Model Evaluation and Assessment

ALBERT EINSTEIN: Things should be made as simple as possible, but not any simpler.

Theodore A. HaighConfederated Tribes of the Umatilla Indian

ReservationEnvironmental Science & Technology Program

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Objectives Types of models

What choices do you have? Model input data

What needs to go into your chosen model? Model output data

What comes out of your chosen model?

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Modeling

Data Current Accurate Precise Comprehensive

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What data?

Meteorological Demographic

Lifestyles Accurate

for local population

All factors accounted for

Topographic Local Regional

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Models & Testing

Theory Limitations of

the model Constraints

Reality You don’t

know unless you test

Agreement?

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Common Sense Does it make sense?

Do predicted quantities have believable values?

Predictions How long will the

contamination persist? How far will it spread? How much is present?

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Agreement Can this model

be verified? In-house check Check with

another group You run model

with similar data Get similar

results? Errors can be

identified and remedied

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What’s IN/NOT IN the Model Weather data

Wind Direction and

speed Historical record

long enough Rain Snow Humidity Temperature

Terrain data Health effects Sources Urban effects Chemistry

Kinetics Photochemistry Additional

sources

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Predicting Measurements agree

with model? Values from the

model seem okay? Level of uncertainty

with predictions Estimates of

dispersion Conservative Liberal

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Alternative Methods

Always more than one way to do Anything

Types of models Physical Numerical Empirical Dispersion

Gaussian Eulerian Lagrangian

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Model Limitations

Physical Stability difficult to

simulate Scale effects not well

known Vertical & horizontal

turbulence damped by walls

Measurement is tricky

Numerical Assume theory is

well defined Require significant

CPU resources and time

Little validation data available

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Model Limitations (cont.) Empirical models

Based upon analysis of source, meteorological, and air quality data

Gaussian dispersion model Mathematical

expression using Gaussian distributions to relate emissions of pollutants to ambient concentrations

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Conclusions Identify how and what you want to model Gather required input data

Verify data Run model Verify output Refine input data (as needed to correct errors) Models are not static

Refinements always being incorporated Input data sources being updated New information being uncovered