Environmental Fluid Mechanics

, Volume 4, Issue 4, pp 339–365 | Cite as

A comparative study of prognostic MM5 meteorological modeling with aircraft, wind profiler, lidar, tethered balloon and RASS data over Philadelphia during a 1999 summer episode

  • Anantharaman Chandrasekar
  • C. Russell Philbrick
  • Bruce Doddridge
  • Richard Clark
  • Panos G. Georgopoulos
Article

Abstract

This study presents a comparative evaluation of the prognostic meteorological Fifth Generation NCAR Pennsylvania State University Mesoscale Model (MM5) using data from the Northeast Oxidant and Particle Study (NE-OPS) research program collected over Philadelphia, PA during a summer episode in 1999. A set of model simulations utilizing a nested grid of 36 km, 12 km and 4 km horizontal resolutions with 21 layers in the vertical direction was performed for a period of 101 h from July 15, 1999; 12 UTC to July 19, 1999; 17 UTC. The model predictions obtained with 4 km horizontal grid resolution were compared with the NE-OPS observations. Comparisons of model temperature with aircraft data revealed that the model exhibited slight underestimation as noted by previous investigators. Comparisons of model temperature with aircraft and tethered balloon data indicate that the mean absolute error varied up to 1.5 °C. The comparisons of model relative humidity with aircraft and tethered balloon indicate that the mean relative error varied from −11% to −22% for the tethered balloon and from −5% to −30% for the aircraft data. The mean relative error for water vapor mixing ratio with respect to the lidar data exhibited a negative bias consistent with the humidity bias corresponding to aircraft and tethered balloon data. The tendency of MM5 to produce estimates of very low wind speeds, especially in the early-mid afternoon hours, as noted by earlier investigators, is seen in this study also. It is indeed true that the initial fields as well as the fields utilized in the data assimilation also contribute to some of the differences between the model and observations. Studies such as these which compare the grid averaged mean state variables with observations have inherent difficulties. Despite the above limitations, the results of the present study broadly conform to the general traits of MM5 as noted by earlier investigators.

Key words

aircraft lidar MM5 NE-OPS RASS tethered balloon wind profiler 

Preview

Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.

References

  1. 1.
    Philbrick, C.R., Ryan, W.F., Clark, R.D., Doddridge, B.G., Dickerson, R.R., Koutrakis, P., Allen, G., McDow, S.R., Rao, S.T., Hopke, P.K., Eatough, D.J., Dasgupta, P.K., Tollerud, D.J., Georgopoulos, P., Kleinman, L.I., Daum, P., Nunnermacker, L., Dennis, R., Schere, K., McClenny, W., Gaffney, J., Marley, N., Coulter, R., Fast, J., Doren, C. and Mueller, P.K.: 2002, Overview of the NARSTO-NE-OPS Program. In: Proceedings of the Fourth Conference on Atmospheric Chemistry: Urban, Regional, and Global-Scale Impacts of Air Pollutants, pp. 107–114, American Meteorological Society, Orlando, FL.Google Scholar
  2. 2.
    Hanna, S.T.: 1994, Mesoscale meteorological model evaluation techniques with emphasis on needs of air quality models. In: R.A. Pielke and R.P. Pearce (eds.), Mesoscale Modeling of the Atmosphere, Vol. 25, pp. 47–58, American Meteorological Society, Boston, MA.Google Scholar
  3. 3.
    Lyons, W.A., Tremback, C.J. and Pielke, R.A.: 1995, Applications of the Regional Atmospheric Modeling System (RAMS) to provide input to photochemical grid models for the Lake Michigan Ozone Study (LMOS), J. Appl. Meteorol. 34, 1762–1786.Google Scholar
  4. 4.
    Cox, R., Bauer, B.L. and Smith, T.: 1998, A mesoscale model intercomparison, Bull. Amer. Meteorol. Soc. 79, 265–283.Google Scholar
  5. 5.
    TNRCC: 2001, MM5/RAMS Fine Grid Meteorological Modeling for September 8–11, 1993 Ozone Episode, pp. 36 (www.tnrcc.state.tx.us/air/aqp/airquality_contracts.html#met02), TNRCC Report No. 31984-12.Google Scholar
  6. 6.
    TNRCC: 2002, High Resolution (1.33 km) MM5 Modeling of the September 1993 COAST Episode: Sensitivity to Model Configuration and Performance Optimization, p. 58 (www.tnrcc.state.tx.us/air/aqp/airquality_contracts.html#met11), TNRCC Report No. 31984-18.Google Scholar
  7. 7.
    Hogrefe, C., Rao, S.T., Kasibhatla, P., Kallos, G., Tremback, C.J., Hao, W., Olerud, D., Xiu, A., McHenry, J. and Alapaty, K.: 2001a, Evaluating the performance of regional-scale photochemical modeling systems: Part I - meteorological predictions, Atmos. Environ. 35, 4159–4174.Google Scholar
  8. 8.
    Sistla, G., Hao, W., Ku, J.Y., Kallos, G., Zhang, K.S., Mao, H.T. and Rao, S.T.: 2001, An operational evaluation of two regional-scale ozone air quality modeling systems over the eastern United States, Bull. Amer. Meteorol. Soc. 82, 945–964.Google Scholar
  9. 9.
    Fast, J.D., Zaveri, R.A., Bian, X., Chapman, E.G. and Easter, R.C.: 2002, The effect of regionalscale transport on oxidants in the vicinity of Philadelphia during the 1999 NE-OPS field campaign, J. Geophys. Res. 107, 10.1029/2001JD000980.Google Scholar
  10. 10.
    Fast, J.D., Doran, J.C., Shaw, W.J., Coulter, R.L. and Martin, T.J.: 2000, The evolution of the boundary layer and its effect on air chemistry in the Phoenix area, J. Geophys. Res. 105, 22833–22848.Google Scholar
  11. 11.
    Zhang, K., Mao, H., Civerolo, K., Berman, S., Ku, J.Y., Rao, S.T., Doddridge, B.G., Philbrick, C.R. and Clark, R.D.: 2001, Numerical investigation of boundary-layer evolution and nocturnal low-level jets: Local versus non-local PBL schemes, Environ. Fluid Mech. 1, 171–208.Google Scholar
  12. 12.
    Buckley, R.L., Weber, A.H. and Weber, J.H., 2001: Statistical comparison of forecast meteorology with observations using the Regional Atmospheric Modeling System, pp. 9 (www.srs.gov/general/pubs/fulltext/ms2001678/ms2001678.html), WSRC-MS-2001-00678.Google Scholar
  13. 13.
    Pielke, R.A., Kallos, G. and Segal, M.: 1989, Horizontal resolution needs for adequate lower tropospheric profiling involved with atmospheric systems forced by horizontal gradients in surface heating, J. Atmos. Oceanic Technol. 6, 741–758.Google Scholar
  14. 14.
    Clark, R.D., Philbrick, C.R. and Doddridge, B.G.: 2002, The effects of local and regional scale circulations on air pollutants during NARSTO-NEOPS 1999–2001. In: Proceedings of the Fourth Conference on Atmospheric Chemistry: Urban, Regional, and Global-Scale Impacts of Air Pollutants, pp. 125–132, American Meteorological Society, Orlando, FL.Google Scholar
  15. 15.
    Doddridge, B.G.: 2000, An airborne study of chemistry and fine particles over the U.S. Mid-Atlantic region. In: Proceedings of the PM2000: Particulate Matter and Health Conference, pp. 4–5, Air and Waste Management Association, Charleston, SC.Google Scholar
  16. 16.
    Grell, G.A., Dudhia, J. and Stauffer, D.R.: 1994, A Description of the Fifth-Generation Penn State/NCARMesoscale Model (MM5), NCAR Technical Note TN-398 + STR. National Center for Atmospheric Research, Boulder, CO.Google Scholar
  17. 17.
    Pagnotti, V.: 1987, A mesoscale meteorological feature associated with high ozone concentrations in the northeastern United States, J. Air Pollut. Control Assoc. 37, 720–722.Google Scholar
  18. 18.
    Angevine, W.M., Bakwin, P.S. and Davis, K.J.: 1998, Wind profiler and RASS measurements compared with measurements from a 450-m-tall tower, J. Atmos. Oceanic Technol. 15, 818–825.Google Scholar

Copyright information

© Kluwer Academic Publishers 2004

Authors and Affiliations

  • Anantharaman Chandrasekar
    • 1
  • C. Russell Philbrick
    • 2
  • Bruce Doddridge
    • 3
  • Richard Clark
    • 4
  • Panos G. Georgopoulos
    • 1
  1. 1.Environmental & Occupational Health Sciences InstituteUSA
  2. 2.Department of Electrical EngineeringPennsylvania State UniversityUSA
  3. 3.Department of MeteorologyUniversity of MarylandCollege ParkUSA
  4. 4.Department of Earth SciencesMillersville UniversityMillersvilleUSA

Personalised recommendations