The Impact of Nudging in the Meteorological Model for Retrospective Air Quality Simulations. Part I: Evaluation against National Observation Networks

Tanya L. Otte Atmospheric Sciences Modeling Division, NOAA/Air Resources Laboratory,* Research Triangle Park, North Carolina

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Abstract

It is common practice to use Newtonian relaxation, or nudging, throughout meteorological model simulations to create “dynamic analyses” that provide the characterization of the meteorological conditions for retrospective air quality model simulations. Given the impact that meteorological conditions have on air quality simulations, it has been assumed that the resultant air quality simulations would be more skillful by using dynamic analyses rather than meteorological forecasts to characterize the meteorological conditions, and that the statistical trends in the meteorological model fields are also reflected in the air quality model. This article, which is the first of two parts, demonstrates the impact of nudging in the meteorological model on retrospective air quality model simulations. Here, meteorological simulations are generated by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) using both the traditional dynamic analysis approach and using forecasts for a summertime period. The resultant fields are then used to characterize the meteorological conditions for emissions processing and air quality simulations using the Community Multiscale Air Quality (CMAQ) Modeling System. As expected, on average, the near-surface meteorological fields show a significant degradation over time in the forecasts (when nudging is not used), while the dynamic analyses maintain nearly constant statistical scores in time. The use of nudged MM5 fields in CMAQ generally results in better skill scores for daily maximum 1-h ozone mixing ratio simulations. On average, the skill of the daily maximum 1-h ozone simulation deteriorates significantly over time when nonnudged MM5 fields are used in CMAQ. The daily maximum 1-h ozone mixing ratio also degrades over time in the CMAQ simulation that uses MM5 dynamic analyses, although to a much lesser degree, despite no aggregate loss of skill over time in the dynamic analyses themselves. These results affirm the advantage of using nudging in MM5 to create the meteorological characterization for CMAQ for retrospective simulations, and it is shown that MM5-based dynamic analyses are robust at the surface throughout 5.5-day simulations.

* In partnership with the U.S. EPA National Exposure Research Laboratory

Corresponding author address: Tanya L. Otte, NOAA/Atmospheric Sciences Modeling Division, U.S. EPA Mail Drop E243-03, 109 T. W. Alexander Dr., Research Triangle Park, NC 27711. Email: tanya.otte@noaa.gov

Abstract

It is common practice to use Newtonian relaxation, or nudging, throughout meteorological model simulations to create “dynamic analyses” that provide the characterization of the meteorological conditions for retrospective air quality model simulations. Given the impact that meteorological conditions have on air quality simulations, it has been assumed that the resultant air quality simulations would be more skillful by using dynamic analyses rather than meteorological forecasts to characterize the meteorological conditions, and that the statistical trends in the meteorological model fields are also reflected in the air quality model. This article, which is the first of two parts, demonstrates the impact of nudging in the meteorological model on retrospective air quality model simulations. Here, meteorological simulations are generated by the fifth-generation Pennsylvania State University–National Center for Atmospheric Research Mesoscale Model (MM5) using both the traditional dynamic analysis approach and using forecasts for a summertime period. The resultant fields are then used to characterize the meteorological conditions for emissions processing and air quality simulations using the Community Multiscale Air Quality (CMAQ) Modeling System. As expected, on average, the near-surface meteorological fields show a significant degradation over time in the forecasts (when nudging is not used), while the dynamic analyses maintain nearly constant statistical scores in time. The use of nudged MM5 fields in CMAQ generally results in better skill scores for daily maximum 1-h ozone mixing ratio simulations. On average, the skill of the daily maximum 1-h ozone simulation deteriorates significantly over time when nonnudged MM5 fields are used in CMAQ. The daily maximum 1-h ozone mixing ratio also degrades over time in the CMAQ simulation that uses MM5 dynamic analyses, although to a much lesser degree, despite no aggregate loss of skill over time in the dynamic analyses themselves. These results affirm the advantage of using nudging in MM5 to create the meteorological characterization for CMAQ for retrospective simulations, and it is shown that MM5-based dynamic analyses are robust at the surface throughout 5.5-day simulations.

* In partnership with the U.S. EPA National Exposure Research Laboratory

Corresponding author address: Tanya L. Otte, NOAA/Atmospheric Sciences Modeling Division, U.S. EPA Mail Drop E243-03, 109 T. W. Alexander Dr., Research Triangle Park, NC 27711. Email: tanya.otte@noaa.gov

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  • Barna, M., and B. Lamb, 2000: Improving ozone modeling in regions of complex terrain using observational nudging in a prognostic meteorological model. Atmos. Environ., 34 , 48894906.

    • Search Google Scholar
    • Export Citation
  • Bey, I., and Coauthors, 2001: Global modeling of tropospheric chemistry with assimilated meteorology: Model description and evaluation. J. Geophys. Res., 106 , 2307323096.

    • Search Google Scholar
    • Export Citation
  • Binkowski, F. S., and S. J. Roselle, 2003: Models-3 Community Multiscale Air Quality (CMAQ) model aerosol component. 1. Model description. J. Geophys. Res., 108 .4183, doi:10.1029/2001JD001409.

    • Search Google Scholar
    • Export Citation
  • Biswas, J., and S. T. Rao, 2001: Uncertainties in episodic ozone modeling stemming from uncertainties in meteorological fields. J. Appl. Meteor., 40 , 117136.

    • Search Google Scholar
    • Export Citation
  • Black, T., 1994: The new NMC mesoscale Eta model: Description and forecast examples. Wea. Forecasting, 9 , 265278.

  • Byun, D., and K. L. Schere, 2006: Review of the governing equations, computational algorithms, and other components of the Models-3 Community Multiscale Air Quality (CMAQ) modeling system. Appl. Mech. Rev., 59 , 5177.

    • Search Google Scholar
    • Export Citation
  • Civerolo, K., and Coauthors, 2007: Estimating the effects of increased urbanization on surface meteorology and ozone concentrations in the New York City metropolitan region. Atmos. Environ., 41 , 18031818.

    • Search Google Scholar
    • Export Citation
  • Eder, B., and S. Yu, 2006: A performance evaluation of the 2004 release of Models-3 CMAQ. Atmos. Environ., 40 , 48114824.

  • Environmental Protection Agency, 2003: User’s guide to MOBILE6.1 and MOBILE6.2: Mobile source emission factor model. EPA Rep. EPA420-R-03-010, 262 pp. [Available online at http://www.epa.gov/otaq/models/mobile6/420r03010.pdf.].

  • Gilliam, R. C., K. W. Appel, and S. Phillips, 2005: The atmospheric model evaluation tool: Meteorology module. Proc. Fourth Annual Models-3 Users’ Conf., Chapel Hill, NC, Community Modeling and Analysis System, 6.1. [Available online at http://www.cmascenter.org/conference/2005/abstracts/6_1.pdf.].

  • Gilliam, R. C., C. Hogrefe, and S. T. Rao, 2006: New methods for evaluating meteorological models used in air quality applications. Atmos. Environ., 40 , 50735086.

    • Search Google Scholar
    • Export Citation
  • Grell, G. A., J. Dudhia, and D. R. Stauffer, 1994: A description of the fifth-generation Penn State/NCAR mesoscale model (MM5). NCAR Tech. Note NCAR/TN-398+STR, 138 pp.

  • Hogrefe, C., S. T. Rao, P. Kasibhatla, W. Hao, G. Sistla, R. Mathur, and J. McHenry, 2001: Evaluating the performance of regional-scale photochemical modeling systems. Part II— Ozone predictions. Atmos. Environ., 35 , 41754188.

    • Search Google Scholar
    • Export Citation
  • Hogrefe, C., P. S. Porter, E. Gego, A. Gilliland, R. Gilliam, J. Swall, J. Irwin, and S. T. Rao, 2006: Temporal features in observed and simulated meteorology and air quality over the eastern United States. Atmos. Environ., 40 , 50415055.

    • Search Google Scholar
    • Export Citation
  • Houyoux, M. R., J. M. Vukovich, C. J. Coats Jr., N. M. Wheeler, and P. S. Kasibhatla, 2000: Emission inventory development and processing for the Seasonal Model for Regional Air Quality (SMRAQ) project. J. Geophys. Res., 105 , 90799090.

    • Search Google Scholar
    • Export Citation
  • Kain, J. S., 2004: The Kain–Fritsch convective parameterization: An update. J. Appl. Meteor., 43 , 170181.

  • Mlawer, E. J., S. J. Taubman, P. D. Brown, M. J. Iacono, and S. A. Clough, 1997: Radiative transfer for inhomogeneous atmospheres: RRTM, a validated correlated-k model for the longwave. J. Geophys. Res., 102 , 1666316682.

    • Search Google Scholar
    • Export Citation
  • Okamoto, K., and J. Derber, 2006: Assimilation of SSM/I radiances in the NCEP global data assimilation system. Mon. Wea. Rev., 134 , 26122631.

    • Search Google Scholar
    • Export Citation
  • Otte, T. L., 2008: The impact of nudging in the meteorological model for retrospective air quality simulations. Part II: Evaluating collocated meteorological and air quality observations. J. Appl. Meteor. Climatol., 47 , 18681887.

    • Search Google Scholar
    • Export Citation
  • Otte, T. L., A. Lacser, S. Dupont, and J. K. S. Ching, 2004: Implementation of an urban canopy parameterization in a mesoscale meteorological model. J. Appl. Meteor., 43 , 16481665.

    • Search Google Scholar
    • Export Citation
  • Otte, T. L., and Coauthors, 2005: Linking the Eta Model with the Community Multiscale Air Quality (CMAQ) modeling system to build a national air quality forecasting system. Wea. Forecasting, 20 , 367384.

    • Search Google Scholar
    • Export Citation
  • Pierce, T., C. Geron, L. Bender, R. Dennis, G. Tonnesen, and A. Guenther, 1998: Influence of increased isoprene emissions on regional ozone modeling. J. Geophys. Res., 103 , 2561125629.

    • Search Google Scholar
    • Export Citation
  • Pleim, J. E., 2007: A combined local and nonlocal closure model for the atmospheric boundary layer. Part I: Model description and testing. J. Appl. Meteor. Climatol., 46 , 13831395.

    • Search Google Scholar
    • Export Citation
  • Pleim, J. E., and J. S. Chang, 1992: A non-local closure model for vertical mixing in the convective boundary layer. Atmos. Environ., 26A , 965981.

    • Search Google Scholar
    • Export Citation
  • Pleim, J. E., and A. Xiu, 2003: Development of a land surface model. Part II: Data assimilation. J. Appl. Meteor., 42 , 18111822.

  • Pleim, J. E., A. Xiu, P. L. Finkelstein, and T. L. Otte, 2001: A coupled land-surface and dry deposition model and comparison to field measurements of surface heat, moisture, and ozone fluxes. Water Air Soil Pollut. Focus, 1 , 243252.

    • Search Google Scholar
    • Export Citation
  • Rao, S. T., I. G. Zurbenko, R. Neagu, P. S. Porter, J. Y. Ku, and R. F. Henry, 1997: Space and time scales in ambient ozone data. Bull. Amer. Meteor. Soc., 78 , 21532166.

    • Search Google Scholar
    • Export Citation
  • Reisner, J., R. M. Rasmussen, and R. T. Bruintjes, 1998: Explicit forecasting of supercooled liquid water in winter storms using the MM5 mesoscale model. Quart. J. Roy. Meteor. Soc., 124B , 10711107.

    • Search Google Scholar
    • Export Citation
  • Russell, A., and R. Dennis, 2000: NARSTO critical review of photochemical models and modeling. Atmos. Environ., 34 , 22832324.

  • Sarwar, G., D. Luecken, G. Yarwood, G. Z. Whitten, and W. P. Carter, 2008: Impact of an updated carbon bond mechanism on predictions from the CMAQ modeling system: Preliminary assessment. J. Appl. Meteor. Climatol., 47 , 314.

    • Search Google Scholar
    • Export Citation
  • Schwede, D., G. Pouliot, and T. Pierce, 2005: Changes to the Biogenics Emissions Inventory System version 3 (BEIS3). Proc. Fourth Annual Models-3 Users’ Conf., Chapel Hill, NC, Community Modeling and Analysis System, 2.7. [Available online at http://www.cmascenter.org/conference/2005/abstracts/2_7.pdf.].

  • Seaman, N. L., 2000: Meteorological modeling for air-quality assessments. Atmos. Environ., 34 , 22312259.

  • Seaman, N. L., D. R. Stauffer, and A. M. Lario-Gibbs, 1995: A multiscale four-dimensional data assimilation system applied in the San Joaquin Valley during SARMAP. Part I: Modeling design and basic performance characteristics. J. Appl. Meteor., 34 , 17391761.

    • Search Google Scholar
    • Export Citation
  • Sistla, G., W. Hao, J-Y. Ku, G. Kallos, K. Zhang, H. Mao, and S. T. Rao, 2001: An operational evaluation of two regional-scale ozone air quality modeling systems over the eastern United States. Bull. Amer. Meteor. Soc., 82 , 945964.

    • Search Google Scholar
    • Export Citation
  • Stauffer, D. R., and N. L. Seaman, 1990: Use of four-dimensional data assimilation in a limited-area mesoscale model. Part I: Experiments with synoptic-scale data. Mon. Wea. Rev., 118 , 12501277.

    • Search Google Scholar
    • Export Citation
  • Stauffer, D. R., and N. L. Seaman, 1994: Multiscale four-dimensional data assimilation. J. Appl. Meteor., 33 , 416434.

  • Stauffer, D. R., N. L. Seaman, and F. S. Binkowski, 1991: Use of four-dimensional data assimilation in a limited-area mesoscale model. Part II: Effects of data assimilation within the planetary boundary layer. Mon. Wea. Rev., 119 , 734754.

    • Search Google Scholar
    • Export Citation
  • Stauffer, D. R., N. L. Seaman, T. T. Warner, and A. M. Lario, 1993: Application of an atmospheric simulation model to diagnose air-pollution transport in the Grand Canyon region of Arizona. Chem. Eng. Comm., 121 , 925.

    • Search Google Scholar
    • Export Citation
  • Tanrikulu, S., D. R. Stauffer, N. L. Seaman, and A. J. Ranzieri, 2000: A field-coherence technique for meteorological field-program design for air quality studies. Part II: Evaluation in the San Joaquin Valley. J. Appl. Meteor., 39 , 317334.

    • Search Google Scholar
    • Export Citation
  • Umeda, T., and P. T. Martien, 2002: Evaluation of a data assimilation technique for a mesoscale meteorological model used for air quality modeling. J. Appl. Meteor., 41 , 1229.

    • Search Google Scholar
    • Export Citation
  • Willmott, C. J., 1982: Some comments on the evaluation of model performance. Bull. Amer. Meteor. Soc., 63 , 13091313.

  • Xiu, A., and J. E. Pleim, 2001: Development of a land surface model. Part I: Application in a mesoscale meteorology model. J. Appl. Meteor., 40 , 192209.

    • Search Google Scholar
    • Export Citation
  • Yarwood, G., S. Rao, M. Yocke, and G. Whitten, 2005: Updates to the carbon bond chemical mechanism: CB05. Final report to the U.S. EPA, RT-0400675, 246 pp. [Available online at http://www.camx.com/publ/pdfs/CB05_Final_Report_120805.pdf.].

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