evaluation should be to identify and quantify the existing biases and errors in the meteorological

predictions in order to allow for an downstream assessment of how the air quality modeling

results are affected by issues associated with the meteorological data. To address both

objectives, it will be necessary to complete both an operational evaluation (i.e., quantitative,

statistical, and graphical comparisons) as well as a more phenomenological assessment (i.e.,

generally qualitative comparisons of observed features vs. their depiction in the model data).

Operational Evaluation: The operational evaluation results should focus on the values

and distributions of specific meteorological parameters as paired with and compared to observed

data. It is recommended that the observation - model matching be paired as closely as possible

in space and time. Typical statistical comparisons of the key meteorological parameters105 will

include: comparisons of the means, mean bias, mean normalized bias, mean absolute error, mean

absolute normalized error, root mean square error (systematic and unsystematic), and an index of

105 It is difficult to say which meteorological parameters will most affect any particular modeling exercise

and it will vary by parameter and location. However, in general, it is expected that following key variables should

be most closely evaluated: temperature, water vapor mixing ratio, wind speed, wind direction, clouds/radiation,

precipitation, and the depth and evolution of vertical mixing over the modeling periods.

 

 

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