In Part I of this paper, a field-coherence technique (FCT) was developed to provide objective guidance for cost-effective siting of meteorological observations on the mesoscale for air quality applications. The FCT is evaluated here in Part II using the Fifth-Generation Pennsylvania State University-National Center for Atmospheric Research Mesoscale Model (MM5) and the rich datasets of the San Joaquin Valley Air Quality Study (SJVAQS) and the Atmospheric Utility Signatures, Prediction, and Experiments (AUSPEX), which were combined in the SJVAQS-AUSPEX Regional Modeling Adaptation Project (SARMAP). The FCT is used to define a data-starvation Observing System Experiment in which the size of the SARMAP meteorological dataset used for four-dimensional data assimilation (FDDA) in the mesoscale model is reduced optimally by about half. The meteorological conditions for the 2-7 August 1990 period are simulated using the FCT-based reduced-data distribution (partial FDDA), all available data (full FDDA), and no data from the study (no FDDA). The three meteorological simulations then are used as input to the SARMAP Air Quality Model to simulate the 3-6 August 1990 ozone episode in the San Joaquin Valley. It was demonstrated that the MM5 simulation using partial FDDA produces results very similar to those obtained from the full FDDA, and the two FDDA-assisted meteorological datasets are significantly more accurate than that obtained with no FDDA for the 5-day period. The results obtained from the three associated air quality simulations were compared with each other and with ozone and precursor measurements. It was found that the partial-FDDA meteorological input produces air quality model results very similar to those obtained from the full-FDDA input and closer to the observations than results from input based on no FDDA. These findings confirm that the FCT can provide guidance for a more cost-effective field-program design in terms of both the meteorological behavior and the air quality based on that meteorological behavior.
|Original language||English (US)|
|Number of pages||18|
|Journal||Journal of Applied Meteorology|
|State||Published - Mar 2000|
All Science Journal Classification (ASJC) codes
- Atmospheric Science