Incorporating principal component analysis into air quality model evaluation

Abstract The efficacy of standard air quality model evaluation techniques is becoming compromised as the simulation periods continue to lengthen in response to ever increasing computing capacity. Accordingly, the purpose of this paper is to demonstrate a statistical approach called Principal Component Analysis (PCA) with the intent of motivating its use by the evaluation community. One of the main objectives of PCA is to identify, through data reduction, the recurring and independent modes of variations (or signals) within a very large dataset, thereby summarizing the essential information of that dataset so that meaningful and descriptive conclusions can be made. In this demonstration, PCA is applied to a simple evaluation metric – the model bias associated with EPA's Community Multi-scale Air Quality (CMAQ) model when compared to weekly observations of sulfate (SO 4 2− ) and ammonium (NH 4 + ) ambient air concentrations measured by the Clean Air Status and Trends Network (CASTNet). The advantages of using this technique are demonstrated as it identifies strong and systematic patterns of CMAQ model bias across a myriad of spatial and temporal scales that are neither constrained to geopolitical boundaries nor monthly/seasonal time periods (a limitation of many current studies). The technique also identifies locations (station–grid cell pairs) that are used as indicators for a more thorough diagnostic evaluation thereby hastening and facilitating understanding of the probable mechanisms responsible for the unique behavior among bias regimes. A sampling of results indicates that biases are still prevalent in both SO 4 2− and NH 4 + simulations that can be attributed to either: 1) cloud processes in the meteorological model utilized by CMAQ, which are found to overestimated convective clouds and precipitation, while underestimating larger-scale resolved clouds that are less likely to precipitate, and 2) biases associated with Midwest NH 3 emissions which may be partially ameliorated using the bi-directional NH 3 exchange option in CMAQ.

[1]  I. Jolliffe.  Principal Component Analysis , 2002 .

[2]  K. Wyat Appel,et al.  Evaluation of the Community Multiscale Air Quality (CMAQ) model version 4.5 : Sensitivities impacting model performance Part I-Ozone , 2007 .

[3]  R. Martin,et al.  Application of empirical orthogonal functions to evaluate ozone simulations with regional and global models , 2003 .

[4]  Philippe Thunis,et al.  Evaluation and intercomparison of Ozone and PM10 simulations by several chemistry transport models over four European cities within the CityDelta project , 2007 .

[5]  D. Byun,et al.  Review of the Governing Equations, Computational Algorithms, and Other Components of the Models-3 Community Multiscale Air Quality (CMAQ) Modeling System , 2006 .

[6]  R. Preisendorfer,et al.  Principal Component Analysis in Meteorology and Oceanography , 1988 .

[7]  G. Grell,et al.  A description of the fifth-generation Penn State/NCAR Mesoscale Model (MM5) , 1994 .

[8]  M. C. Dodge,et al.  A photochemical kinetics mechanism for urban and regional scale computer modeling , 1989 .

[9]  J. E. Jackson.  A User's Guide to Principal Components , 1991 .

[10]  Evaluation of an 18-year CMAQ simulation: Seasonal variations and long-term temporal changes in sulfate and nitrate , 2010 .

[11]  Shaocai Yu,et al.  Moment-based simulation of microphysical properties of sulfate aerosols in the eastern United States: Model description, evaluation, and regional analysis , 2003 .

[12]  R. Pinder,et al.  Seasonal NH3 emissions for the continental united states: Inverse model estimation and evaluation , 2006 .

[13]  E. Cooter,et al.  Linking agricultural crop management and air quality models for regional to national-scale nitrogen assessments , 2012 .

[14]  Gail S. Tonnesen,et al.  CMAQ/CAMx annual 2002 performance evaluation over the eastern US , 2006 .

[15]  R. L. Dennis,et al.  The evaluation of acid deposition models using principal component spaces , 1994 .

[16]  Shao-Meng Li,et al.  Evaluation of a comprehensive Eulerian air quality model with multiple chemical species measurements using principal component analysis , 1994 .

[17]  D. Jacob,et al.  Global modeling of tropospheric chemistry with assimilated meteorology : Model description and evaluation , 2001 .

[18]  Yuhang Wang,et al.  Diagnosis of an underestimation of summertime sulfate using the Community Multiscale Air Quality model , 2011 .

[19]  Philippe Thunis,et al.  Evaluation of long-term ozone simulations from seven regional air quality models and their ensemble , 2007 .

[20]  Shaocai Yu,et al.  A performance evaluation of the 2004 release of Models-3 CMAQ , 2006 .

[21]  Jeffrey M. Vukovich,et al.  Emission inventory development and processing for the Seasonal Model for Regional Air Quality (SMRAQ) project , 2000 .

[22]  R. Dennis,et al.  Evaluation of a regional air-quality model with bidirectional NH 3 exchange coupled to an agroecosystem model , 2012 .

[23]  Robin L. Dennis,et al.  A multi-resolution assessment of the Community Multiscale Air Quality (CMAQ) model v4.7 wet deposition estimates for 2002–2006 , 2010 .

[24]  K. Wyat Appel,et al.  Evaluation of the community multiscale air quality (CMAQ) model version 4.5: Sensitivities impacting model performance; Part II—particulate matter , 2008 .

[25]  John Irwin,et al.  A framework for evaluating regional-scale numerical photochemical modeling systems , 2010, Environmental fluid mechanics.

[26]  Jeffrey Young,et al.  Incremental testing of the Community Multiscale Air Quality (CMAQ) modeling system version 4.7 , 2009 .

[27]  J. Pleim,et al.  Development and evaluation of an ammonia bidirectional flux parameterization for air quality models , 2013 .

[28]  F. Binkowski,et al.  Models-3 community multiscale air quality (cmaq) model aerosol component , 2003 .