ACP Atmospheric Chemistry and Physics ACP 1680-7324 Copernicus GmbH Göttingen, Germany 10.5194/acp-11-12901-2011 Improvement of ozone forecast over Beijing based on ensemble Kalman filter with simultaneous adjustment of initial conditions and emissions Tang X. 1 Zhu J. 1 Wang Z. F. 1 Gbaguidi A. 1 LAPC and ICCES, Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing, China 20 12 2011 11 24 12901 12916 This is an open-access article ditributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited. This article is available from http://www.atmos-chem-phys.net/11/12901/2011/acp-11-12901-2011.html The full text article is available as a PDF file from http://www.atmos-chem-phys.net/11/12901/2011/acp-11-12901-2011.pdf

In order to improve the surface ozone forecast over Beijing and surrounding regions, data assimilation method integrated into a high-resolution regional air quality model and a regional air quality monitoring network are employed. Several advanced data assimilation strategies based on ensemble Kalman filter are designed to adjust O<sub>3</sub> initial conditions, NO<sub>x</sub> initial conditions and emissions, VOCs initial conditions and emissions separately or jointly through assimilating ozone observations. As a result, adjusting precursor initial conditions demonstrates potential improvement of the 1-h ozone forecast almost as great as shown by adjusting precursor emissions. Nevertheless, either adjusting precursor initial conditions or emissions show deficiency in improving the short-term ozone forecast at suburban areas. Adjusting ozone initial values brings significant improvement to the 1-h ozone forecast, and its limitations lie in the difficulty in improving the 1-h forecast at some urban site. A simultaneous adjustment of the above five variables is found to be able to reduce these limitations and display an overall better performance in improving both the 1-h and 24-h ozone forecast over these areas. The root mean square errors of 1-h ozone forecast at urban sites and suburban sites decrease by 51% and 58% respectively compared with those in free run. Through these experiments, we found that assimilating local ozone observations is determinant for ozone forecast over the observational area, while assimilating remote ozone observations could reduce the uncertainty in regional transport ozone.

 References An, J. L., Wang, Y. S., and Zhu, B.: Principal component and multiple regress analysis predicting ozone concentrations: case study in summer in Beijing, Acta Scientiae Circumstantiae (in Chinese), 30, 1286–1294, 2010. Anderson, J. L.: An ensemble adjustment Kalman filter for data assimilation, Mon. Weather Rev., 129, 2884–2903, 2001. Anderson, H. R., Ponce de Leon, A., Bland, J. M., Bower, J. S., and Strachan, D. P.: Air pollution and daily mortality in London: 1987-92, British Medical Journal, 312, 665–669, 1996. Carmichael, G. R., Sandu, A., Chai, T. F., Daescu, D. N., Constantinescu, E. M., and Tang, Y. H.: Predicting air quality: improvements through advanced methods to integrate models and measurements, J. Comput. Phys., 227, 3540–3571, 2008. Chai, T. F., Carmichael, G. R., Sandu, A., Tang, Y. H., and Daescu, D. N.: Chemical data assimilation of Transport and Chemical Evolution over the Pacific (TRACE-P) aircraft measurements, J. Geophys. Res., 111, D02301, http://dx.doi.org/10.1029/2005JD005883doi:10.1029/2005JD005883, 2006. Chameides, W. L., Li, X. S., Tang, X. Y., Zhou, X. J., Chao, L., Kiang, C.S., John, J. St., Saylor, R. D., Liu, S. C., Lam, K. S., Wang, T., and Giorgi, F.: Is ozone pollution affecting crop yields in China?, Geophys. Res. Lett., 26, 867–870, 1999. Chan, C. K. and Yao, X.: Air pollution in mega cities in China, Atmos. Environ., 42, 1–42, 2008. Constantinescu, E. M., Sandu, A., Chai, T. F., and Carmichael, G. R.: Ensemble-based chemical data assimilation. II: Covariance localization, Q. J. R. Meteorol. Soc., 133, 1245–1256, 2007a. Constantinescu, E. M., Sandu, A., Chai, T. F., and Carmichael, G. R.: Assessment of ensemble-based chemical data assimilation in an idealized setting, Atmos. Environ., 41, 18–36, 2007b. Constantinescu, E. M., Sandu, A., Chai, T. F., and Carmichael, G. R.: Ensemble-based chemical data assimilation. I: General approach, Q. J. Roy. Meteorol. Soc., 133, 1229–1243, 2007c. Eben, K., Jurus, P., Resler, J., Belda, M., Pelikan, E., Kruger, B. C., and Keder, J.: An ensemble Kalman filter for short-term forecasting of tropospheric ozone concentrations, Q. J. Roy. Meteorol. Soc., 131, 3313–3322, 2005. Elbern, H., Strunk, A., Schmidt, H., and Talagrand, O.: Emission rate and chemical state estimation by 4-dimensional variational inversion, Atmos. Chem. Phys., 7, 3749–3769, http://dx.doi.org/10.5194/acp-7-3749-2007doi:10.5194/acp-7-3749-2007, 2007. Evensen, G.: Sequential Data Assimilation with a Nonlinear Quasi-Geostrophic Model Using Monte-Carlo Methods to Forecast Error Statistics, J. Geophys. Res., 99, 10143–10162, 1994. Evensen, G.: The ensemble Kalman Filter: theoretical formulation and practical implementation, Ocean Dynam., 53, 343–367, 2003. Evensen, G.: The Ensemble Kalman Filter for combined state and parameter estimation, IEEE Control Systems Magazine, 29, 83–104, 2009. Feng, Y., Zhang, W. F., Sun., D. Z., and Zhang, L. Q.: Ozone concentration forecast method based on genetic algorithm optimized back propagation neural networks and support vector machine data classification, Atmos. Environ., 45, 1979–1985, 2011. Flemming, J., Reimer, E., and Stern, R.: Long term evaluation of the ozone forecast by an Eulerian model, Phys. Chem., Earth, 26, 775–779, 2001. Grell, G. A., Dudhia, J., and Stauffer, D. R.: A description of the fifth-generation Penn State/NCAR mesoscale model (MM5), NCAR Technical Note, NCAR/TN-398+STR, 117 pp., 1994. Hanea, R. G., Velders, G. J. M., and Heemink, A.: Data assimilation of ground-level ozone in Europe with a Kalman filter and chemistry transport model, J. Geophys. Res., 109, D10302, http://dx.doi.org/10.1029/2003JD004283doi:10.1029/2003JD004283, 2004. Hanna, S. R., Chang, J. C., and Fernau, M. E.: Monte Carlo estimates of uncertainties in predictions by a photochemical grid model (UAM-IV) due to uncertainties in input variables, Atmos. Environ., 32, 3619–3628, 1998. Hao, J. M., Wang, L. T., Shen, M. J., Li, L., and Hu, J. N.: Air quality impacts of power plant emissions in Beijing, Environ. Pollut., 147, 401–408, 2007. Houtekamer, P. L. and Mitchell, H. L.: A sequential ensemble Kalman filter for atmospheric data assimilation, Mon. Weather Rev., 129, 123–137, 2001. Houyoux, M. R., Vukovich, J. M., Coats, C. J., Wheeler, N. J. M., and Kasibhatla, P. S.: Emission inventory development and processing for the Seasonal Model for Regional Air Quality (SMRAQ) project, J. Geophys. Res., 105, 9079–9090, 2000. Kalman, R. E.: A new approach to linear filtering and prediction problems, Transactions of the ASME – Journal of Basic Engineering, 82, 35–45, 1960. Keppenne, C. L.: Data assimilation into a primitive-equation model with a parallel ensemble Kalman filter, Mon. Weather Rev., 128, 1971–1981, 2000. Li, J., Wang, Z. F., Akimoto, H., Tang, J., and Uno, I.: Modeling of the impacts of China's anthropogenic pollutants on the surface ozone summer maximum on the northern Tibetan Plateau, Geophys. Res. Lett., 36, L24802, http://dx.doi.org/10.1029/2009GL041123doi:10.1029/2009GL041123, 2009. Li, J., Wang, Z., Wang, X., Yamaji, K., Takigawa, M., Kanaya, Y., Pochanart, P., Liu, Y., Irie, H., Hu, B., Tanimoto, H., and Akimoto, H.: Impacts of aerosols on summertime tropospheric photolysis frequencies and photochemistry over Central Eastern China, Atmos. Environ., http://dx.doi.org/10.1016/j.atmosenv.2011.01.016doi:10.1016/j.atmosenv.2011.01.016, in press, 2011. Mallet, V.: Ensemble forecast of analyses: Coupling data assimilation and sequential aggregation, J. Geophys. Res., 115, D24303, http://dx.doi.org/10.1029/2010JD014259doi:10.1029/2010JD014259, 2010. Mallet, V., Stoltz, G., and Mauricette, B.: Ozone ensemble forecast with machine learning algorithms, J. Geophys. Res., 114, D05307, http://dx.doi.org/10.1029/2008JD009978doi:10.1029/2008JD009978, 2009. Mitchell, H. L. and Houtekamer, P. L.: An adaptive ensemble Kalman filter, Mon. Weather Rev., 128, 416–433, 2000. Sakov, P. and Oke, P. R.: Implications of the form of the ensemble transformation in the ensemble square root filters, Mon. Weather Rev., 136, 1042–1053, 2008. Segers, A.: Data assimilation in atmospheric chemistry models using Kalman filtering, Ph.D. thesis, Delft Univ., Delft, The Netherlands, 2002. Shao, M., Tang, X. Y., Zhang, Y. H., and Li, W. J.: City clusters in China: air and surface water pollution, Front. Ecol. Environ., 4, 353–361, 2006. Streets, D. G., Fu, J. H. S., Jang, C. J., Hao, J. M., He, K. B., Tang, X. Y., Zhang, Y. H., Wang, Z. F., Li, Z. P., Zhang, Q., Wang, L. T., Wang, B. Y., and Yu, C.: Air quality during the 2008 Beijing Olympic Games, Atmos. Environ., 41, 480–492, 2007. Sudo, K. M., Takahashi, J., Kurokawa, and Akimoto, H.: CHASER: A global chemical model of the troposphere 1.Model description, J. Geophys. Res., 107, 4339, http://dx.doi.org/10.1029/2001JD001113doi:10.1029/2001JD001113, 2002. Tang, X., Wang, Z. F., Zhu, J., Wu, Q. Z., and Gbaguidi, A.: Ensemble-based surface ozone forecast over Beijing, Climate and Environmental Research (in Chinese), 15, 677–684, 2010a. Tang, X., Wang, Z. F., Zhu, J., Wu, Q. Z., and Gbaguidi, A.: Preliminary application of Monte Carlo uncertainty analysis in ozone simulation, Climate and Environmental Research (in Chinese), 15, 541–550, 2010b. Tang, X., Wang, Z. F., Zhu, J., Gbaguidi, A., Wu, Q. Z., Li, J., and Zhu, T.: Sensitivity of ozone to precursor emissions in urban Beijing with a Monte Carlo scheme, Atmos. Environ., 44, 3833–3842, 2010c. van Loon, M., Builtjes, P. J. H., and Segers, A. J.: Data assimilation of ozone in the atmospheric transport chemistry model LOTOS, Environ. Model. Softw., 15, 603–609, 2000. van Loon, R., Vautard, R., Schaap, M., Bergstrom, R., Bessagnet,B., Brandt, J., Builtjes, P. J. H., Christensen, J. H., Cuvelier, C., Graff, A., Jonson, J. E., Kroli, M., Langner, J., Roberts, P., Rouil, L., Stern, R., Tarrason, L., Thunis, P., Vignati., E., White, L., and Wind, P.: Evaluation of long-term ozone simulations from seven regional air quality models and their ensemble, Atmos. Environ., 41, 2083–2097, 2007. Wang, M., Zhu, T., Zhang, J. P., Zhang, Q. H., Lin, W. W., Li, Y., and Wang, Z. F.: Using a mobile laboratory to characterize the distribution and transport of sulfur dioxide in and around Beijing, Atmos. Chem. Phys., 11, 11631–11645, http://dx.doi.org/10.5194/acp-11-11631-2011doi:10.5194/acp-11-11631-2011, 2011. Wang, Y., Hao, J., McElroy, M. B., Munger, J. W., Ma, H., Chen, D., and Nielsen, C. P.: Ozone air quality during the 2008 Beijing Olympics: effectiveness of emission restrictions, Atmos. Chem. Phys., 9, 5237–5251, http://dx.doi.org/10.5194/acp-9-5237-2009doi:10.5194/acp-9-5237-2009, 2009. Wang, Z., Akimoto, H., and Uno, I.: Neutralization of soil aerosol and its impact on the distribution of acid rain over East Asia: Observations and model results, J. Geophys. Res., 107, http://dx.doi.org/10.1029/2001JD001040doi:10.1029/2001JD001040, 2002. Wang, Z. F., Huang, M. Y., He, D., Xu, H. Y., and Zhou, L.: Studies on transport of acid substance in China and East Asia part I: 3-D Eulerian transport model for pollutants, Chin. J. Atmos. Sci., 21, 367–375, 1997. Wang, Z. F., Xie, F. Y., Wang, X. Q., An, J. L., and Zhu, J.: Development and application of Nested Air Quality Prediction Modeling System, Chinese Journal of Atmospheric Sciences (in Chinese), 30, 778–790, 2006. Wesely, M. L.: Parameterization of surface resistances to gaseous dry deposition in regional-scale numerical models, Atmos. Environ., 23, 1293–1304, 1989. Wu, L., Mallet, V., Bocquet, M., and Sportisse, B.: A comparison study of data assimilation algorithms for ozone forecasts, J. Geophys. Res., 113, D20310, http://dx.doi.org/10.1029/2008JD009991doi:10.1029/2008JD009991, 2008. Wu, Q. Z., Wang, Z. F., Gbaguidi, A., Gao, C., Li, L. N., and Wang, W.: A numerical study of contributions to air pollution in Beijing during CAREBeijing-2006, Atmos. Chem. Phys., 11, 5997–6011, http://dx.doi.org/10.5194/acp-11-5997-2011doi:10.5194/acp-11-5997-2011, 2011. Xin, J. Y., Wang, Y. S., Tang, G. Q., Wang, L. L., Sun, Y., Wang, Y. H., Hu, B., Song, T., Ji, D. S., Wang, W. F., Li, L., and Liu, G. R.: Variability and reduction of atmospheric pollutants in Beijing and its surrounding area during the Beijing 2008 Olympic Games, Chinese Sci. Bull. (in Chinese), 55, 1937–1944, 2010. Zaveri, R. A. and Peters, L. K.: A new lumped structure photochemical mechanism for large-scale applications, J. Geophys. Res., 104, 30387–30415, 1999. Zhang, Q., Streets, D. G., Carmichael, G. R., He, K., Huo, H., Kannari, A., Klimont, Z., Park, I., Reddy, S., Fu, J. S., Chen, D., Duan, L., Lei, Y., Wang, L., and Yao, Z.: Asian emissions in 2006 for the NASA INTEX-B mission, Atmos. Chem. Phys., 9, 5131–5153, http://dx.doi.org/10.5194/acp-9-5131-2009doi:10.5194/acp-9-5131-2009, 2009. Zhang, W., Wang, Z. F., An, J. L., Yang, T., and Tang, X.: Update the ensemble air quality modeling system with BP model during Beijing Olympics (in Chinese), Clim. Environ. Res., 15, 595–601, 2010. Zou, Y. F., Wu, Q., Wang, Z. F., and Zhu, J.: Update of industrial pollution source in Hebei and its effects on regional air quality modeling during 2008 Beijing Olympics Games (in Chinese), Clim. Environ. Res., 15, 624–635, 2010.