Data assimilation and air quality
Implementation of Real-Time Bias-Corrected O3 and PM2.5 Air Quality Forecast and Their Performance Evaluations During 2008 over the Continental United States
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...
KeywordsSecondary Organic Aerosol False Alarm Ratio Vertical Eddy Diffusivity Assimilation Window Normalize Mean Bias
- 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
- 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
- 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
- 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
- Pandis SN, Harley RA, Cass GR et al. (1992) Secondary organic aerosol formation and transport. Atmos. Environ. 26A:2269–2282.Google Scholar
- Pudykiewicz JA, Kallaur A, Smolarkiewicz PK (1997) Semi-Lagrangian modelling of tropospheric ozone. Tellus 49B:231–248.Google Scholar
- 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
- 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]
- 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
- 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
- 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
- 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
- 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
- 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.
- 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
- U.S. Environmental Protection Agency (2004a) EGAS version 5.0 beta. Available at http://www.epa.gov/ttn/ecas/egas5.htm.
- 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.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- Schaap et al.: The LOTOS-EUROS model: description, validation and latest developments. Int. J. Environment and Pollution 32 no 2. (2008)Google Scholar
- 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