A three-dimensional variational data assimilation system for multiple aerosol species with WRF/Chem and an application to PM2.5 prediction

Atmospheric Chemistry and Physics

Volumn 13, Issue 8, 2013, Pages 4265-4278

  Li, Z ^a,b   Zang, Z ^b   Li, Q B ^b   Chao, Y ^b,d   Chen, D ^b   Ye, Z ^b   Liu, Y ^c   Liou, K N ^b  

^a CALIFORNIA INSTITUTE OF TECHNOLOGY   (United States)

^b UNIVERSITY OF CALIFORNIA   (United States)

^c BROOKHAVEN NATIONAL LABORATORY   (United States)

^d 248 E Foothill Blvd   (United States)

Author keywords

[No Author keywords available]

Indexed keywords

AEROSOL COMPOSITION; AEROSOL FORMATION; ALGORITHM; CONCENTRATION (COMPOSITION); DATA ASSIMILATION; PARTICULATE MATTER; SIZE DISTRIBUTION;

CALIFORNIA; LOS ANGELES [CALIFORNIA]; UNITED STATES;

EID: 84876920295     PISSN: 16807316     EISSN: 16807324     Source Type: Journal    
DOI: 10.5194/acp-13-4265-2013     Document Type: Article

References (45)

1. Adhikary, B., Kulkarni, S., D'allura, A., Tang, Y., Chai, T., Leung, L. R., Qian, Y., Chung, C. E., Ramanathan, V., and Carmichael, G. R.: A regional scale chemical transport modeling of Asian aerosols with data assimilation of AOD observations using optimal interpolation technique, Atmos. Environ., 42, 8600-8615, doi:10.1016/j.atmosenv.2008.08.031, 2008.
2. Bannister, R. N.: A review of forecast error covariance statistics in atmospheric variational data assimilation. I: Characteristics and measurements of forecast error covariances, Q. J. Roy. Meteorol. Soc., 134, 1951-1970, 2008.
3. Bauer, S. E., Wright, D. L., Koch, D., Lewis, E. R., Mc-Graw, R., Chang, L.-S., Schwartz, S. E., and Ruedy, R.: MATRIX (Multiconfiguration Aerosol TRacker of mIXing state): an aerosol microphysical module for global atmospheric models, Atmos. Chem. Phys., 8, 6003-6035, doi:10.5194/acp-8-6003-2008, 2008.
4. Benedetti, A. and Fisher, M.: Background error statistics for aerosols, Q. J. Roy. Meteor. Soc., 133, 391-405, 2007. (Pubitemid 46621304)
5. Benedetti, A. and Janiskova, M.: Assimilation of MODIS cloud optical depths in the ECMWF model, Mon. Weather Rev., 136, 1727-1746, doi:10.1175/2007MWR2240.1, 2008. (Pubitemid 351819023)
6. Benedetti, A., Morcrette, J.-J., Boucher, O., Dethof, A., Engelen, R. J., Fisher, M., Flentje, H., Huneeus, N., Jones, L., Kaiser, J. W., Kinne, S., Mangold, A., Razinger, M., Simmons, A. J., and Suttie, M.: Aerosol analysis and forecast in the European Centre for Medium-RangeWeather Forecasts Integrated Forecast System: 2. Data assimilation, J. Geophys. Res., 114, D13205, doi:10.1029/2008JD011115, 2009.
7. Binkowski, F. S. and Roselle, S. J.: Models-3 Community Multiscale Air Quality (CMAQ) model aerosol component 1. Model description, J. Geophys. Res., 108, 4183, doi:10.1029/2001JD001409, 2003.
8. Cohn, S. E.: Estimation theory for data assimilation problems: Basic conceptual framework and some open questions, J. Meteorol. Soc. Jpn., 75, 257-288, 1997.
9. Collins, W. D., Rasch, P. J., Eaton, B. E., Khattatov, B. V., Lamarque, J.-F., and Zender, C. S.: Simulating aerosols using a chemical transport model with assimilation of satellite aerosol retrievals: Methodology for INDOEX, J. Geophys. Res., 106, 7313-7336, doi:10.1029/2000JD900507, 2001.
10. Courtier, P., Thepaut, J.-N., and Hollingsworth, A.: A strategy for operational implementation of 4D-Var, using an incremental approach, Q. J. Roy. Meteor. Soc., 120, 1367-1388, 1994.
11. Daley, R.: Atmospheric Data Assimilation, Cambridge atmospheric and space science series, Cambridge University Press, Cambridge, UK, 457 pp., 1991.
12. 10 exceedances on the European scale, Atmos. Environ., 42, 7122-7134, 2008.
13. Diner, D. J., Ackerman, T. P., Anderson, T. L., Bosenberg, J., Braverman, A. J., Charlson, R. J., Collins, W. D., Davies, R., Holben, B. N., Hostetler, C. A., Kahn, R. A., Martonchik, J. V., Menzies, R. T., Miller, M. A., Ogren, J. A., Penner, J. E., Rasch, P. J., Schwartz, S. E., Seinfeld, J. H., Stephens, G. L., Torres, O., Travis, L. D., Wielicki, B. A., and Yu, B.: PARAGON: An integrated approach for characterising aerosol climate impacts and environmental interactions, B. Am. Meteorol. Soc., 85, 1491-1501, doi:10.1175/BAMS-85-10- 1491, 2004.
14. Fast, J. D., Gustafson, Jr.,W. I., Easter, R. C., Zaveri, R. A., Barnard, J. C., Chapman, E. G., and Grell, G. A.:Evolution of ozone, particulates, and aerosol direct forcing in an urban area using a new fully-coupled meteorology-chemistry-Aerosol model, J. Geophys. Res., 111, D21305, doi:10.1029/2005JD006721, 2006. (Pubitemid 46978606)
15. Gelbard, F., Tambour, Y., and Seinfeld, J. H.: Sectional representations for simulating aerosol dynamics, J. Colloid Interface Sci., 76, 541-556, 1980.
16. Generoso, S., Br'eon, F.-M., Chevallier, F., Balkanski, Y., Schulz, M., and Bey, I.: Assimilation of POLDER aerosol optical thickiness into the LMDz-INCA model: Implications for the Arctic aerosol burden, J. Geophys. Res., 112, D02311, doi:10.1029/2005JD006954, 2007. (Pubitemid 47263290)
17. Graham, A.: Kronecker product and matrix Calculus with Applications, 130 pp., Ellis Horwood Ltd., Chichester, England, 1981.
18. Grell, G. A., Peckham, S. E., Schmitz, R., McKeen, S. A., Frost, G., Skamarock, W. C., and Eder, B.: Fully coupled "online" chemistry within the WRF model, Atmos. Environ., 39, 6957-6976, doi:10.1016/j.atmosenv. 2005.04.027, 2005. (Pubitemid 41539465)
19. Ide, K., Courtier, P., Ghil, M., and Lorenc, A. C.: Unified notation for data assimilation, J. Meteorol. Soc. Jpn., 75, 71-79, 1997.
20. Jacobson, M. Z.: Development and application of a new air pollution modeling system-II. Aerosol module structure and design, Atmos. Environ., 31, 131-144, 1997.
21. Jazwinski, A. H.: Stochastic processes and filtering theory, Academic Press, New York, 376 pp., 1970.
22. Kalnay, E.: Atmospheric modeling, data assimilation and predictability, Cambridge Univ. Press, New York, 341 pp., 2003.
23. Kahnert, M.: Variational data analysis of aerosol species in a regional CTM: Background error covariance constraint and aerosol optical observation operators, Tellus B, 60, 753-770, doi:10.1111/j.1600-0889.2008.00377.x, 2008.
24. Li, Z. and Navon, I. M.: Optimality of variational data assimilation and its relationship with the Kalman filter and smoother, Q. J. Roy. Meteor. Soc., 127, 661-683, 2001. (Pubitemid 32257539)
25. Li, Z., Chao, Y., McWilliams, J. C., and Ide, K.: A threedimensional variational data assimilation scheme for the Regional Ocean Modeling System, J. Atmos. Ocean. Tech., 25, 2074-2090, 2008a.
26. Li, Z., Chao, Y., McWilliams, J. C., and Ide, K.: A threedimensional variational data assimilation scheme for the Regional Ocean Modeling System: Implementation and basic experiments, J. Geophys. Res., 113, C05002, doi:10.1029/2006JC004042, 2008b.
27. Liu, Z., Liu, Q., Lin, H.-C., Schwartz, C. S., Lee, Y.-H., and Wang, T: Three-dimensional variational assimilation of MODIS aerosol optical depth: Implementation and application to a dust storm over East Asia, J. Geophys. Res., 116, D23206, doi:10.1029/2011JD016159, 2011.
28. Lorenc, A. C., Ballard, S. P., Bell, R. S., Ingleby, N. B., Andrews, P. L. F., Barker, D. M., Bray, J. R., Clayton, A. M., Dalby, T., Li, D., Payne, T. J., and Saunders, F. W.: The Met. Office global 3-dimensional variational data assimilation scheme, Q. J. Roy. Meteor. Soc., 126, 2991-3012, 2000. (Pubitemid 32015491)
29. Mangold, A., De Backer, H., De Paepe, B., Dewitte, S., Chiapello, I., Derimian, Y., Kacenelenbogen, M., L'eon, J.-F., Huneeus, N., Schulz, M., Ceburnis, D., O'Dowd, C., Flentje, H., Kinne, S., Benedetti, A., Morcrette, J.-J., and Boucher, O.: Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: 3. Evaluation by means of case studies, J. Geophys. Res., 116, D03302, doi:10.1029/2010JD014864, 2011.
30. McGraw, R.: Description of atmospheric aerosol dynamics by the quadrature method of moments, Aerosol Sci. Technol., 27, 255-265, 1997. (Pubitemid 27327931)
31. McKeen, S. A., Wotawa, G., Parrish, D. D., Holloway, J. S., Buhr, M. P., Hubler, G., Fehsenfeld, F. C., and Meagher, J. F.: Ozone production from Canadian wildfires during June and July of 1995, J. Geophys. Res., 107, 4192, doi:10.1029/2001JD000697, 2002. (Pubitemid 43197317)
32. M'enard, R. and Daley, R.: The application of Kalman smoother theory to the estimation of 4DVAR error statistics, Tellus, 48A, 221-237, 1996.
33. Mesinger, F., DiMego, G., Kalnay, E., Mitchell, K., Shafran, P. C., Ebisuzaki, W., Jovic, D., Woollen, J., Rogers, E., Berbery, E. H., Ek, M. B., Fan, Y., Grumbine, R., Higgins, W., Li, H., Lin, Y., Manikin, G., Parrish, D., and Shi, W.: North American Regional Reanalysis, B. Am. Meteorol. Soc., 87, 343-360, doi:10.1175/BAMS-87-3-343, 2006.
34. Parrish, D. F. and Derber, J. C.: The National Meteorological Center's spectral statistical interpolation analysis system, Mon. Weather Rev., 120, 1747-1763, 1992. (Pubitemid 23375504)
35. Pagowski, M., Grell, G. A., McKeen, S. A., Peckham, S. E., and Devenyi, D.: Three-dimensional variational data assimilation of ozone and fine particulate matter observations: Some results using the Weather Research and Forecasting-Chemistry model and Grid-point Statistical Interpolation, Q. J. Roy. Meteorol. Soc., 136, 2013-2024, 2010.
36. Sandu, A. and Chai, T. F.: Chemical data assimilation an overview, The Atmosphere, 2, 426-463, 2011.
37. Seinfeld, J. H. and Pandis, S. N.: Atmospheric Chemistry and Physics: From Air Pollution to Climate Change, 2nd Edn., J.Wiley, New York, 2006.
38. Sekiyama, T. T., Tanaka, T. Y., Shimizu, A., and Miyoshi, T.: Data assimilation of CALIPSO aerosol observations, Atmos. Chem. Phys., 10, 39-49, doi:10.5194/acp-10-39-2010, 2010.
39. Simmons, A.: Assimilation of satellite data for numerical weather prediction: basic importance, concepts and issues, ECMWF seminar proceedings. Exploitation of the new generation of satellite instruments for numerical weather prediction, 4-8 September 2000, Reading, UK, 21-46, 2000.
40. Skamarock, W. C., Klemp, J. B., Dudhia, J., Gill, D. O., Barker, D. M., Duda, M. G., Huang, X., Wang, W., and Powers, J. G.: A description of the advanced research WRF version 3, NCAR Tech. Note, NCAR/TN-475+STR, 8 pp., Natl. Cent. for Atmos. Res., Boulder, Colorado, http://www.mmm.ucar.edu/wrf/users/ docs/arw v3.pdf, 2008.
41. 10 data assimilation over Europe with the optimal interpolation method, Atmos. Chem. Phys., 9, 57-70, doi:10.5194/acp-9-57-2009, 2009.
42. Whitby, K. T.: The physical characteristics of sulfate aerosols, Atmos. Environ., 12, 135-159, 1978.
43. Yu, H., Dickinson, R. E., Chin, M., Kaufman, Y. J., Geogdzhayev, B., and Mishchenko, M. I.: Annual cycle of global distributions of aerosol optical depth from integration of MODIS retrievals and GOCART model simulations, J. Geophys. Res., 108, 4128, doi:10.1029/2002JD002717, 2003.
44. Zaveri, R. A., Easter, R. C., Fast, J. D., and Peters, L. K.: Model for Simulating Aerosol Interactions and chemistry (MOSAIC), J. Geophys. Res., 113, D13204, doi:10.1029/2007JD008782, 2008.
45. Zhang, J., Reid, J. S., Westphal, D., Baker, N., and Hyer, E.: A System for Operational Aerosol Optical Depth Data Assimilation over Global Oceans, J. Geophys. Res., 113, D10208, doi:10.1029/2007JD009065, 2008.