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Pattern-Based Evaluation of Coupled Meteorological and Air Quality Models.

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Title: Pattern-Based Evaluation of Coupled Meteorological and Air Quality Models.
Author: Beaver, Scott; Tanrikulu, Saffet; Palazoglu, Ahmet; Singh, Angadh; Soong, Su-Tzai; Jia, Yiqin; Tran, Cuong; Ainslie, Bruce; Steyn, Douw G.
Issue Date: 2010-10
Publicly Available in cIRcle 2011-04-20
Publisher American Meteorological Society
Citation: Beaver, Scott, Tanrikulu, Saffet, Palazoglu, Ahmet, Singh, Angadh, Soong, Su-Tzai Jia, Yiqin, Tran, Cuong, Ainslie, Bruce, Steyn, Douw G. 2010. Pattern-Based Evaluation of Coupled Meteorological and Air Quality Models. Journal of Applied Meteorology and Climatology 49(10) 2077-2091 http://dx.doi.org/10.1175/2010JAMC2471.1
Abstract: 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. Copyright 2010 American Meteorological Society (AMS). Permission to use figures, tables, and brief excerpts from this work in scientific and educational works is hereby granted provided that the source is acknowledged. Any use of material in this work that is determined to be “fair use” under Section 107 of the U.S. Copyright Act or that satisfies the conditions specified in Section 108 of the U.S. Copyright Act (17 USC §108, as revised by P.L. 94-553) does not require the AMS’s permission. Republication, systematic reproduction, posting in electronic form, such as on a web site or in a searchable database, or other uses of this material, except as exempted by the above statement, requires written permission or a license from the AMS. Additional details are provided in the AMS Copyright Policy, available on the AMS Web site located at (http://www.ametsoc.org/) or from the AMS at 617-227-2425 or copyright@ametsoc.org.
Affiliation: Earth and Ocean Sciences, Dept. of (EOS), Dept of
URI: http://hdl.handle.net/2429/33851
Peer Review Status: Reviewed
Scholarly Level: Faculty

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