4,771
Views
17
CrossRef citations to date
0
Altmetric
Technical Papers

A comparison of correlation-length estimation methods for the objective analysis of surface pollutants at Environment and Climate Change Canada

, &
Pages 874-895 | Received 14 Dec 2015, Accepted 01 Apr 2016, Published online: 22 Apr 2016

References

  • Bannister, R.N. 2008. A review of forecast error covariance statistics in atmospheric variational data assimilation. I: Characteristics and measurements of forecast error covariances. Q. J. R. Meteorol. Soc., 134:1951–70. doi:10.1002/qj.339
  • Bocquet, M., H. Elbern, H. Eskes, M. Hirtl. R. Žabkar, G.R. Carmichael, J. Flemming, A. Inness, M. Pagowski, J.L. Pérez Camaño, P.E. Saide, R. San Jose, M. Sofiev, J. Vira, A. Baklanov, C. Carnevale, G. Grell, and C. Seigneur. 2015. Data assimilation in atmospheric chemistry models: Current status and future prospects for coupled chemistry meteorology models. Atmos. Chem. Phys. 15:5325–58. www.atmos-chem-phys.net/15/5325/2015. doi:10.5194/acp-15-5325-2015
  • Bouttier, F. 1994. Sur la prévision de la qualité des prévisions météorologiques. Ph.D. dissertation, Université Paul Sabatier, 240 pp. [Available from Université Paul Sabatier, Toulouse, France.]
  • Buehner, M., R. McTaggart-Cowan, A. Beaulne, C. Charette, L. Garand, S. Heilliette. E. Lapalme, S. Laroche, S.R. Macpherson, J. Morneau, and A. Zadra. 2015. Implementation of a deterministic weather forecasting system based on ensemble-variational data assimilation at Environment Canada. Part I: The global system. Monthly Weather Rev. 143:2532–59. doi:10.1175/MWR-D-14-00354.1
  • Caron, J.-F., T. Milewski, M. Buehner, R. L. Fillion, M. Reszka, S.R. Macpherson, and J. St-James. 2015. Implementation of a deterministic weather forecasting system based on ensemble-variational data assimilation at Environment Canada. Part II: The regional system. Monthly Weather Rev. 143:2560–80. doi:10.1175/MWR-D-14-00353.1
  • Chai, T., G.R. Carmichael, Y. Tang, A. Sandu, M. Hardesty, P. Pilewskie, S. Whitlow, E.V. Browel, M.A. Avery, P. Nédélec, J.T. Merrill, A.M. Thompson, and E. Williams. 2007. Four-dimensional data assimilation experiments with International Consortium for Atmospheric Research on Transport and Transformation ozone measurements. J. Geophys. Res. 112:D12S15. doi:10.1029/2006JD007763,2007
  • Crouze, D.L., P.A. Peters, P. Hystad, J.R. Brook, A. van Donkelaar, R.V. Martin, P.J. Villeneuve, M. Jerrett, M.S. Goldberg, C.A. Pope III, M. Brauer, R.D. Brook, A. Robichaud, R. Ménard, R.T. Burnett. 2014. Ambient PM2.5, O3, and NO2 exposures and association with mortality over 16 years of follow-up in the Canadian Census Health and Environment Cohort (CanCHEC). Environ. Health Perspect. 123:1180–86. doi:10.1289/ehp.1409276( accessedNovember2, 2015)
  • Daley, R. 1991: Atmospheric Data Analysis. New York, NY: Cambridge University Press.
  • Daley, R., and R. Ménard. 1993. Spectral characteristics of Kalman filter systems for atmospheric data assimilation. Monthly Weather Rev. 121:1554–65. doi:10.1175/1520-0493(1993)121%3C1554:SCOKFS%3E2.0.CO;2
  • Daley, R, and E. Barker. 2001. NAVDAS: formulation and diagnostics. Monthly Weather Rev. 129:869–83. doi:10.1175/1520-0493(2001)129<0869:NFAD>2.0.CO;2
  • Dee, D.P., and A.M. da Silva, 1999a. Maximum likelihood estimation of forecast and observation error covariance parameters. Part I. Methodology. Monthly Weather Rev. 127:1822–34. doi:10.1175/1520-0493(1999)127%3C1822:MLEOFA%3E2.0.CO;2
  • Dee, D.P., and A.M. da Silva, 1999b. Maximum likelihood estimation of forecast and observation error covariance parameters. Part II. Applications. Monthly Weather Rev. 127:1835–49. doi:10.1175/1520-0493(1999)127%3C1835:MLEOFA%3E2.0.CO;2
  • Desroziers, G., L. Berre, B. Chapnik, and P. Poli. 2005. Diagnostic of observation, background and analysis-error statistics in observation space. Q. J. R. Meteorol. Soc. 131:3385–96. doi:10.1256/qj.05.108.doi:10.1256/qj.05.108
  • Elbern, H., and H. Schmidt. 2001. Ozone episode analysis for four-dimensional variational chemistry data assimilation. J. Geophys. Res. 106(D4):3569–90. doi:10.1029/2000JD900448
  • Elbern, H., A. Strunk, H. Schmidt, and O. Talagrand. 2007. Emission rate and chemical state estimation by 4-dimensional variational inversion. Atmos. Chem. Phys. 7:3749–69. www.atmos-chem-phys.net/7/3749/2007. doi:10.5194/acp-7-3749-2007
  • Frydendall, J., J. Brandt, and J.H. Christensen. 2009. Implementation and testing of a simple data assimilation algorithm in the regional air pollution forecast model, DEOM. Atmos. Chem. Phys. 9:5475–88. www.atmos-chem-phys.net/9/5475/2009. doi:10.5194/acp-9-5475-2009
  • Fisher, M. 2003. Background error covariance modeling. Proceedings of the ECMWF Seminar on recent developments in data assimilation for atmosphere and ocean, September 8–12, 2003. Reading, UK: ECMWF.
  • Gaspari, G., and S.E. Cohn, 1999. Construction of correlation functions in two and three dimensions. Q. J. R. Meteorol. Soc. 125:723–57. doi:10.1002/(ISSN)1477-870X
  • Gneiting, T. 2002. Compactly supported correlation functions. J. Multivariate Anal. 83:493–508. doi:10.1006/jmva.2001.2056
  • Hoelzemann, H. Elbern, and A. Ebel. 2001. PSAS and 4D-var data assimilation for chemical state analysis by urban and rural observation sites. Phys. Chem. Earth (B) 26:807–812. doi:10.1016/S1464-1909(01)00089-2
  • Hollingsworth, A., and P. Lönnberg. 1986. The statistical structure of short-range forecast errors as determined from radiosonde data. Part I: The wind field. Tellus A 38:111–36. doi:10.1111/j.1600-0870.1986.tb00460.x
  • Houtekamer, P.L, and H.L. Mitchell. 2005. Ensemble Kalman filtering. Q. J. R. Meteorol. Soc. 131:3269–89. doi:10.1256/qj.05.135
  • Lee, P., and Y. Liu. 2014. Preliminary evaluation of a regional atmospheric chemical data assimilation systems for environmental surveillance. Int. J. Environ. Res. Public Health 11:12795–816. doi:10.3390/ijerph111212795
  • Lupton, R. 1993: Statistics in Theory and Practice. Princeton, NJ: Princeton University Press.
  • Ménard, R. 2016. Error covariance estimation methods based on analysis residuals: Theoretical foundation and convergence properties derived from simplified observation networks. Q. J. R. Meteorol. Soc. 142:257–273. doi:10.1002/qj.2650.
  • Ménard, R., and L.P. Chang. 2000. Assimilation of stratospheric chemical tracer observations using a Kalman filter. Part II: χ2-validated results and analysis of variance and correlation dynamics. Monthly Weather Rev. 128:2672–86. doi:10.1175/1520-0493(2000)128<2672:AOSCTO>2.0.CO;2
  • Ménard, R., L.P. Chang and J.W. Larson. 2000. Application of a robust χ2 validation diagnostic in PSAS and Kalman filtering experiments. In Proceedings of the Third WMO International Symposium on Assimilation of Observations in Meteorology and Oceanography, WWRP Report Series No.2. WMO/TD – No 986. 404 pp. Quebec City, Canada, 7–11 June 1999. World Meteorological Organization.
  • Ménard, R., and A. Robichaud. 2005. The chemistry-forecast system at the Meteorological Service of Canada. In ECMWF Seminar Proceedings on Global Earth-System Monitoring, September 5–9, 2005, Reading, UK, 297–308.
  • Mitchell, H.L., and P.L. Houtekamer. 2000. An adaptative ensemble Kalman filter. Monthly Weather Rev. 128:416–33. doi:10.1175/1520-0493(2000)128<0416:AAEKF>2.0.CO;2
  • Moran, M.D., S. Ménard, R. Pavlovic, D. Anselmo, S. Antonopoulus, A. Robichaud, S. Gravel, P.A. Makar, W. Gong, C. Stroud, J. Zhang, Q. Zheng, H. Landry, P.A. Beaulieu, S. Gilbert, J. Chen, and A. Kallaur. 2012. Recent advances in Canada’s national operational air quality forecasting system. 32nd NATO-SPS ITM, Utrecht, NL, May 7–11, 2012.
  • Pagowski, M., G.A. Grell, S.A. McKeen, S.E. Peckham and D. Devenyi. 2010. 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. R. Meteorol. Soc. 136:2013–24. doi:10.1002/qj.700
  • Pannekoucke, O., L. Berre, and G. Desroziers. 2008. Background-error correlation length-scale estimates and their sampling statistics. Q. J. R. Meteorol. Soc. 134:497–508. doi:10.1002/qj.212
  • Pannekoucke, O., E. Emili, and O. Thual. 2014. Modelling of local length-scale dynamics and isotropizing deformations. Q. J. R. Meteorol. Soc. 140:1387–98. doi:10.1002/qj.2204
  • Parrish, D.F., and J.C. Derber. 1992. The National Meteorological Center’s spectral statistical interpolation analysis system. Monthly Weather Rev. 120:1747–63. doi:10.1175/1520-0493(1992)120<1747:TNMCSS>2.0.CO;2
  • Priestley, M.B. 1981. Spectral Analysis and Time Series. Volume 1: Univariate Series. London, UK: Academic Press.
  • Robichaud, A., and R. Ménard. 2014. Multi-year objective analysis of warm season ground-level ozone and PM2.5 over North America using real-time observations and Canadian operational air quality models. Atmos. Chem. Phys. 14:1769–800. doi:10.5194/acp-14-1769-2014
  • Robichaud, A., R. Ménard, Y. Zaïtseva, and D. Anselmo. 2016. Multi-pollutant surface objective analyses and mapping of air quality health index over North America. Air Qual. Atmos. Health. in press. doi:10.1007s11869-015-0385-9.
  • Rösevall, J.D., D.P. Murtagh, J. Urban, and A.K. Jones. 2007. A study of polar ozone depletion based on sequential assimilation of satellite data from the ENVISAT/MIPAS and Odin/SMR instruments. Atmos. Chem. Phys. 7:899–911. www.atmos-chem-phys.net/7/899/2007/( accessed February 16, 2007). doi:10.5194/acp-7-899-2007
  • Rutherford, I. 1972. Data assimilation by statistical interpolation of forecast error fields. J. Atmos. Sci. 29:809–15. doi:10.1175/1520-0469(1972)029<0809:DABSIO>2.0.CO;2
  • Sandu, A., and T. Chai, 2011: Chemical data assimilation—An overview. Atmosphere 2:426–63. doi:10.3390/atmos2030426
  • Segers, A.J., H.J. Eskes, R.J. van der A, R.F. van Oss, and P.F.J. van Velthoven. 2005. Assimilation of GOME ozone profiles and a global chemistry-transport model using a Kalman filter with anisotropic covariance. Q. J. R. Meteorol. Soc. 131: 477–502. doi:10.1256/qj.04.92
  • Silibello, C., A. Bolingnano, R. Sozzi, and C. Gariazzo. 2014. Application of a chemical transport model and optimized data assimilation methods to improve air quality assessment. Air Qual. Atmos. Health. 7: 283–296. doi:10.1007/s11869-014-0235-1.
  • Singh, K, M. Jardak, A. Sandu, K. Bowman, M. Lee, and D. Jones. 2011. Construction of non-diagonal background error covariance matrices for global chemical data assimilation. Geosci. Model Dev. 4:299–316. doi:10.5194/gmd-4-299-2011
  • Štajner, I., L.P. Riishøjgaard, and R.B. Rood. 2001. The GEOS ozone data assimilation system: Specification of error statistics. Q. J. R. Meteorol. Soc. 127:1069–94. doi:10.1002/(ISSN)1477-870X
  • Stieb, D.M., R.T. Burnett, M. Smith-Dorion, O. Brion, H.H. Shin, and V. Economou. 2008. A new multipollutant, no-threshold air quality health index based on short-term associations observed in a daily time-series analyses. J. Air Waste Manage. Assoc. 58:435–50. doi:10.3155/1047-3289,58.3,3,435
  • Talagrand, O. 1998. A posteriori evaluation and verification of analysis and assimilation algorithms. Proceedings of the Workshop on Diagnostics of Data Assimilation Systems, pp. 17–28, ECMWF, England, November 2–4, 1998.
  • Tanborn, A., R. Ménard, and D. Ortland. 2002. Bias correction and random error characterization for the assimilation of high-resolution Doppler imager line of sight velocity measurements. J. Geophys. Res. 107(D12): 4147. doi: 10.1029/2001JD000397
  • Tarantola, A. 1987. Inverse Problem Theory: Methods for Data Fitting and Model Parameter Estimation. Amsterdam, The Netherlands: Elsevier.
  • Tilmes, S. 2001. Quantitative estimation of surface ozone observation and forecast errors. Phys. Chem. Earth (B) 26:759–62. doi:10.1016/S1464-1909(01)00082-X
  • Tombette, M., V. Mallet, and B. Sportisse. 2009. PM10 data assimilation over Europe with optimal interpolation method. Atmos. Chem. Phys. 9:57–70. www.atmos-chem-phys.net/9/57/2009 ( accessed January 7, 2009).
  • Waller, J.A., S.L. Dance, and N.K. Nichols. 2015. Theoretical insight in diagnosing observation error correlations using observed-minus-background and observed-minus analysis statistics. Q. J. R. Meteorol. Soc. doi:10.1002/qj.2661 ( accessed October 26, 2015).
  • Wu, L. V. Mallet, M. Bocquet, and B. Sportisse. 2008. A comparison study of data assimilation algorithms for ozone forecasting. J. Geophys. Res. 113(D20310). doi:10.1029/2008JD009991.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.