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Data assimilation and air quality

  • G. Kallos
  • B. Pun
Conference paper
Part of the NATO Science for Peace and Security Series C: Environmental Security book series (NAPSC)

Implementation of Real-Time Bias-Corrected O3 and PM2.5 Air Quality Forecast and Their Performance Evaluations During 2008 over the Continental United States

  • Daiwen Kang
  • Rohit Mathur
  • S. Trivikrama Rao

Abstract

Real-time bias-corrected O3 and PM2.5 forecast systems are implemented using the Kalman Filter, combining observations from AIRnow and outputs from the NOAA/EPA’s NAM/CMAQ air quality forecast model. Bias-corrected O3 and PM2.5 forecasts are created at locations of the AIRNow monitoring network where report hourly concentrations of these species. Observations and model outputs from two previous consecutive days are required to produce bias-corrected model forecasts. The performance of these systems is examined on a daily basis using O3 and PM2.5observations and the results are compared with raw model forecasts. The overall performance of the Kalman filtering technique and its capability to produce a real-time bias correction to improve the day-to-day forecast from the NAM-CMAQ...

Keywords

Secondary Organic Aerosol False Alarm Ratio Vertical Eddy Diffusivity Assimilation Window Normalize Mean Bias 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

References

  1. Delle Monache, L., T. Nipen, X. Deng, Y. Zhou, and R. Stull (2006), Ozone ensemble forecasts: 2. A Kalman filter predictor bias correction, J. Geophys. Res., 111, D05308, doi:10.1029/2005JD 006311.CrossRefGoogle Scholar
  2. Kang, D., B. Eder, A. Stein, G. Grell, S. Peckham, and J. McHenry (2005), The New England air quality forecasting pilot program: development of an evaluation protocol and performance benchmark. J. Air & Waste Manage. Assoc., 55, 1782–1796.Google Scholar
  3. Kang, D., R. Mathur, S.T. Rao, and S. Yu (2008), Bias adjustment techniques for improving ozone air quality forecasts, J. Geophys. Res., 113, D23308, doi: 10.1029/2008JD010151.CrossRefGoogle Scholar
  4. Kang, D., R. Mathur, and S.T. Rao (2009), Assessment on bias-adjusted NAM-CMAQ PM2.5 air quality forecasts over the continental United States during 2007, in preparation.Google Scholar
  5. Mathur, R., S. Yu, D. Kang, and K. Schere (2008), Assessment of the Winter-time Performance of Developmental Particulate Matter Forecasts with the Eta-CMAQ Modeling System, J. Geophys. Res., doi:10.1029/2007JD008580, 113, D02303.CrossRefGoogle Scholar
  6. Wilczak, J., S. McKeen, I. Djalalova, G. Grell, S. Peckham, W. Gong, V. Bouchet, R. Moffet, J. McHenry, P. Lee, Y. Tang, and G. R. Carmichael (2006), Bias-corrected ensemble and probabilistic forecasts of surface ozone over eastern North America during the summer of 2004, J. Geophys. Res., 111, D23S28, doi:10.1029/2006JD007598.CrossRefGoogle Scholar

References

  1. Côté J, Desmarais J-G, Gravel S et al. (1998a) The operational CMC/MRB Global Environmental Multiscale (GEM) model. Part 1: Design considerations and formulation. Mon. Wea. Rev. 126:1373–1395.CrossRefGoogle Scholar
  2. Côté J, Desmarais J-G, Gravel S et al. (1998b) The operational CMC-MRB Global Environment Multiscale (GEM) model. Part II: Results. Mon. Wea. Rev. 126:1397–1418.CrossRefGoogle Scholar
  3. Gong W, Dastoor AP, Bouchet VS et al. (2006) Cloud processing of gases and aerosols in a regional air quality model (AURAMS). Atmos. Res. 82:248–275.CrossRefGoogle Scholar
  4. Jiang W (2004) Reply to the “Comment on ‘Instantaneous secondary organic aerosol yields and their comparison with overall aerosol yields for aromatic and biogenic hydrocarbons’”, by Knipping et al. (2004). Atmos. Environ. 38:2763–2767.CrossRefGoogle Scholar
  5. McKeen S., Chung SH, Wilczak J et al. (2007) Evaluation of several real-time PM2.5 forecast models using data collected during the ICARTT/NEAQS 2004 field study. J. Geophys. Res. doi:10.1029/2006JD007608.Google Scholar
  6. Pandis SN, Harley RA, Cass GR et al. (1992) Secondary organic aerosol formation and transport. Atmos. Environ. 26A:2269–2282.Google Scholar
  7. Pudykiewicz JA, Kallaur A, Smolarkiewicz PK (1997) Semi-Lagrangian modelling of tropospheric ozone. Tellus 49B:231–248.Google Scholar
  8. Sirois A, Pudykiewicz JA, Kallaur A (1999) A comparison between simulated and observed ozone mixing ratios in eastern North America. J. Geophys. Res., 104:21, 397–21, 423.Google Scholar
  9. Smyth SC, Jiang W, Roth H et al. (2009) A comparative performance evaluation of the AURAMS and CMAQ air quality modelling systems. Atmos. Environ. 43:1059–1070.CrossRefGoogle Scholar
  10. Talbot D, Moran MD, Bouchet V et al. (2008) Development of a new Canadian operational air quality forecast model. In: Borrego C, Miranda AI (eds) Air pollution modelling and its application XIX, Springer, Dordrecht, 470–478.CrossRefGoogle Scholar

References

  1. Chang, J. S., R. A. Brost, I. S. A. Isaksen, S. Madronich, P. Middleton, W. R. Stockwell, and C. J. Walcek, 1987: A three-dimensional Eulerian acid deposition model: Physical concepts and formulation. J. Geophys. Res., 92, 14681–14700.CrossRefGoogle Scholar
  2. Janjic, Z. I., 2001: Nonsingular implementation of the Mellor-Yamada level 2.5 scheme in the NCEP meso-model, NCEP Office Note 437. [Available at http://www.emc.ncep.noaa.gov/ officenotes/FullTOC.html]
  3. Lee, P. and coauthors (2009): Impact of consistent boundary layer mixing approaches between NAM and CMAQ, Environmental Fluid Mechanics: Volume 9, Issue1, Page 23–42 DOI:10.1007/ s10652-008-9089-0.CrossRefGoogle Scholar
  4. Mellor, G. L., and T. Yamada, 1982: Development of a turbulence closure model for geophysical fluid problems. Rev. Geophys. Space Phys., 20, 851–871.CrossRefGoogle Scholar
  5. Otte, T.L., G. Pouliot, J. E. Pleim, J. O. Young, K. L. Schere, D. C. Wong, P. C. S. Lee, M. Tsidulko, J. T. McQueen, P. Davidson, R. Mathur, H.Y. Chuang, G. DiMego, and N. L. Seaman, 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 (No.3), 367–384.CrossRefGoogle Scholar
  6. Thompson A. M., J. E. Yorks, S. K. Miller, J. C. Witte, K. M. Dougherty, G. A. Morris, D. Baumgardner, L. Ladino, and B. Rappenglueck, (2008), Tropospheric ozone sources and wave activity over Mexico City and Houston during MILAGRO/Intercontinental Transport Experiment (INTEX-B) Ozonesonde Network Study, 2006 (IONS-06), Atmos. Chem. Phys. Discuss., 8, 5979–6007.CrossRefGoogle Scholar

References

  1. H. Elbern, A. Strunk, H. Schmidt, O. Talagrand, Atmospheric Chemistry and Physics 7(14), 3749 (2007)CrossRefGoogle Scholar
  2. M. Sofiev, P. Siljamo, I. Valkama, M. Ilvonen, J. Kukkonen, Atmospheric Environment 40, 674 (2006)CrossRefGoogle Scholar
  3. M. Galperin, in Large Scale Computations in Air Pollution Modelling, ed. By Z. Zlatev, J. Dongarra, I. Dimov, J. Brandt, P.J. Builtjes (Kluwer Academic Publishers, 1999), pp. 161–172Google Scholar
  4. M. Sofiev, J. Geophys. Res. 107 (2002)Google Scholar
  5. M. Sofiev, Atmospheric Environment 34, 2481 (2000)CrossRefGoogle Scholar
  6. A. Sandu, D.N. Daescu, G.R. Carmichael, T. Chai, J. Comput. Phys. 204(1), 222 (2004). DOI http://dx.doi.org/10.1016/j.jcp.2004.10.011 Google Scholar

References

  1. Grell G.A., Dudhia J., and Stauffer D.R. (1994) A description of the fifth-generation Penn State/NCAR mesoscale model (MM5). Prepared by the National Center for Atmospheric Research, Boulder, CO, NCAR Technical Note-398.Google Scholar
  2. Houyoux M., Vukovich J., and Brandmeyer J. (2000) Sparse Matrix Operator Kernel Emissions Modeling System (SMOKE) user manual. Prepared by MCNC-North Carolina Supercomputing Center, Environmental Programs, Research Triangle Park, NC.Google Scholar
  3. Larkin N.K., O’Neill S.M., Solomon R., Krull C., Raffuse S.M., Rorig M., Peterson J., and Ferguson S.A. (2008) The BlueSky smoke modeling framework. International Journal of Wildland Fire 18, 906–920 (8).CrossRefGoogle Scholar
  4. National Exposure Research Laboratory (1999) Science algorithms of the EPA Models-3 Community Multiscale Air Quality (CMAQ) modeling system. Report prepared by the National Exposure Research Laboratory, Research Triangle Park, NC, EPA/600/R-99/030 (peer reviewed), March.Google Scholar
  5. Ruminski M., Kondragunta S., Draxler R.R., and Zeng J. (2006) Recent changes to the Hazard Mapping System. 15th International Emission Inventory Conference, New Orleans, LA. Available at http://www.epa.gov/ttn/chief/conferences.htm.
  6. Sullivan D.C., Raffuse S.M., Pryden D.A., Craig K.J., Reid S.B., Wheeler N.J.M., Chinkin L.R., Larkin N.K., Solomon R., and Strand T. (2008) Development and applications of systems for modeling emissions and smoke from fires: the BlueSky smoke modeling framework and SMARTFIRE: 17th International Emissions Inventory Conference, Portland, OR, June 2–5.Google Scholar
  7. U.S. Environmental Protection Agency (2004a) EGAS version 5.0 beta. Available at http://www.epa.gov/ttn/ecas/egas5.htm.
  8. U.S. Environmental Protection Agency (2004b) Biogenic emissions inventory system (BEIS) modeling. Website prepared by the Atmospheric Sciences Modeling Division. Available at http://www.epa.gov/asmdnerl/biogen.html.
  9. U.S. Environmental Protection Agency (2005) EPA’s National Inventory Model (NMIM), a consolidated emissions modeling system for MOBILE6 and NONROAD. Prepared by the Office of Transportation and Air Quality, Research Triangle Park, NC, EPA 420-R-05-024, December.Google Scholar

References

  1. Damski, J., Thölix, L., Backman, L., Kaurola, J., Taalas, P., Austin, J., Butchart, N. and Kulmala, M. (2007) A Chemistry-Transport Model Simulation of Middle Atmospheric Ozone from 1980 to 2019 Using Coupled Chemistry GCM Winds and Temperatures, Atmos. Chem. Phys., 7, 2165–2181.CrossRefGoogle Scholar
  2. Galperin, M. (1999) Approaches for improving the numerical solution of the advection equation. In Z. Zlatev, J. Dongarra, I. Dimov, J. Brandt, and P. J. Builtjes, (Eds), Large Scale Computations in Air Pollution Modelling, pages 161–172. Kluwer Academic Publishers, 1999.Google Scholar
  3. Galperin M.V., (2000). The Approaches to Correct Computation of Airborne Pollution Advection. In: Problems of Ecological Monitoring and Ecosystem Modelling. XVII, St. Petersburg, Gidrometeoizdat, 2000, pp. 54–68 (in Russian)Google Scholar
  4. Guenther A., Hewitt N.C., Erickson D., Fall R., Geron C., Graedel T., Harley P., Klinger L., Lerdau M., McKay W. A, Pierce T., Scholes B., Steinbrecher R., Tallamraju R., Taylor J., Zimmerman P.A (1995): Global model of natural volatile organic compound emissions. J. Geophys. Res. 100, 8873–8892CrossRefGoogle Scholar
  5. Maryon, R.H., Smith, F.B., Conway, B.J., Goddard, D.M. (1991) The UK Nuclear Accident Model. Progress in Nuclear Energy, 26, No 2, 85–104CrossRefGoogle Scholar
  6. Robertson, L., Langner, J. and Engardt, M. (1999) An Eulerian limited-area atmospheric transport model. J. Appl. Meteor. 38, 190–210CrossRefGoogle Scholar
  7. Saltbones, J., Foss, A., Bartnicki, J. (1996) A real time dispersion model for severe nuclear accidents, tested in the European tracer experiment. Syst. Anal. Modelling Simulat. 25, 263–279Google Scholar
  8. Sofiev, M. (2002) Extended resistance analogy for construction of the vertical diffusion scheme for dispersion models. J. of Geophys.Research – Atmosphere, 107, D12, doi: 10.10292001JD 001233.Google Scholar
  9. Sofiev M., Siljamo, P., Valkama, I., Ilvonen, M., Kukkonen, J. (2006) A dispersion modelling system SILAM and its evaluation against ETEX data. Atmosph.Environ., 40, 674–685, doi:10.1016/j.atmosenv.2005.09.069.CrossRefGoogle Scholar
  10. Stohl, A., Hittenberger, M., and Wotawa, G. (1998): Validation of the Lagrangian particle dispersion model FLEXPART against large scale tracer experiments. Atmosph. Envir. 24, 4245–4264.CrossRefGoogle Scholar

References

  1. Schaap et al.: The LOTOS-EUROS model: description, validation and latest developments. Int. J. Environment and Pollution 32 no 2. (2008)Google Scholar
  2. Manders et al.: Testing the capability of the chemistry transport model LOTOS-EUROS to forecast PM10 levels in the Netherlands. Atmospheric Environment, 43 (2009)Google Scholar

Copyright information

© Springer Science+Business Media B.V. 2010

Authors and Affiliations

  1. 1.Division of Applied PhysicsUniversity of Athens School of PhysicsAthensGreece

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