An objective comparison of CMAQ and REMSAD performances

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  • Atmospheric Environment 40 (200

    ripe

    Edith Gegoa,, P. Steven Porterb, Christian Hogrefec, John S. Irwind

    simulation/observation pairs from ten geographic regions and 12 seasons (months). Following the application of the

    facilitate decisions about the most suitable alter-natives.

    ARTICLE IN PRESS

    Corresponding author. Tel.: +1 208 523 5873;

    Two of the most prominent modeling systems are

    the community multiscale air quality (CMAQ)

    1352-2310/$ - see front matter r 2006 Elsevier Ltd. All rights reserved.

    doi:10.1016/j.atmosenv.2005.12.045

    fax: +1208 282 7975.

    E-mail address: e.gego@onewest.net (E. Gego).Wilcoxon matched-pair signed rank test, we conclude that CMAQ is more skillful than REMSAD for simulation of

    aerosol sulfate. Simulations of particulate nitrate concentrations by CMAQ and REMSAD can seldom be differentiated,

    leading to the conclusion that both models perform equally for this pollutant specie.

    r 2006 Elsevier Ltd. All rights reserved.

    Keywords: Aerosol sulfate; Aerosol nitrate; Wilcoxon signed rank test; Evaluation metric; Photochemical model

    1. Introduction

    Models are the principal tools used by govern-mental agencies to develop emission reduction

    strategies aimed at achieving safe and thereforeadmissible air quality. Models are indeed the onlytool that allows testing of the impact of differentreduction strategies on air quality and, therefore,bUniversity of Idaho, 1776 Science Center Drive, Idaho Falls, ID 83402, USAcAtmospheric Sciences Research Center, University at Albany, ES 351, State University of New York, Albany,

    NY 12222, USAd1900 Pony Run Road, Raleigh, NC 27615-7415, USA

    Received 8 February 2005; received in revised form 17 October 2005; accepted 22 December 2005

    Abstract

    Photochemical air quality modeling systems are the primary tools used in regulatory applications to assess the impact of

    different emission reduction strategies aimed at reducing air pollutant concentrations to levels considered safe for public

    health. Two such modeling systems are the community multiscale air quality (CMAQ) model and the regional modeling

    system for aerosols and deposition (REMSAD). To facilitate their inter-comparison, the United States Environmental

    Protection Agency performed simulations of air quality over the contiguous United States during year 2001 (horizontal

    grid cell size of 36 36 km) with CMAQ and REMSAD driven by identical emission and meteorological elds. Here, wecompare the abilities of CMAQ and REMSAD to reproduce measured aerosol nitrate and sulfate concentrations. Model

    estimates are compared to observations reported by the interagency monitoring of protected visual environment

    (IMPROVE) and the clean air status and trend network (CASTNet). Root mean squared errors are calculated fora308 Evergreen Drive, Idaho Falls, ID 83401, USAAn objective compaREMSAD6) 49204934

    son of CMAQ andrformances

    www.elsevier.com/locate/atmosenv

  • model (Byun and Ching, 1999) and the regionalmodeling system for aerosols and deposition (RE-MSAD) (ICF Consulting, 2002). To promotemodel-to-model comparison of these two modelingsystems, the United States Environmental Protec-tion Agency (US EPA) recently used CMAQ andREMSAD to simulate air quality over the contig-uous US during year 2001, with both modelsresponding to identical inputs (meteorology, emis-sions, etc.). Our objective is to use the results ofthese simulations to compare the ability of RE-MSAD and CMAQ to reproduce measured aerosolnitrate and sulfate concentrations.Our evaluation of the respective strengths and

    weaknesses of CMAQ and REMSAD relies oncalculation of the root mean squared errors(RMSE) between model estimates and correspond-ing observations. In an effort to unveil the areas and

    elds. CMAQ and REMSAD are three-dimensionalEulerian air quality modeling systems designedto simulate the chemistry, transport, and depositionof airborne pollutants. The two systems mostlydiffer from each other by their modeling ofchemistry. Details about CMAQ and REMSADcan be found at http://www.epa.gov/asmdnerl/models3/doc/science/ and http://remsad.saintl.com,respectively.The meteorological elds used in CMAQ and

    REMSAD were produced by MM5, the fthgeneration Penn State University (PSU)/ NationalCenter for Atmospheric Research (NCAR) mesos-cale model (Grell et al., 1994). MM5 (version 5) wasused to reconstruct meteorology over the continen-tal United States from 1 January 2001 to 31December 2001 with a horizontal resolution of36 km. Vertically, the domain comprises 34 layers

    ARTICLE IN PRESS

    Reg

    E. Gego et al. / Atmospheric Environment 40 (2006) 49204934 4921time periods where the quality of CMAQ andREMSAD estimates signicantly differ from eachother, simulation results were organized into tengeographical areas and monthly periods for calcula-tion of the evaluation metric (RMSE). Objectivity inour assessment is attained by submitting matchingsets of the evaluation metric characterizing CMAQand REMSAD respectively, to a statistical test ofcomparison of means.

    2. Models

    Air quality estimates were produced by CMAQ(2004 release version) and REMSAD (version 7.6)using nearly identical meteorological and emission

    CASTNet observation siteIMPROVE observation site

    Region III

    Region IV

    Region VRegion I

    Region IIFig. 1. Regions identied in the contiguous US and locawith the surface layer approximately 50m deep.Topographic information was developed using theNCAR and the United States Geological Survey(USGS) terrain databases. Vegetation type and landuse information was developed using the NCAR/PSU databases provided with MM5. Initial andboundary conditions were extracted from theNCAR ETA reanalysis archives. An analysis-nud-ging technique was used to nudge predictions(winds, temperature and the mixing ratio) towardsurface and aloft observations. Thermodynamicvariables were not nudged within the boundarylayer. The model was run with a 51/2 day windowand a restart at 12:00 GMT (Greenwich mean time)every fth day. Further details about the MM5

    Region IX

    Region VIII

    Region VI

    ion VII Region Xtion of the observation sites included in the study.

  • setting, such as the physical options utilized, areavailable in McNally (2003).The MM5 elds were processed by the meteorol-

    ogy-chemistry interface preprocessor (MCIP) ver-sion 2.2 to provide linkage to the air quality models.See details about MCIP at http://www.epa.gov/asmdnerl/models3/doc/science/chap12.pdf.Anthropogenic emission elds from xed sources

    were obtained with the sparse matrix operatorkernel emission model (SMOKE) (Carolina Envir-onmental Programs, 2003) processing the US EPANational Emissions Inventory for 2001. Emissions

    from mobile sources were prepared with theMOBILE 6 module (US EPA, 2003); biogenicemissions were estimated with BEIS3.12 (http://www.epa.gov/asmdnerl/biogen.html) in conjunctionwith the MM5-derived meteorological estimates.Model-ready emission data with a horizontal gridsize of 36 km 36 km were created from theemission elds by the emission-chemistry interfaceprocessor (ECIP).

    3. Observations

    Observations used to judge model performanceare aerosol sulfate and nitrate concentrationsreported by the interagency monitoring of protectedvisual environment (IMPROVE) network and theclean air status and trend network (CASTNet). TheIMPROVE network was designed to supervise airquality in pristine environments whereas CASTNetsites are located mostly in rural, not necessarilypristine, situations. In the western United States,though, newly added CASTNet sites are often

    ARTICLE IN PRESS

    Table 1

    Number of IMPROVE and CASTNet sites in each region

    Region Total

    I II III IV V VI VII VIII IX X

    CASTNet 3 7 3 8 1 14 12 6 14 5 73

    IMPROVE 9 12 12 24 5 4 6 4 5 5 86

    Table 2

    Comparison of CMAQ and REMSAD estimates of sulfate concentration to observations at IMPROVE sites

    Month Region

    I II III IV V VI VII VIII IX X

    RMSE (mgm3) characterizing CMAQ estimates, by month and regionJan. 0.71 0.35 0.38 0.61 0.76Feb. 0.38 0.29 0.41 0.34 0.88Mar. 0.30 0.76 0.31 0.49 0.68Apr. 0.47 0.54 0.35 0.44 0.72May 0.40 0.96 0.34 0.43 0.61Jun. 0.51 1.08 0.26 0.56 0.59Jul. 0.87 1.26 0.20 0.61 0.65Aug. 0.72 1.21 0.25 0.55 0.52Sep. 0.47 0.99 0.25 0.45 0.48Oct. 0.27 1.11 0.23 0.39 0.43

    0.54

    0.55

    d reg

    0.95

    0.93

    0.73

    0.90

    0.47

    0.49

    0.68

    0.61

    0.47

    0.39

    0.41

    0.59

    E. Gego et al. / Atmospheric Environment 40 (2006) 492049344922Nov. 0.30 0.52 0.26 0.84Dec. 0.24 0.34 0.24 0.32

    RMSE (mgm3) characterizing REMSAD estimates, by month anJan. 0.74 0.34 0.38 0.65Feb. 0.36 0.28 0.35 0.38Mar. 0.35 0.75 0.33 0.43Apr. 0.38 0.57 0.41 0.58May 0.41 0.86 0.40 0.44Jun. 0.52 1.00 0.27 0.63Jul. 0.91 1.21 0.23 0.70Aug. 0.79 1.13 0.31 0.62Sep. 0.43 0.94 0.26 0.57Oct. 0.28 0.99 0.22 0.54Nov. 0.32 0.49 0.29 0.96Dec. 0.24 0.35 0.19 0.180.59 1.39 0.83 1.31 1.31

    0.48 3.83 0.79 0.97 1.19

    0.71 0.98 0.62 1.34 1.65

    1.10 1.30 0.72 1.24 1.36

    1.15 1.87 1.47 3.09 2.11

    1.44 2.17 1.51 2.65 2.24

    1.63 2.61 1.57 1.89 2.09

    1.16 2.91 1.78 9.59 2.22

    0.92 2.24 1.71 2.61 1.78

    0.62 2.13 1.32 1.91 1.50

    1.10 1.45 1.25 1.03 1.12

    0.64 0.92 1.10 2.13 1.31

    ion

    0.64 1.59 1.07 1.30 1.15

    0.50 3.80 0.65 1.05 0.96

    0.89 0.97 0.78 1.31 1.56

    1.00 2.41 1.49 1.61 1.42

    0.52 2.26 1.87 3.48 2.40

    1.38 2.95 2.56 3.34 1.86

    1.75 4.66 3.55 2.21 2.58

    1.23 3.92 2.25 11.00 2.86

    1.35 2.24 1.23 2.88 1.35

    0.42 0.75 1.55 0.83 0.76

    0.84 0.95 1.09 1.09 1.17

    0.64 0.79 1.04 3.02 1