Use of Data Assimilation: As noted above, the use of FDDA helps to keep the model
predictions from widely diverging from what was actually observed to occur at a particular point
in time/space. However, if used improperly, FDDA can significantly degrade overall model
performance and introduce computational artifacts (Tesche and McNally, 2001). Inappropriately
strong nudging coefficients can distort the magnitude of the physical terms in the underlying
atmospheric thermodynamic equations and result in "patchwork" meteorological fields with
strong gradients between near-site grid cells and the remainder of the grid. Additionally, if
specific meteorological features are expected to be important for predicting the location and
amount of pollution formed, based on an area's conceptual model, then the meteorological
modeling should be set up to ensure that FDDA does not prevent the model from forming these
features (e.g. nocturnal low-level wind jets). In general, analysis nudging strengths should be no
greater than 1.0 x 10-4 for winds and temperatures and 1.0 x 10-5 for humidity. In the case of
observation nudging (i.e., FDDA based on individual observations as opposed to analysis fields),
it is recommended that the resultant meteorological fields be examined to ensure that the results
over the entire domain are still consistent. Further, based on past experience, we recommend
against using FDDA below the boundary layer for thermodynamic variables like temperature and
humidity because of the potential for spurious convection. If the dynamic model is applied
without FDDA, it is suggested that the simulation durations be shorter than 24 hours.


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