Pattern-Based Evaluation of Coupled Meteorological and Air Quality Models

A novel pattern-based model evaluation technique is proposed and demonstrated for air quality models (AQMs) driven by meteorological model (MM) output. The evaluation technique is applied directly to the MM output; however, it is ultimately used to gauge the performance of the driven AQM. This evalu...

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Bibliographic Details
Published in:	Journal of applied meteorology and climatology 2010-10, Vol.49 (10), p.2077-2091
Main Authors:	Beaver, Scott, Tanrikulu, Saffet, Palazoglu, Ahmet, Singh, Angadh, Soong, Su-Tzai, Jia, Yiqin, Tran, Cuong, Ainslie, Bruce, Steyn, Douw G.
Format:	Article
Language:	eng
Subjects:	Air Air pollution Air quality Air quality assessment Air quality models Algorithms Atmospheric models Atmospherics Bias Classification Cluster analysis Clustering Earth, ocean, space Empirical analysis Evaluation Exact sciences and technology External geophysics Labels Measurement Meteorology Modeling Modelling Orthogonal functions Outdoor air quality Ozone Particulate matter Performance evaluation Pollutants Simulation Simulations Statistical analysis Studies Surface wind Suspended particulate matter Time series Weather Weather patterns Wind Wind fields Wind velocity
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Summary:	A novel pattern-based model evaluation technique is proposed and demonstrated for air quality models (AQMs) driven by meteorological model (MM) output. The evaluation technique is applied directly to the MM output; however, it is ultimately used to gauge the performance of the driven AQM. This evaluation of AQM performance based on MM performance is a major advance over traditional evaluation methods. First, meteorological cluster analysis is used to assign the days of a historical measurement period among a small number of weather patterns having distinct air quality characteristics. The clustering algorithm groups days sharing similar empirical orthogonal function (EOF) representations of their measurements. In this study, EOF analysis is used to extract space–time patterns in the surface wind field reflecting both synoptic and mesoscale influences. Second, simulated wind fields are classified among the determined weather patterns using the measurement-derived EOFs. For a given period, the level of agreement between the observation-based clustering labels and the simulation-based classification labels is used to assess the validity of the simulation results. Mismatches occurring between the two sets of labels for a given period imply inaccurately simulated conditions. Moreover, the specific nature of a mismatch can help to diagnose the downstream effects of improperly simulated meteorological fields on AQM performance. This pattern-based model evaluation technique was applied to extended simulations of fine particulate matter (PM2.5) covering two winter seasons for the San Francisco Bay Area of California.
ISSN:	1558-8424 1558-8432