CHAPTER 4

DATA QUALITY ASSURANCE AND MODEL DIAGNOSTIC ANALYSES

This chapter provides general guidance for quality assurance

testing of component data input fields and diagnostic testing of

base case episodes. These analyses are designed to establish and

improve reliability of the input data and proper functioning of the

model.

Although the UAM has been evaluated on a number of historical

data bases, measures of model behavior with respect to observed

data are necessary for new applications. Model developers and

users perform diagnostic tests to uncover potential input data gaps

that, when corrected, may lead to improved treatment of model

processes. Regulators need some indication that the model captures

the key features of the base meteorological episodes being applied

in the model simulations in order to have confidence in a model's

ability to predict future ozone (1) after applying projected growth

and planned emission controls and (2) after applying alternative

emission control strategies.

Important prerequisites for a model performance evaluation

(see Chapter 5) are (1) quality assurance testing of model inputs

and (2) diagnostic testing of the base meteorological episode

simulation to ensure that the model is functioning properly and

that apparently accurate model results are being obtained for the

right reasons. For example, quality assurance testing of input

data helps to ensure that apparently good model results have not

resulted from compensating errors in input data.

An excellent compilation of model performance evaluation

techniques, including diagnostic tests and related issues, is

contained in Reference 17. This reference serves as the basis for

this chapter and for the model performance evaluation described in

Chapter 5. Various graphical and numerical measures described

below are treated in detail in Reference 17.

 

 

 

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