ACP Atmospheric Chemistry and Physics ACP 1680-7324 Copernicus GmbH Göttingen, Germany 10.5194/acp-9-5475-2009 Implementation and testing of a simple data assimilation algorithm in the regional air pollution forecast model, DEOM Frydendall J. 1 Brandt J. 2 Christensen J. H. 2 DTU Informatic, Technical University of Denmark, 2800 Kgs. Lyngby, Denmark National Environmental Research Institute, Aarhus University, 4000 Roskilde, Denmark 04 08 2009 9 15 5475 5488 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/9/5475/2009/acp-9-5475-2009.html The full text article is available as a PDF file from http://www.atmos-chem-phys.net/9/5475/2009/acp-9-5475-2009.pdf

A simple data assimilation algorithm based on statistical interpolation has been developed and coupled to a long-range chemistry transport model, the Danish Eulerian Operational Model (DEOM), applied for air pollution forecasting at the National Environmental Research Institute (NERI), Denmark. In this paper, the algorithm and the results from experiments designed to find the optimal setup of the algorithm are described. The algorithm has been developed and optimized via eight different experiments where the results from different model setups have been tested against measurements from the EMEP (European Monitoring and Evaluation Programme) network covering a half-year period, April–September 1999. The best performing setup of the data assimilation algorithm for surface ozone concentrations has been found, including the combination of determining the covariances using the Hollingsworth method, varying the correlation length according to the number of adjacent observation stations and applying the assimilation routine at three successive hours during the morning. Improvements in the correlation coefficient in the range of 0.1 to 0.21 between the results from the reference and the optimal configuration of the data assimilation algorithm, were found. The data assimilation algorithm will in the future be used in the operational THOR integrated air pollution forecast system, which includes the DEOM.

 References %Andersson, E., Hollingsworth, A., Kelly, G., Lonnberg, P., Pailleux, J., and %Zhang, Z.: Global Observing System Experiments on Operational Statistical %Retrievals of Satellite Sounding Data, Mon. Weather Rev., 119(8), 1851–1864, %1991. Balgovind, R., Dalcher, A., Ghil, M., and Kalnay, E.: A Stochastic-Dynamic Model for the Spatial Structure of Forecast Error Statistics, Mon. Weather Rev., 111(4), 701–722, 1983. Blond, N. and Vautard, R.: Three-dimensional ozone analyses and their use for short-term ozone forecasts, J. Geophys. Res., 109(17), D17303, doi:10.1029/2004JD004515, 2004. Blond, N., Bel, L., and Vautard, R.: Three-dimensional ozone data analysis with an air quality model over the Paris area, J. Geophys. Res., 108(D23), 4744, doi:10.1029/2003JD003679, 2003. Bouttier, F. and Courtier, P.: Data assimilation concepts and methods, Tech. rep., ECMWF training course lecture notes, 59~pp., 1998. Brandt, J., Christensen, J., Frohn, L., Berkowicz, R., and Palmgren, F.: The DMU-ATMI THOR Air Pollution Forecast System, System Description, 2000. Brandt, J., Christensen, J. H., Frohn, L. M., and Berkowicz, R.: Operational air pollution forecasts from regional scale to urban street scale. Part 1: system description, Phys. Chem. Earth Pt B, 26, 781–786, 2001a. Brandt, J., Christensen, J. H., Frohn, L. M., and Berkowicz, R.: Operational air pollution forecasts from regional scale to urban street scale. Part 2: performance evaluation, Phys. Chem. Earth Pt B, 26, 825–830, 2001b. Brandt, J., Christensen, J. H., Frohn, L. M., Palmgren, F., Berkowicz, R., and Zlatev, Z.: Operational air pollution forecasts from European to local scale, Atmos. Environ., 35, 91–98, 2001c. Brandt, J., Christensen, J., Frohn, L. M., Berkowicz, R., Skjøth, C. A., Geels, C., Hansen, K. M., Frydendall, J., Hedegaard, G. B., Hertel, O., Jensen, S. S., Hvidberg, M., Ketzel, M., Olesen, H. R., Løfstrøm, P., and Zlatev, Z.: THOR – an operational and integrated model system for air pollution forecasting and management from global to local scale, in: Proceedings from the First ACCENT Symposium, Urbino, Italy, 12th–16th September 2005, 6~pp., ACCENT, 2005. Burges, G., van Leeuwen, P. J., and Evensen, G.: Analysis Scheme in the Ensemble Kalman Filter, American Meteorological Society, Mon. Weather Rev., 126(6), 1719–1724, 1998. Daley, R.: Atmospheric Data Analysis, Cambridge University Press, 1996. Denby, B., Kahnert, M., Brandt, J., Frydendall, J., and Zlatev, Z.: Development and application of data assimilation in regional scale atmospheric chemistry models, Tech. Rep. NILU O 30/2007, Ref:~O-105077, 47~pp., April 2008. Elbern, H. and Schmidt, H.: A four-dimensional variational chemistry data assimilation scheme for Eulerian chemistry transport modeling, J. Geophys. Res.-Atmos., 104, 18 583–18 598, 1999. Elbern, H., Schmidt, H., and Ebel, A.: Variational data assimilation for tropospheric chemistry modeling, J. Geophys. Res.-Atmos., 102, 15 967–15 985, 1997. Elbern, H., Schmidt, H., Talagrand, O., and Ebel, A.: 4D-variational data assimilation with an adjoint air quality model for emission analysis, Environ. Modell. Softw., 15, 539–548, 2000. Evensen, G.: Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res., 99, 10 143–10 162, 1994. Gandin, L. S.: Objective Analysis of Meteorological Fields, Program Scientific Translalions, 1963. Gaspari, G. and Cohn, S. E.: Construction of correlation functions in two and three dimensions, Q. J. Roy. Meteor. Soc., 125, 723–757, 1999. Hamill, T. M., Whitaker, J. S., and Snyder, C.: Distance-Dependent Filtering of Background Error Covariance Estimates in an Ensemble Kalman Filter, Mon. Weather Rev., 129(11), 2776–2790, 2001. Hertel, O., Ellermann, T., Palmgren, F., Berkowicz, R., Løfstrøm, P., Frohn, L. M., Geels, C., Skjøth, C. A., Brandt, J., Christensen, J., Kemp, K., and Ketzel, M.: Integrated Air Quality Monitoring – Combined use of measurements and models in monitoring programmes, Environ. Chem., 4, 65–74, 2007. Hesstvedt, E., Hov, Ø., and Isaksen, I. S. A.: Quasi-Steady-State Approximations in Air-Pollution Modeling – Comparison of Two Numerical Schemes for Oxidant Prediction, Int. J. Chem. Kinet., 10, 971–994, 1978. Hoelzemann, J. J., Elbern, H., and Ebel, A.: PSAS and 4D-var data assimilation for chemical state analysis by urban and rural observation sites, Phys. Chem. Earth Pt B, 26, 807–812, 2001. Hollingsworth, A. and Lönnberg, P.: The Statistical Structure of Short-Range Forecast Errors As Determined from Radiosonde Data. Part~I: The Wind-Field, Tellus~A, 38, 111–136, 1986. Houtekamer, P. L. and Herschel, M. L.: Data Assimilation Using an Ensemble Kalman Filter Technique, Mon. Weather Rev., 126(3), 796–811, 1998. Ide, K., Courtier, P., Ghil, M., and Lorenc, A. C.: Unified Notation for Data Assimilation: Operational, Sequential and Variational, Special Issue on Data Assimilation in Meteorology and Oceanography: Theory and Practice, J. Meteorol. Soc. Jpn., 75, 181–189, 1997. Kang, D., Mathur, R., Rao, T. S., and Yu, S.: Bias adjustment techniques for improving ozone air quality forecasts, J. Geophys. Res., 113, D23308, doi:10.1029/2008JD010151, 2008. Lamarque, J. F., Khattatov, B. V., Gille, J. C., and Brasseur, G. P.: Assimilation of Measurement of Air Pollution from Space (MAPS) CO in a global three-dimensional model, J. Geophys. Res.-Atmos., 104, 26 209–26 218, 1999. Lawrence, M. G., Hov, Ø., Beekmann, M., Brandt, J., Elbern, H., Eskes, H., Feichter, H., and Takigawa, M.: The Chemical Weather, Environ. Chem., 2, 6–8, 2005. Lönnberg, P. and Hollingsworth, A.: The Statistical Structure of Short-Range Forecast Errors As Determined from Radiosonde Data. Part~II: The Covariance of Height and Wind Errors, Tellus~A, 38, 137–161, 1986. McRae, G. J., Goodin, W. R., and Seinfeld, J. H.: Numerical-Solution of the Atmospheric Diffusion Equation for Chemically Reacting Flows, J. Comput. Phys., 45, 1–42, 1982. M'enard, R. and Chang, L. P.: Assimilation of Stratospheric Chemical Tracer Observations Using a Kalman Filter. Part II: chi2-Validated Results and Analysis of Variance and Correlation Dynamics, Mon. Weather Rev., 128, 2672–2686, 2000. M'enard, R., Cohn, S. E., Chang, L. P., and Lyster, P. M.: Assimilation of Stratospheric Chemical Tracer Observations Using a Kalman Filter. Part~I: Formulation, Mon. Weather Rev., 128, 2654–2671, 2000. Tilmes, S., Brandt, J., Flatoy, F., Bergstrom, R., Flemming, J., Langer, J., Christensen, J. H., Frohn, L. M., Hov, O., Jacobsen, I., Reimer, E., Stren, R., and Zimmermann, J.: Comparison of five eulerian air pollution forecasting systems for the summer of 1999 using the german ozone monitoring data, J. Atmos. Chem., 42, 91–121, 2002. van Loon, M. and Heemink, A. W.: Kalman filtering for nonlinear atmospheric chemistry models: first experiences, Tech. Rep. MAS-R9711, Centrum voor Wiskunde en Informatica, 1997. Zlatev, Z. and Brandt, J.: Testing Variational Data Assimilation Modules, Springer-Verlag, 395–402, 2005. Zlatev, Z. and Brandt, J.: Testing the accuracy of a data assimilation algorithm, International Journal of Computational Science and Engineering, 3, 305–313, 2007.