide favorable comparisons with observed concentration
taken to compute the value of the evaluation objective from the
maxima, this is resulting from happenstance, rather than skill in
observed and modeled patterns of concentration values.
the model; therefore, in this discussion, it is suggested a model
3.2.6 fate, n—the destiny of a chemical or biological pol-
be assessed on its ability to reproduce what it was designed to
lutant after release into the environment.
produce, for at least in these comparisons, one can be assured
3.2.7 model input value, n—characterizations that must be
that zero bias with the least amount of scatter is by definition
estimated or provided by the model developer or user before
good model performance.
model calculations can be performed.
4.4 As an illustration of the principles espoused in this
3.2.8 regime, n—a repeatable narrow range of conditions,
guide, a procedure is provided in Appendix X1 for comparison
defined in terms of model input values, which may or may not
of observed and modeled near-centerline concentration values,
be explicitly employed by all models being tested, needed for
which accommodates the fact that observed concentration
dispersion model calculations. It is envisioned that the disper-
values include a large component of stochastic, and possibly
sion observed should be similar for all cases having similar
deterministic, variability unaccounted for by current models.
model input values.
The procedure provides an objective statistical test of whether
3.2.9 uncertainty, n—refers to a lack of knowledge about
differences seen in model performance are significant.
specific factors or parameters. This includes measurement
errors, sampling errors, systematic errors, and differences
5. Significance and Use
arising from simplification of real-world processes. In
5.1 Guidance is provided on designing model evaluation
principle, uncertainty can be reduced with further information
performance procedures and on the difficulties that arise in
or knowledge (1).
statistical evaluation of model performance caused by the
3.2.10 variability, n—refers to differences attributable to
stochastic nature of dispersion in the atmosphere. It is recog-
true heterogeneity or diversity in atmospheric processes that
nized there are examples in the literature where, knowingly or
result in part from natural random processes. Variability
unknowingly, models were evaluated on their ability to de-
usually is not reducible by further increases in knowledge, but
scribe something which they were never intended to charac-
it can in principle be better characterized (1).
terize. This guide is attempting to heighten awareness, and
4. Summary of Guide thereby, to reduce the number of “unknowing” comparisons. A
goal of this guide is to stimulate development and testing of
4.1 Statistical evaluation of dispersion model performance
evaluation procedures that accommodate the effects of natural
with field data is viewed as part of a larger process that
variability. A technique is illustrated to provide information
3 from which subsequent evaluation and standardization can be
The boldface numbers in parentheses refer to the list of references at the end of
this standard. derived.
D6589 − 05 (2015)
6. Model Evaluation 6.4 Statistical Evaluations with Field Data—The objective
comparison of modeled concentrations with observed field data
6.1 Background—Air quality simulation models have been
provides a means for assessing model performance. Due to the
used for many decades to characterize the transport and
limited supply of evaluation data sets, there are severe practical
dispersion of material in the atmosphere (2-4). Early evalua-
limits in assessing model performance. For this reason, the
tions of model performance usually relied on linear least-
conclusions reached in the science peer reviews (see 6.3) and
squares analyses of observed versus modeled values, using
the supportive analyses (see 6.5) have particular relevance in
traditional scatter plots of the values, (5-7). During the 1980s,
deciding whether a model can be applied for the defined model
attempts have been made to encourage the standardization of
evaluation objectives. In order to conduct a statistical
methods used to judge air quality model performance (8-11).
comparison, one will have to define one or more evaluation
Further development of these proposed statistical evaluation
objectives for which objective comparisons are desired (Sec-
procedures was needed, as it was found that the rote applica-
tion 10). As discussed in 8.4.4, the process of summarizing the
tion of statistical metrics, such as those listed in (8), was
overall performance of a model over the range of conditions
incapable of discerning differences in model performance (12),
experienced within a field experiment typically involves deter-
whereas if the evaluation results were sorted by stability and
mining two points for each of the model evaluation objectives:
distance downwind, then differences in modeling skill could be
which of the models being assessed has on average the smallest
discerned (13). It was becoming increasingly evident that the
combined bias and scatter in comparisons with observations,
models were characterizing only a small portion of the ob-
and whether the differences seen in the comparisons with the
served variations in the concentration values (14). To better
other models statistically are significant in light of the uncer-
deduce the statistical significance of differences seen in model
tainties in the observations.
performance in the face of large unaccounted for uncertainties
6.5 Other Tasks Supportive to Model Evaluation—As atmo-
and variations, investigators began to explore the use of
spheric dispersion models become more sophisticated, it is not
bootstrap techniques (15). By the late 1980s, most of the model
easy to detect coding errors in the implementation of the model
performance evaluations involved the use of bootstrap tech-
algorithms. And as models become more complex, discerning
niques in the comparison of maximum values of modeled and
the sensitivity of the modeling results to input parameter
observed cumulative frequency distributions of the concentra-
variations becomes less clear; hence, two important tasks that
tions values (16). Even though the procedures and metrics to be
support model evaluation efforts are verification of software
employed in describing the performance of air quality simula-
and sensitivity and Monte Carlo analyses.
tion models are still evolving (17-19), there has been a general
6.5.1 Verification of Software—Often a set of modeling
acceptance that defining performance of air quality models
algorithms will require numerical solution. An important task
needs to address the large uncertainties inherent in attempting
supportive to a model evaluation is a review in which the
to characterize atmospheric fate, transport and dispersion
mathematics described in the technical description of the
processes. There also has been a consensus reached on the
model are compared w
...
This document is not an ASTM standard and is intended only to provide the user of an ASTM standard an indication of what changes have been made to the previous version. Because
it may not be technically possible to adequately depict all changes accurately, ASTM recommends that users consult prior editions as appropriate. In all cases only the current version
of the standard as published by ASTM is to be considered the official document.
´1
Designation: D6589 − 05 (Reapproved 2010) D6589 − 05 (Reapproved 2015)
Standard Guide for
Statistical Evaluation of Atmospheric Dispersion Model
Performance
This standard is issued under the fixed designation D6589; the number immediately following the designation indicates the year of
original adoption or, in the case of revision, the year of last revision. A number in parentheses indicates the year of last reapproval. A
superscript epsilon (´) indicates an editorial change since the last revision or reapproval.
ε NOTE—Reapproved with editorial corrections in April 2010.
1. Scope
1.1 This guide provides techniques that are useful for the comparison of modeled air concentrations with observed field data.
Such comparisons provide a means for assessing a model’s performance, for example, bias and precision or uncertainty, relative
to other candidate models. Methodologies for such comparisons are yet evolving; hence, modifications will occur in the statistical
tests and procedures and data analysis as work progresses in this area. Until the interested parties agree upon standard testing
protocols, differences in approach will occur. This guide describes a framework, or philosophical context, within which one
determines whether a model’s performance is significantly different from other candidate models. It is suggested that the first step
should be to determine which model’s estimates are closest on average to the observations, and the second step would then test
whether the differences seen in the performance of the other models are significantly different from the model chosen in the first
step. An example procedure is provided in Appendix X1 to illustrate an existing approach for a particular evaluation goal. This
example is not intended to inhibit alternative approaches or techniques that will produce equivalent or superior results. As
discussed in Section 6, statistical evaluation of model performance is viewed as part of a larger process that collectively is referred
to as model evaluation.
1.2 This guide has been designed with flexibility to allow expansion to address various characterizations of atmospheric
dispersion, which might involve dose or concentration fluctuations, to allow development of application-specific evaluation
schemes, and to allow use of various statistical comparison metrics. No assumptions are made regarding the manner in which the
models characterize the dispersion.
1.3 The focus of this guide is on end results, that is, the accuracy of model predictions and the discernment of whether
differences seen between models are significant, rather than operational details such as the ease of model implementation or the
time required for model calculations to be performed.
1.4 This guide offers an organized collection of information or a series of options and does not recommend a specific course
of action. This guide cannot replace education or experience and should be used in conjunction with professional judgment. Not
all aspects of this guide may be applicable in all circumstances. This guide is not intended to represent or replace the standard of
care by which the adequacy of a given professional service must be judged, nor should it be applied without consideration of a
project’s many unique aspects. The word “Standard” in the title of this guide means only that the document has been approved
through the ASTM consensus process.
1.5 The values stated in SI units are to be regarded as standard. No other units of measurement are included in this guide.
1.6 This standard does not purport to address all of the safety concerns, if any, associated with its use. It is the responsibility
of the user of this standard to establish appropriate safety and health practices and to determine the applicability of regulatory
limitations prior to use.
2. Referenced Documents
2.1 ASTM Standards:
D1356 Terminology Relating to Sampling and Analysis of Atmospheres
This guide is under the jurisdiction of ASTM Committee D22 on Air Quality and is the direct responsibility of Subcommittee D22.11 on Meteorology.
Current edition approved April 1, 2010April 1, 2015. Published July 2010April 2015. Originally approved in 2000. Last previous edition approved in 20052010 as
ε1
D6589 - 05.D6589 – 05 (2010) . DOI: 10.1520/D6589-05R10E01.10.1520/D6589-05R15.
For referenced ASTM standards, visit the ASTM website, www.astm.org, or contact ASTM Customer Service at service@astm.org. For Annual Book of ASTM Standards
volume information, refer to the standard’s Document Summary page on the ASTM website.
Copyright © ASTM International, 100 Barr Harbor Drive, PO Box C700, West Conshohocken, PA 19428-2959. United States
D6589 − 05 (2015)
3. Terminology
3.1 Definitions—For definitions of terms used in this guide, refer to Terminology D1356.
3.2 Definitions of Terms Specific to This Standard:
3.2.1 atmospheric dispersion model, n—an idealization of atmospheric physics and processes to calculate the magnitude and
location of pollutant concentrations based on fate, transport, and dispersion in the atmosphere. This may take the form of an
equation, algorithm, or series of equations/algorithms used to calculate average or time-varying concentration. The model may
involve numerical methods for solution.
3.2.2 dispersion, absolute, n—the characterization of the spreading of material released into the atmosphere based on a
coordinate system fixed in space.
3.2.3 dispersion, relative, n—the characterization of the spreading of material released into the atmosphere based on a
coordinate system that is relative to the local median position of the dispersing material.
3.2.4 evaluation objective, n—a feature or characteristic, which can be defined through an analysis of the observed concentration
pattern, for example, maximum centerline concentration or lateral extent of the average concentration pattern as a function of
downwind distance, which one desires to assess the skill of the models to reproduce.
3.2.5 evaluation procedure, n—the analysis steps to be taken to compute the value of the evaluation objective from the observed
and modeled patterns of concentration values.
3.2.6 fate, n—the destiny of a chemical or biological pollutant after release into the environment.
3.2.7 model input value, n—characterizations that must be estimated or provided by the model developer or user before model
calculations can be performed.
3.2.8 regime, n—a repeatable narrow range of conditions, defined in terms of model input values, which may or may not be
explicitly employed by all models being tested, needed for dispersion model calculations. It is envisioned that the dispersion
observed should be similar for all cases having similar model input values.
3.2.9 uncertainty, n—refers to a lack of knowledge about specific factors or parameters. This includes measurement errors,
sampling errors, systematic errors, and differences arising from simplification of real-world processes. In principle, uncertainty can
be reduced with further information or knowledge (1)). .
3.2.10 variability, n—refers to differences attributable to true heterogeneity or diversity in atmospheric processes that result in
part from natural random processes. Variability usually is not reducible by further increases in knowledge, but it can in principle
be better characterized (1).
4. Summary of Guide
4.1 Statistical evaluation of dispersion model performance with field data is viewed as part of a larger process that collectively
is called model evaluation. Section 6 discusses the components of model evaluation.
4.2 To statistically assess model performance, one must define an overall evaluation goal or purpose. This will suggest features
(evaluation objectives) within the observed and modeled concentration patterns to be compared, for example, maximum surface
concentrations, lateral extent of a dispersing plume. The selection and definition of evaluation objectives typically are tailored to
the model’s capabilities and intended uses. The very nature of the problem of characterizing air quality and the way models are
applied make one single or absolute evaluation objective impossible to define that is suitable for all purposes. The definition of
the evaluation objectives will be restricted by the limited range conditions experienced in the available comparison data suitable
for use. For each evaluation objective, a procedure will need to be defined that allows definition of the evaluation objective from
the available observations of concentration values.
4.3 In assessing the performance of air quality models to characterize a particular evaluation objective, one should consider
what the models are capable of providing. As discussed in Section 7, most models attempt to characterize the ensemble average
concentration pattern. If such models should provide favorable comparisons with observed concentration maxima, this is resulting
from happenstance, rather than skill in the model; therefore, in this discussion, it is suggested a model be assessed on its ability
to reproduce what it was designed to produce, for at least in these comparisons, one can be assured that zero bias with the least
amount of scatter is by definition good model performance.
4.4 As an illustration of the principles espoused in this guide, a procedure is provided in Appendix X1 for comparison of
observed and modeled near-centerline concentration values, which accommodates the fact that observed concentration values
include a large component of stochastic, and possibly deterministic, variability unaccounted for by current models. The procedure
provides an objective statistical test of whether differences seen in model performance are significant.
The boldface numbers in parentheses refer to the list of references at the end of this standard.
D6589 − 05 (2015)
5. Significance and Use
5.1 Guidance is provided on designing model evaluation performance procedures and on the difficulties that arise in statistical
evaluation of model performance caused by the stochastic nature of dispersion in the atmosphere. It is recognized there are
examples in the literature where, knowingly or unknowingly, models were evaluated on their ability to describe something which
they were never intended to characterize. This guide is attempting to heighten awareness, and thereby, to reduce the number of
“unknowing” comparisons. A goal of this guide is to stimulate development and testing of evaluation procedures that accommodate
the effects of natural variability. A technique is illustrated to provide information from which subsequent evaluation and
standardization can be derived.
6. Model Evaluation
6.1 Background—Air quality simulation models have been used for many decades to characterize the transport and dispersion
of material in the atmosphere (2-4). Early evaluations of model performance usually relied on linear least-squares analyses of
observed versus modeled values, using traditional scatter plots of the values, (5-7). During the 1980s, attempts have been made
to encourage the standardization of methods used to judge air quality model performance (8-11). Further development of these
proposed statistical evaluation procedures was needed, as it was found that the rote application of statistical metrics, such as those
listed in (8), was incapable of discerning differences in model performance (12), whereas if the evaluation results were sorted by
stability and distance downwind, then differences in modeling skill could be discerned (13). It was becoming increasingly evident
that the models were characterizing only a small portion of the observed variations in the concentration values (14). To better
deduce the statistical significance of differences seen in model performance in the face of large unaccounted for uncertainties and
variations, investigators began to explore the use of bootstrap techniques (15). By the late 1980s, most of the model performance
evaluations involved the use of bootstrap techniques in the comparison of maximum values of modeled and observed cumulative
frequency distributions of the concentrations values (16). Even though the procedures and metrics to be employed in describing
the performance of air quality simulation models are still evolving (17-19), there has been a general acceptance that defining
performance of air quality models needs to address the large uncertainties inherent in attempting to characterize atmospheric fate,
transport and dispersion processes. There also has been a consensus reached on the philosophical reasons that models of earth
science processes can never be validated, in the sense of claiming that a model is truthfully representing natural processes. No
general empirical proposition about the natural world can be certain, since there will always remain the prospect that future
observations may call the theory in question (20). It is seen that numerical models of air pollution are a form of a highly complex
scientific hypothesis concerning natural processes, that can be confirmed through comparison with observations, but never
validated.
6.2 Components of Model Evaluation—A model evaluation includes science peer reviews and statistical evaluations with field
data. The completion of each of these components assumes specific model goals and evaluation objectives (see Section 10) have
been defined.
6.3 Science Peer Reviews—Given the complexity of characterizing atmospheric processes, and the inevitable necessity of
limiting model algorithms to a resolvable set, one component of a model evaluation is to review the model’s science to confirm
that the construct is reasonable and defensible for the defined evaluation objectives. A key part of the scientific peer review will
include the review of residual plots where modeled and observed evaluation objectives are compared over a range of model inputs,
for example, maximum concentrations as a function of estimated plume rise or as a function of distance downwind.
6.4 Statistical Evaluations with Field Data—The objective comparison of modeled concentrations with observed field data
provides a means for assessing model performance. Due to the limited supply of evaluation data sets, there are severe practical
limits in assessing model performance. For this reason, the conclusions reached in the science peer reviews (see 6.3) and the
supportive analyses (see 6.5) have particular relevance in deciding whether a model can be a
...
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