Model Evaluation and Assessment ALBERT EINSTEINALBERT EINSTEIN: Things should be made as simple as possible, but not any simpler. Theodore A. Haigh Confederated Tribes of the Umatilla Indian Reservation Environmental Science & Technology Program

2 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?

3 Modeling Data Current Accurate Precise Comprehensive

4 What data? Meteorological Demographic Lifestyles Accurate for local population All factors accounted for Topographic Local Regional

5 Models & Testing Theory Limitations of the model Constraints Reality You don’t know unless you test Agreement?

6 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?

7 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

8 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

9 Predicting Measurements agree with model? Values from the model seem okay? Level of uncertainty with predictions Estimates of dispersion Conservative Liberal

10 Alternative Methods Always more than one way to do Anything Types of models Physical Numerical Empirical Dispersion Gaussian Eulerian Lagrangian

11 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

12 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

13 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