Data assimilation in atmospheric chemistry models: current status and future prospects for coupled chemistry meteorology models

Copernicus GmbH - Tập 15 Số 10 - Trang 5325-5358
Marc Bocquet1,2, Hendrik Elbern3, Henk Eskes4, Marcus Hirtl5, Rahela Žabkar6, Gregory R. Carmichael7, Johannes Flemming8, Antje Inness8, Mariusz Pagowski9, Juan L. Pérez10, Pablo E. Saide7, Roberto San José10, Mikhail Sofiev11, Julius Vira11, Alexander Baklanov12, Claudio Carnevale13, G. A. Grell9, Christian Seigneur14
1CEREA, Joint Laboratory École des Ponts ParisTech/EDF R&D, Université Paris-Est, Marne-la-Vallée, France
2INRIA, Paris Rocquencourt Research Center, Rocquencourt, France
3Institute for Physics and Meteorology, University of Cologne, Cologne, Germany
4KNMI, De Bilt, The Netherlands
5Central Institute for Meteorology and Geodynamics, Vienna, Austria
6Faculty of Mathematics and Physics, University of Ljubljana, Ljubljana, Slovenia
7Center for Global and Regional Environmental Research, University of Iowa, Iowa City, USA
8European Centre for Medium-Range Weather Forecasts, Reading, UK
9NOAA/ESRL, Boulder, Colorado, USA
10Technical University of Madrid (UPM), Madrid, Spain
11Finnish Meteorological Institute, Helsinki, Finland
12World Meteorological Organization (WMO), Geneva, Switzerland and Danish Meteorological Institute (DMI), Copenhagen, Denmark
13Department of Mechanical and Industrial Engineering, University of Brescia, Brescia, Italy
14CEREA, Joint Laboratory École des Ponts ParisTech - EDF R&D, Université Paris-Est, Marne la Vallée, France

Tóm tắt

Abstract. Data assimilation is used in atmospheric chemistry models to improve air quality forecasts, construct re-analyses of three-dimensional chemical (including aerosol) concentrations and perform inverse modeling of input variables or model parameters (e.g., emissions). Coupled chemistry meteorology models (CCMM) are atmospheric chemistry models that simulate meteorological processes and chemical transformations jointly. They offer the possibility to assimilate both meteorological and chemical data; however, because CCMM are fairly recent, data assimilation in CCMM has been limited to date. We review here the current status of data assimilation in atmospheric chemistry models with a particular focus on future prospects for data assimilation in CCMM. We first review the methods available for data assimilation in atmospheric models, including variational methods, ensemble Kalman filters, and hybrid methods. Next, we review past applications that have included chemical data assimilation in chemical transport models (CTM) and in CCMM. Observational data sets available for chemical data assimilation are described, including surface data, surface-based remote sensing, airborne data, and satellite data. Several case studies of chemical data assimilation in CCMM are presented to highlight the benefits obtained by assimilating chemical data in CCMM. A case study of data assimilation to constrain emissions is also presented. There are few examples to date of joint meteorological and chemical data assimilation in CCMM and potential difficulties associated with data assimilation in CCMM are discussed. As the number of variables being assimilated increases, it is essential to characterize correctly the errors; in particular, the specification of error cross-correlations may be problematic. In some cases, offline diagnostics are necessary to ensure that data assimilation can truly improve model performance. However, the main challenge is likely to be the paucity of chemical data available for assimilation in CCMM.

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Tài liệu tham khảo

Abida, R. and Bocquet, M.: Targeting of observations for accidental atmospheric release monitoring, Atmos. Environ., 43, 6312–6327, 2009.

Adhikary, B., Kulkarni, S., Dallura, 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, https://doi.org/10.1016/j.atmosenv.2008.08.031, 2008.

Anderson, J. L. and Anderson, S. L.: A Monte Carlo implementation of the nonlinear filtering problem to produce ensemble assimilations and forecasts, Mon. Weather Rev., 127, 2741–2758, 1999.

Aumann, H. H., Chahine, M. T., Gautier, C., Goldberg, M. D., Kalnay, E., McMillin, L. M., Revercomb, H., Rosenkranz, P. W., Smith, W. L., Staelin, D. H., Strow, L. L., and Susskind, J.: AIRS/AMSU/HSB on the Aqua mission: Design, science objectives, data products, and processing systems, IEEE Trans. Geosci. Remote Sens., 41, 253–264, 2003.

Baklanov, A., Schlünzen, K., Suppan, P., Baldasano, J., Brunner, D., Aksoyoglu, S., Carmichael, G., Douros, J., Flemming, J., Forkel, R., Galmarini, S., Gauss, M., Grell, G., Hirtl, M., Joffre, S., Jorba, O., Kaas, E., Kaasik, M., Kallos, G., Kong, X., Korsholm, U., Kurganskiy, A., Kushta, J., Lohmann, U., Mahura, A., Manders-Groot, A., Maurizi, A., Moussiopoulos, N., Rao, S. T., Savage, N., Seigneur, C., Sokhi, R. S., Solazzo, E., Solomos, S., Sørensen, B., Tsegas, G., Vignati, E., Vogel, B., and Zhang, Y.: Online coupled regional meteorology chemistry models in Europe: current status and prospects, Atmos. Chem. Phys., 14, 317–398, https://doi.org/10.5194/acp-14-317-2014, 2014.

Barbu, A. L., Segers, A. J., Schaap, M., Heemink, A. W., and Builtjes, P. J. H.: A multi-component data assimilation experiment directed to sulphur dioxide and sulphate over Europe, Atmos. Environ., 43, 1622–1631, 2009.

Barnes, W. L., Pagano, T. S., and Salomonson, V. V.: Prelaunch characteristics of the moderate resolution imaging spectroradiometer (MODIS) on EOS-AM1, IEEE Trans. Geosci. Remote Sens., 36, 1088–1100, 1998.

Beer, R., Glavich, T. A., and Rider, D. M.: Tropospheric emission spectrometer for the Earth Observing System's Aura satellite, Appl. Optics, 40, 2356–2367, 2001.

Bellouin, N., Quaas, J., Morcrette, J.-J., and Boucher, O.: Estimates of aerosol radiative forcing from the MACC re-analysis, Atmos. Chem. Phys., 13, 2045–2062, https://doi.org/10.5194/acp-13-2045-2013, 2013.

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., Suttie, M., and the GEMS-AER team: Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: Data Assimilation, J. Geophys. Res., 114, D13205, https://doi.org/10.1029/2008JD011115, 2008.

Benedetti, A., Jones, L. T., Inness, A., Kaiser, J. W., and Morcrette, J.-J.: Global climate Aerosols, in: State of the Climate in 2012, Bull. Amer. Meteor. Soc., 94, S34–S36, 2013.

Berliner, L. M., Lu, Z. Q., and Snyder, C.: Statistical design for Adaptive Weather Observations, J. Atmos Sci., 56, 2536–2552, 1999.

Bocquet, M., Pires, C. A., and Wu, L.: Beyond Gaussian statistical modeling in geophysical data assimilation, Mon. Weather Rev., 138, 2997–3023, 2010.

Bocquet, M.: Parameter field estimation for atmospheric dispersion: Applications to the Chernobyl accident using 4D-Var, Q. J. Roy. Meteorol. Soc., 138, 664–681, 2012.

Bocquet, M. and Sakov, P.: Joint state and parameter estimation with an iterative ensemble Kalman smoother, Nonlin. Processes Geophys., 20, 803–818, https://doi.org/10.5194/npg-20-803-2013, 2013.

Boersma, K. F., Eskes, H. J., Dirksen, R. J., van der A, R. J., Veefkind, J. P., Stammes, P., Huijnen, V., Kleipool, Q. L., Sneep, M., Claas, J., Leitão, J., Richter, A., Zhou, Y., and Brunner, D.: An improved tropospheric NO2 column retrieval algorithm for the Ozone Monitoring Instrument, Atmos. Meas. Tech., 4, 1905–1928, https://doi.org/10.5194/amt-4-1905-2011, 2011.

Borrego, C., Coutinho, M., Costa, A. M., Ginja, J., Ribeiro, C., Monteiro, A., Ribeiro, I., Valente, J., Amorim, J. H., Martins, H., Lopes, D., Miranda, A. I.: Challenges for a new air quality directive: the role of monitoring and modelling techniques, Urban Climate, https://doi.org/10.1016/j.uclim.2014.06.007, in press, 2015.

Bovensmann, H., Burrows, J. P., Buchwitz, M., Frerick, J., Noël, S., Rozanov, V. V., Chance, K. V., and Goede, A. P. H.: SCIAMACHY: Mission Objectives and Measurement Modes. J. Atmos. Sci., 56, 127–150, https://doi.org/10.1175/1520-0469, 1999.

Brandt, J., Christensen, J. H., and Frohn, L. M.: Modelling transport and deposition of caesium and iodine from the Chernobyl accident using the DREAM model, Atmos. Chem. Phys., 2, 397–417, https://doi.org/10.5194/acp-2-397-2002, 2002.

Buehner, M. P., Houtekamer, I., Charette, C., Mitchell, H. L., and He, B.: Intercomparison of variational data assimilation and the ensemble Kalman filter for global determinst-ic N.W.P., Part I. Description and single-observation experiments, Mon. Weather Rev., 138, 1550–1566, 2010a.

Buehner, M. P., Houtekamer, I., Charette, C., Mitchell, H. L., and He, B.: Intercomparison of variational data assimilation and the ensemble Kalman filter for global determinstic N.W.P., Part II. One-month experiments with real observations, Mon. Weather Rev., 138, 1567–1586, 2010b.

Buizza, R., Miller, M., and Palmer, T. N.: Stochastic representation of model uncertainties in the ECMWF ensemble prediction system, Q. J. Roy. Meteorol. Soc., 125, 2887–2908, 1999.

Callies, J., Corpaccioli, E., Eisinger, M., Hahne, A., and Lefebvre, A.: GOME-2-Metop's second-generation sensor for operational ozone monitoring, ESA bulletin, 102, 28–36, 2000.

Candiani, G., Carnevale, C., Finzi, G., Pisoni, E., and Volta, M.: A comparison of reanalysis techniques: Applying optimal interpolation and Ensemble Kalman Filtering to improve air quality monitoring at mesoscale, Sci. Total Environ., 458–460, 7–14, 2013.

Carmichael, G. R., Sandu, A., Chai, T., Daescu, D., Constantinescu, E., and Tang, Y.: Predicting air quality: Improvements through advanced methods to integrate models and measurements, J. Comp. Phys., 227, 3540–3571, 2008.

Carmichael, G. R., Adhikary, B., Kulkarni, S., D'Allura, A., Tang, Y., Streets, D., Zhang, Q., Bond, T. C., Ramanathan, V., and Jamroensan, A.: Asian aerosols: current and year 2030 distributions and implications to human health and regional climate change, Environ. Sci. Technol., 43, 5811–5817, 2009.

Carnevale, C., Decanini, E., and Volta, M.: Design and validation of a multiphase 3D model to simulate tropospheric pollution, Sci. Total Environ., 390, 166–176, 2008.

Cathala, M.-L., Pailleux, J., and Peuch, V.-H.: Improving global chemical simulations of the upper troposphere–lower stratosphere with sequential assimilation of MOZAIC data, Tellus B, 55, 1–10, https://doi.org/10.1034/j.1600-0889.2003.00002.x, 2003.

CEOS-ACC: A Geostationary Satellite Constellation for Observing Global Air Quality: An International Path Forward, Prepared by the CEOS Atmospheric Composition Constellation, Draft Version 4.0, 12 April 2011.

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, https://doi.org/10.1029/2006JD007763, 2006.

Chai, T., Carmichael, G. R., Tang, Y., Sandu, A., Hardesty, M., Pilewskie, P., Whitlow, S., Browell, E. V., Avery, M. A., Nédélec, P., Merrill, J. T., Thompson, A. M., and Williams, E.: Four dimensional data assimilation experiments with International Consortium for Atmospheric Research on Transport and Transformation ozone measurements, J. Geophys. Res., 112, D12S15, https://doi.org/10.1029/2006JD007763, 2007.

Chance, K., Liu, X., Suleiman, R. M., Flittner, D. E., Al-Saadi, J., and Janz, S. J.: Tropospheric emissions: monitoring of pollution (TEMPO), in: SPIE Optical Engineering +t Applications, International Society for Optics and Photonics, 88660D–88660D, September 2013.

Chapnik, B., Desroziers, G., Rabier, F., and Talagrand, O.: Properties and first application of an error-statistics tuning method in variational assimilation, Q. J. Roy. Meteorol. Soc., 130, 2253–2275, 2004.

Chazette, P., Bocquet, M., Royer, P., Winiarek, V., Raut, J.-C., Labazuy, P., Gouhier, M., Lardier, M., and Cariou, J.-P.: Eyjafjallajökull ash concentrations derived from both lidar and modeling, J. Geophys. Res., 117, D00U14, https://doi.org/10.1029/2011JD015755, 2012.

Chen, D., Liu, Z., Schwartz, C. S., Lin, H.-C., Cetola, J. D., Gu, Y., and Xue, L.: The impact of aerosol optical depth assimilation on aerosol forecasts and radiative effects during a wild fire event over the United States, Geosci. Model Dev., 7, 2709–2715, https://doi.org/10.5194/gmd-7-2709-2014, 2014.

Chin, M., Rood, R. B., Lin, S.-J., Muller, J.-F., and Thompson, A. M.: Atmospheric sulfur cycle simulated in the global model GOCART: Model description and global properties, J. Geophys. Res., 105, 24671–24687, https://doi.org/10.1029/2000JD900384, 2000.

Chin, M., Ginoux, P., Kinne, S., Torres, O., Holben, B. N., Duncan, B. N., Martin, R. V., Logan, J. A., and Higurashi, A.: Tropospheric aerosol optical thickness from the GOCART model and comparisons with satellite and Sun photometer measurements, J. Atmos. Sci., 59, 461–483, 2002.

Clerbaux, C., Boynard, A., Clarisse, L., George, M., Hadji-Lazaro, J., Herbin, H., Hurtmans, D., Pommier, M., Razavi, A., Turquety, S., Wespes, C., and Coheur, P.-F.: Monitoring of atmospheric composition using the thermal infrared IASI/MetOp sounder, Atmos. Chem. Phys., 9, 6041–6054, https://doi.org/10.5194/acp-9-6041-2009, 2009.

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, https://doi.org/10.1029/2000jd900507, 2001.

Coman, A., Foret, G., Beekmann, M., Eremenko, M., Dufour, G., Gaubert, B., Ung, A., Schmechtig, C., Flaud, J.-M., and Bergametti, G.: Assimilation of IASI partial tropospheric columns with an Ensemble Kalman Filter over Europe, Atmos. Chem. Phys., 12, 2513–2532, https://doi.org/10.5194/acp-12-2513-2012, 2012.

Constantinescu, E. M., Sandu, A., Chai, T., and Carmichael, G. R.: Ensemble-based chemical data assimilation. i: General approach, Q. J. Roy. Meteorol. Soc., 133, 1229–1243, 2007a.

Constantinescu, E. M., Sandu, A., Chai, T., and Carmichael, G. R.: Ensemble-based chemical data assimilation. ii: Covariance localization, Q. J. Roy. Meteorol. Soc., 133, 1245–1256, 2007b.

Courtier, P., Thépaut, J.-N., and Hollingsworth, A.: A strategy for operational implementation of 4D-Var, using an incremental approach, Q. J. Roy. Meteorol. Soc., 120, 1367–1388, 1994.

Curier, R. L., Timmermans, R., Calabretta-Jongen, S., Eskes, H., Segers, A., Swart, D., and Schaap, M.: Improving ozone forecasts over Europe by synergistic use of the LOTOS-EUROS chemical transport model and in-situ measurements, Atmos. Environ., 60, 217–226, 2012.

Daley, R.: Atmospheric Data Analysis, Cambridge University Press, 1991.

Davoine, X. and Bocquet, M.: Inverse modelling-based reconstruction of the Chernobyl source term available for long-range transport, Atmos. Chem. Phys., 7, 1549–1564, https://doi.org/10.5194/acp-7-1549-2007, 2007.

Dee, D. P. and Uppala, S.: Variational bias correction of satellite radiance data in the ERA-Interim reanalysis, Q. J. Roy. Meteorol. Soc., 135, 1830–1841, 2009.

Dee, D. P., Uppala, S. M., Simmons, A. J., Berrisford, P., Poli, P., Kobayashi, S., Andrae, U., Balmaseda, M. A., Balsamo, G., Bauer, P., Bechtold, P., Beljaars, A. C. M., van de Berg, L., Bidlot, J., Bormann, N., Delsol, C., Dragani, R., Fuentes, M., Geer, A. J., Haimberger, L., Healy, S. B., Hersbach, H., Hólm, E. V., Isaksen, L., Kållberg, P., Köhler, M., Matricardi, M., McNally, A. P., Monge-Sanz, B. M., Morcrette, J.-J., Park, B.-K., Peubey, C., de Rosnay, P., Tavolato, C., Thépaut, J.-N., and Vitarta, F.: The ERA-Interim reanalysis: Configuration and performance of the data assimilation system, Q. J. Roy. Meteorol. Soc., 137, 553–597, 2011.

Desroziers, G., Berre, L., Chapnik, B., and Poli, P.: Diagnosis of observation, background and analysis error statistics in observation space, Q. J. Roy. Meteorol. Soc., 131, 3385–3396, 2005.

Desroziers, G. and Ivanov, S.: Diagnosis and adaptive tuning of observationerror parameters in a variational assimilation, Q. J. Roy. Meteorol. Soc. 127, 1433–1452, 2001.

Dethof, A. and Hólm, E.V.: Ozone assimilation in the ERA-40 reanalysis project, Q. J. Roy. Meteorol. Soc., 130, 2851–2872, 2004.

Diner, D. J., Abdou, W. A., Bruegge, C. J., Conel, J. E., Crean, K. A., Gaitley, B. J., Helmlinger, M. C., Kahn, R. A., Martonchik, J. V., Pilorz, S. H., and Holben, B. N.: MISR aerosol optical depth retrievals over southern Africa during the SAFARI-2000 dry season campaign, Geophys. Res. Lett., 28, 3127–3130, 2001.

Dragani, R.: On the quality of the ERA-Interim ozone reanalyses: comparisons with satellite data, Q. J. Roy. Meteorol. Soc., 137m 1312–1326, https://doi.org/10.1002/qj.821, 2011.

Drummond, J. R. and Mand, G. S.: The Measurements of Pollution in the Troposphere (MOPITT) instrument: Overall performance and calibration requirements, J. Atmos. Ocean. Technol., 13, 314–320, 1996.

EEA: Air quality in Europe, 2013 report, EEA Report No 9/2013, 2013.

Eisele, F., Mauldin, L., Cartrell, C., Zondio, M., Apel, E., Fried, A., Walega, J., Sheffer, R., Lefer, B., Flocke, F., Weinheimer, A., Avery, M., Vay, S., Sachse, G., Podolske, J., Diskin, G., Barrick, J. D., Singh, H. B., Brune, W., Harder, H., Martinez, M., Bandy, A., Thornton, D., Heikes, B., Kondo, Y., Riemer, D., Sandholm, S., Tan, D., Talbot, R., and Dibb, J.: Summary of measurement intercomparisons during TRACE-P, J. Geophys. Res., 108, 8791, https://doi.org/10.1029/2002JD003167, 2003.

Elbern, H. and Schmidt, H.: A four-dimensional variational chemistry data assimilation scheme for Eulerian chemistry transport modeling, J. Geophys. Res., 104, 18583–18598, 1999.

Elbern, H. and Schmidt, H.: Ozone episode analysis by four-dimensional variational chemistry data assimilation, J. Geophys. Res., 106, 3569–3590, 2001.

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, https://doi.org/10.5194/acp-7-3749-2007, 2007.

EMEP: Transboundary Particulate Matter in Europe: EMEP Status Report 2012, edited by: Yttri, K. E., Aas, W., Tørseth, K. Kristiansen, N. I., Myhre, C. L., Tsyro, S., Simpson, D., Bergström, R., Marecková, K., Wankmüller, R., Klimont, Z., Amman, M., Kouvarakis, G. N., Laj, P., Pappalardo, G., and Prévôt, A., European Monitoring and Evaluation Programme Status Report 4/2012, 2012.

Emmons, L. K., Walters, S., Hess, P. G., Lamarque, J.-F., Pfister, G. G., Fillmore, D., Granier, C., Guenther, A., Kinnison, D., Laepple, T., Orlando, J., Tie, X., Tyndall, G., Wiedinmyer, C., Baughcum, S. L., and Kloster, S.: Description and evaluation of the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4), Geosci. Model Dev., 3, 43–67, https://doi.org/10.5194/gmd-3-43-2010, 2010.

Engelen, R. J. and Bauer, P.: The use of variable CO2 in the data assimilation of AIRS and IASI radiances, Q. J. Roy. Meteorol. Soc., 140, 958–965, https://doi.org/10.1002/qj.919, 2014.

Engelen R. J., Serrar, S., and Chevallier, F.: Four-dimensional data assimilation of atmospheric CO2 using AIRS observations, J. Geophys. Res., 114, D03303, https://doi.org/10.1029/2008JD010739, 2009.

Eskes, H. J. and Boersma, K. F.: Averaging kernels for DOAS total-column satellite retrievals, Atmos. Chem. Phys., 3, 1285–291, https://doi.org/10.5194/acp-3-1285-2003, 2003.

Evensen, G.: Sequential data assimilation with a nonlinear quasigeostrophic model using Monte Carlo methods to forecast error statistics, J. Geophys. Res., 99, 10143–10162, 1994.

Evensen, G.: Data Assimilation: The Ensemble Kalman Filter, 2nd Edn., Springer-Verlag, 2009.

Fedorov, V. V.: Kriging and other estimators of spatial field characteristics (with special reference to environmental studies), Atmos. Environ., 23, 175–184, 1989.

Fehsenfeld, F. C., Ancellet, G., Bates, T. S., Goldstein, A. H., Hardesty, R. M., Honrath, R., Law, K. S., Lewis, A. C., Leaitch, R., McKeen, S., Meagher, J., Parrish, D. D., Pszenny, A. A. P., Russell, P. B., Schlager, H., Seinfeld, J., Talbot, R., and Zbinden, R.: International Consortium for Atmospheric Research on Transport and Transformation (ICARTT): North America to Europe – Overview of the 2004 summer field study, J. Geophys. Res., 111, D23S01, https://doi.org/10.1029/2006JD007829, 2006.

Ferro, C. A. T. and Stephenson, D. B.: Extremal dependence indices: improved verification measures for deterministic forecasts of rare binary events, Weather Forecast., 26, 699–713, 2011.

Fioletov, V. E., McLinden, C. A., Krotkov, N., Moran, M. D., and Yang, K.: Estimation of SO2 emissions using OMI retrievals, Geophys. Res. Lett., 38, L21811, https://doi.org/10.1029/2011GL049402, 2011.

Fisher, M. and Lary, D. J.: Lagrangian four-dimensional variational data assimilation of chemical species, Q. J. Roy. Meteorol. Soc., 121, 1681–1704, 1995.

Fisher, M. and Andersson, E.: Developments in 4D-Var and Kalman Filtering. ECMWF Technical Memorandum 347, available from ECMWF, Shinfield Park, Reading, Berkshire, RG2 9AX, UK, 2001.

Fisher, M., Leutbecher, M., and Kelly, G. A.: On the equivalence between Kalman smoothing and weak-constraint four-dimensional variational data assimilation, Q. J. Roy. Meteorol. Soc., 131, 3235–3246, 2005.

Fishman, J., Bowman, K. W., Burrows, J. P., Richter, A., Chance, K. V., Edwards, D. P., Martin, R. V., Morris, G. A., Pierce, R. B., and Ziemke, J. R.: Remote sensing of tropospheric pollution from space, Bull. Am. Meteorol. Soc., 89, 805–822, 2008.

Flemming, J., Inness, A., Flentje, H., Huijnen, V., Moinat, P., Schultz, M. G., and Stein, O.: Coupling global chemistry transport models to ECMWF's integrated forecast system, Geosci. Model Dev., 2, 253–265, https://doi.org/10.5194/gmd-2-253-2009, 2009.

Flemming, J. and Inness, A.: Global climate Carbon monoxyde, in: State of the Climate in 2013, Bull. Am. Meteorol. Soc., 95, S43–S44, 2014.

Flemming, J., Inness, A., Jones, L., Eskes, H. J., Huijnen, V., Schultz, M. G., Stein, O., Cariolle, D., Kinnison, D., and Brasseur, G.: Forecasts and assimilation experiments of the Antarctic ozone hole 2008, Atmos. Chem. Phys., 11, 1961–1977, https://doi.org/10.5194/acp-11-1961-2011, 2011.

Fuentes, M., Chaudhuri, A., and Holland, D. M.: Bayesian entropy for spatial sampling design of environmental data, Environ. Ecol. Stat., 14, 323–340, 2007.

GAW: Global Atmosphere Watch (GAW) Programme: 25 years of global coordinated atmospheric composition observations and analysis, WMO, Geneva, Switzerland, 70 pp., 2014.

GCOS, Global Climate Observing System, implementation plan 2010, and satellite supplement, 2011, available at: http://www.wmo.int/pages/prog/gcos/documents/SatelliteSupplement2011Update.pdf (last access: 7 May 2015), 2011.

Generoso, S., Bréon, F. M., Chevallier, F., Balkanski, Y., Schulz, M., and Bey, I.: Assimilation of POLDER aerosol optical thickness into the LMDz-INCA model: Implications for the Arctic aerosol burden, J. Geophys. Res., 112, D02311, https://doi.org/10.1029/2005jd006954, 2007.

GEOSS: Global Earth Observation System of Systems, available at: http://www.earthobservations.org/geoss.shtml (last access: 7 May 2015), 2014.

Ghil, M. and Malanotte-Rizzoli, P.: Data assimilation in meteorological and oceanography, Adv. Geophys. 33, 141–266, 1991.

Gibson, J. K., Kallberg, P., Uppala, S. M., Nomura, A., Hernandez, A., and Serrano, E.: ERA description, ERA-15 Report Series, No.1, ECMWF, Reading, UK, 1997.

Giglio, L., Randerson, J. T., and van der Werf, G. R.: Analysis of daily, monthly, and annual burned area using the fourth-geneartion global fire emissions database (GFED4), J. Geophys. Res. Biogeosci., 118, 317–328, https://doi.org/10.1002/jgrg.20042, 2013.

Ginoux, P., Chin, M., Tegen, I., Prospero, J., Holben, B., Dubovik, O., and Lin, S.-J.: Sources and distributions of dust aerosols simulated with the GOCART model, J. Geophys. Res., 106, 20225–20273, https://doi.org/10.1029/2000JD000053, 2001.

Grell, G., 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, 6957–6975, https://doi.org/10.1016/j.atmosenv.2005.04.027, 2005.

GSFC: Joint Polar Satellite System (JPSS) VIIRS Aerosol Optical Thickness (AOT) and Particle Size Parameter Algorithm Theoretical Basis Document (ATBD), 2011.

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, 2776–2790, 2001.

Hanea, R. G., Velders, G. J. M., and Heemink, A. W.: Data assimilation of ground-level ozone in Europe with a Kalman filter and chemistry transport model, J. Geophys. Res., 109, D10302, https://doi.org/10.1029/2003JD004283, 2004.

Hitzenberger, R., Berner, A., Galambos, Z., Maenhaut, W., Cafmeyer, J., Schwarz, J., Müller, K., Spindler, G., Wieprecht, W., Acker, K., Hillamo, R., and Mäkelä, T.: Intercomparison of methods to measure the mass concentration of the atmospheric aerosol during INTERCOMP2000 – Influence of instrumentation and size cuts, Atmos. Environ., 38, 6467–6476, https://doi.org/10.1016/j.atmosenv.2004.08.025, 2004.

Holben, B., Tanré, D., Smirnov, A., Eck, T., Slutsker, I., Abuhassan, N., Newcomb, W., Schafer, J., Chatenet, B., and Lavenu, F.: An emerging ground-based aerosol climatology: Aerosol optical depth from AERONET, J. Geophys. Res., 106, 12067–12012, 2001.

Holben, B. N., Eck, T. F., Slutsker, I., Tanré, D., Buis, J. P., Setzer, A., Vermote, E., Reagan, J. A., Kaufman, Y. J., Nakajima, T., Lavenu, F., Jankowiak, I., and Smirnov, A.: AERONET – a federated instrument network and data archive for aerosol characterization, Remote Sens. Environ., 66, 1–16, 1998.

Hollingsworth, A. and Lönnberg, P.: The statistical structure of shortrange forecast errors as determined from radiosonde data: Part 1. The wind field, Tellus A, 38, 111–136, 1986.

Hollingsworth, A., Engelen, R. J., Textor, C., Benedetti, A., Boucher, O., Chevallier, F., Dethof, A., Elbern, H., Eskes, H., Flemming, J., Granier, C., Kaiser, J. W., Morcrette, J.-J., Rayner, R., Peuch, V.-H., Rouil, L., Schultz, M. G., Simmons, A. J. and The GEMS Consortium: Toward a Monitoring and Forecasting System For Atmospheric Composition: The GEMS Project, Bull. Am. Meteorol. Soc., 89, 1147–1164, https://doi.org/10.1175/2008BAMS2355.1, 2008.

Houtekamer, P. L. and Mitchell, H. L.: A sequential ensemble Kalman filter for atmospheric data assimilation, Mon. Weather Rev., 129, 123–137, 2001.

Huijnen, V., Flemming, J., Kaiser, J. W., Inness, A., Leitão, J., Heil, A., Eskes, H. J., Schultz, M. G., Benedetti, A., Hadji-Lazaro, J., Dufour, G., and Eremenko, M.: Hindcast experiments of tropospheric composition during the summer 2010 fires over western Russia, Atmos. Chem. Phys., 12, 4341–4364, https://doi.org/10.5194/acp-12-4341-2012, 2012.

IGACO 2004: An Integrated Global Atmospheric Chemistry Observation Theme for the IGOS Partnership, GAW report No. 159. September 2004, available at: ftp://ftp.wmo.int/Documents/PublicWeb/arep/gaw/gaw159.pdf (last access: 7 May 2015), 2004.

Ingmann, P., Veihelmann, B., Langen, J., Lamarre, D., Stark, H., and Courrèges-Lacoste, G. B.: Requirements for the GMES Atmosphere Service and ESA's implementation concept: Sentinels-4/-5 and-5p, Remote Sens. Environ., 120, 58–69, 2012.

Inness, A., Baier, F., Benedetti, A., Bouarar, I., Chabrillat, S., Clark, H., Clerbaux, C., Coheur, P., Engelen, R. J., Errera, Q., Flemming, J., George, M., Granier, C., Hadji-Lazaro, J., Huijnen, V., Hurtmans, D., Jones, L., Kaiser, J. W., Kapsomenakis, J., Lefever, K., Leitão, J., Razinger, M., Richter, A., Schultz, M. G., Simmons, A. J., Suttie, M., Stein, O., Thépaut, J.-N., Thouret, V., Vrekoussis, M., Zerefos, C., and the MACC team: The MACC reanalysis: an 8 yr data set of atmospheric composition, Atmos. Chem. Phys., 13, 4073–4109, https://doi.org/10.5194/acp-13-4073-2013, 2013.

IPCC: Climate Change 2013: The Physical Science Basis. Contribution of Working Group I to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change, edited by: Stocker, T. F., Qin, D., Plattner, G.-K., Tignor, M., Allen, S. K., Boschung, J., Nauels, A., Xia, Y., Bex V., and Midgley, P. M., Cambridge University Press, Cambridge, United Kingdom and New York, NY, USA, 1535 pp., 2013.

Issartel, J.-P. and Baverel, J.: Inverse transport for the verification of the Comprehensive Nuclear Test Ban Treaty, Atmos. Chem. Phys., 3, 475–486, https://doi.org/10.5194/acp-3-475-2003, 2003.

Jacobson, M. Z. and Kaufman, Y. J.: Wind reduction by aerosol particles, Geophys. Res. Lett., 33, L24814, https://doi.org/10.1029/2006GL027838, 2006.

Jiang, Z., Liu, Z., Wang, T., Schartz, C. S., Lin, H.-C., and Jiang, F.: Probing into the impact of 3DVAR assimilation of surface PM10 observations over China using process analysis, J. Geophys. Res. Atmos., 118, 6738–6749, https://doi.org/10.1002/jgrd.50495, 2013.

Joly, M. and Peuch, V.-H.: Objective classification of air quality monitoring sites over Europe, Atmos. Environ., 47, 111–123, https://doi.org/10.1016/j.atmosenv.2011.11.025, 2012.

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, https://doi.org/10.1111/j.1600-0889.2008.00377.x, 2008.

Kaiser, J. W., Heil, A., Andreae, M. O., Benedetti, A., Chubarova, N., Jones, L., Morcrette, J.-J., Razinger, M., Schultz, M. G., Suttie, M., and van der Werf, G. R.: Biomass burning emissions estimated with a global fire assimilation system based on observed fire radiative power, Biogeosciences, 9, 527–554, https://doi.org/10.5194/bg-9-527-2012, 2012.

Kalnay, E.: Atmospheric modeling, data assimilation and predictability, Cambridge University Press, Cambridge, UK, 2003.

Kalnay, E., Kanamitsu, M., Kistler, R., Collins, W., Deaven, D., Gandin, L., Iredell, M., Saha, S., White, G., Woollen, J., Zhu, Y., Chelliah, M., Ebisuzaki, W., Higgins, W., Janowiak, J., Mo, K. C., Ropelewski, C., Wang, J., Leetmaa, A., Reynolds, R., Jenne, R., and Joseph, D.: The NCEP/NCAR 40-year reanalysis project, Bull. Am. Meteorol. Soc., 77, 437–470, 1996.

Kleist, D. T., Parrish, D. F., Derber, J. C., Treadon, R., Wu, W.-S., and Lord, S.: Introduction of the GSI into the NCEP global data assimilation system, Weather Forecast., 24, 1691–1705, 2009.

Koohkan, M. R. and Bocquet, M.: Accounting for representativeness errors in the inversion of atmospheric constituent emissions: Application to the retrieval of regional carbon monoxide fluxes, Tellus B, 64, 19047, https://doi.org/10.3402/tellusb.v64i0.19047, 2012.

Koohkan, M. R., Bocquet, M., Roustan, Y., Kim, Y., and Seigneur, C.: Estimation of volatile organic compound emissions for Europe using data assimilation, Atmos. Chem. Phys., 13, 5887–5905, https://doi.org/10.5194/acp-13-5887-2013, 2013.

Krysta, M. and Bocquet, M.: Source reconstruction of an accidental radionuclide release at European scale, Q. J. Roy. Meteorol. Soc., 133, 529–544, 2007.

Kumar, U., De Ridder, K., Lefebvre, W., and Janssen, S.: Data assimilation of surface air pollutants (O3 and NO2) in the regional-scale air quality model AURORA, Atmos. Environ., 60, 99–108, 2012.

Kuze, A., Suto, H., Nakajima, M., and Hamazaki, T.: Thermal and near infrared sensor for carbon observation Fourier-transform spectrometer on the Greenhouse Gases Observing Satellite for greenhouse gases monitoring, Appl. Optics, 48, 6716–6733, 2009.

Lahoz, W., Khattatov, B., and Ménard, R. (Eds.): Data assimilation – Making sense of observations, Spinger, 718 pp., 2010.

Lauvaux, T., Schuh, A. E., Bocquet, M., Wu, L., Richardson, S., Miles, N., and Davies, K. J.: Network design for mesoscale inversions of CO2 sources and sinks, Tellus B, 64, 7980, https://doi.org/10.3402/tellusb.v64i0.17980, 2012.

Le Dimet, F.-X. and Talagrand, O.: Variational algorithms for analysis and assimilation of meteorological observations: Theoretical aspects, Tellus A, 38, 97–110, 1986.

Lee, J., Kim, J., Song, C. H., Ryu, J.-H., Ahn, Y.-H., and Song, C. K.: Algorithm for retrieval of aerosol optical properties over the ocean from the Geostationary Ocean Color Imager, Remote Sens. Environ., 114, 1077–1088, https://doi.org/10.1016/j.rse.2009.12.021, 2010.

Levelt, P. F., van den Oord, G. H., Dobber, M. R., Malkki, A., Visser, H., de Vries, J., Stamees, P., Lundell, J. O. V., and Saari, H.: The ozone monitoring instrument, IEEE Trans. Geosci. Remote Sens., 44, 1093–1101, 2006.

Lier, P. and Bach, M.: PARASOL a microsatellite in the A-Train for Earth atmospheric observations, Acta Astronautica, 62, 257–263, 2008.

Lin, C., Wang, Z., and Zhu, J.: An Ensemble Kalman Filter for severe dust storm data assimilation over China, Atmos. Chem. Phys., 8, 2975–2983, https://doi.org/10.5194/acp-8-2975-2008, 2008.

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, D22306, https://doi.org/10.1029/2011JD016159, 2011.

Lorenc, A. C.: Analysis methods for numerical weather prediction, Q. J. Roy. Meteorol. Soc., 112, 1177–1194, 1986.

Lorenc, A. C.: The potential of the ensemble Kalman filter for NWP – a comparison with 4D-Var, Q. J. Roy. Meteorol. Soc., 129, 3183–3203, 2003.

Mallet, V., Quélo, D., Sportisse, B., Ahmed de Biasi, M., Debry, \\'E., Korsakissok, I., Wu, L., Roustan, Y., Sartelet, K., Tombette, M., and Foudhil, H.: Technical Note: The air quality modeling system Polyphemus, Atmos. Chem. Phys., 7, 5479–5487, https://doi.org/10.5194/acp-7-5479-2007, 2007.

Ménard, 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.

Messina, P., D'Isodoro, M., Murizi, A., and Fierli, F.: Impact of assimilated observations on improving tropospheric ozone simulations, Atmos. Environ., 45, 6674–6681, 2011.

Migliorini, S.: On the Equivalence between Radiance and Retrieval Assimilation, Mon. Weather Rev., 140, 258–265, https://doi.org/10.1175/MWR-D-10-05047.1, 2012.

Mijling, B. and van der A, R. J.: Using daily satellite observations to estimate emissions of short-lived air pollutants on a mesoscopic scale, J. Geophys. Res., 117, D17302, https://doi.org/10.1029/2012JD017817, 2012.

Miyazaki, K., Eskes, H. J., Sudo, K., Takigawa, M., van Weele, M., and Boersma, K. F.: Simultaneous assimilation of satellite NO2, O3, CO, and HNO3 data for the analysis of tropospheric chemical composition and emissions, Atmos. Chem. Phys., 12, 9545–9579, https://doi.org/10.5194/acp-12-9545-2012, 2012.

Miyazaki, K., Eskes, H. J., Sudo, K., and Zhang, C.: Global lightning NOx production estimated by an assimilation of multiple satellite data sets, Atmos. Chem. Phys., 14, 3277–3305, https://doi.org/10.5194/acp-14-3277-2014, 2014.

Morcrette, J.-J.: Ozone-radiation interactions in the ECMWF forecast system, December, ECMWF Technical Memorandum 375, 36 pp., European Centre for Medium-range Forecasts, Reading, UK, 2003.

Morcrette, J. J., Boucher, O., Jones, L., Salmond, D., Bechtold, P., Beijaars, A., Benedetti, A., Bonet, A., Kaiser, J. W., Razinger, M., Schulz, M., Seerrar, S., Simmons, A. J., Sofiev, M., Sutte, M., Tompkins, A. M., and Untch, A.: Aerosol analysis and forecast in the European Centre for Medium-Range Weather Forecasts Integrated Forecast System: Forward modeling, J. Geophys. Res., 114, D06206, https://doi.org/10.1029/2008JD011235, 2009.

Müller, W. G.: Collecting Spatial Data: Optimum Design of Experiments for Random Fields, 3rd Edn., Springer-Verlag, 2007.

Munn, R. E.: The Design of Air Quality Monitoring Networks, MacMillan Publishers Ltd, 1981.

Navon, I. M.: Data assimilation for numerical weather prediction: A review, in: Data Assimilation for Atmospheric, Oceanic and Hydrologic Applications, edited by: Park, S. K. and Xu, L., Springer-Verlag, Berlin Heidelberg, Germany, 2009.

Nieradzik, L. and Elbern, H.: Variational assimilation of combined satellite retrieved and in situ aerosol data in an advanced chemistry transport model, Proceedings of the ESA Atmospheric Science Conference, 12, 2006.

NSTC: Air Quality Observations Systems in the United States, National Science and Technology Council, Committee on Environment, Natural Resources, and Sustainability, Washington, DC, USA, 2013.

Nychka, D. and Saltzman, N.: Design of air quality networks, in Case Studies in: Environmental Statistics, edited by: Nychka, D., Piegorsch, W., and Cox, L. H., Lecture Notes in Statistics number 132, Springer Verlag, New York, 51–76, 1998.

OJEU: Directive 2008/50/EC of the European Parliament and of the Council of 21 May 2008, Official Journal of the European Union, L 152/1, 11 June 2008.

Onogi, K., Tsutsui, J., Koide, H., Sakamoto, M., Kobayashi, S., Hatsushika, H., Matsumoto, T., Yamazaki, N., Kamahori, H., Takahashi, K., Kadokura, S.,Wada, K., Kato, K., Oyama, R., Ose, T., Mannoji, N., and Taira, R.: The JRA-25 Reanalysis, J. Meteor. Soc. Japan, 85, 369–2013432, 2007.

Osses, A., Gallardo, L., and Faundez, T.: Analysis and evolution of air quality monitoring networks using combined statistical information indexes, Tellus B, 65, 19822, https://doi.org/10.3402/tellusb.v65i0.19822, 2013.

Ott, E., Hunt, B. R., Szunyogh, I., Zimin, A. V., Kostelich, E. J., Corazza, M., Kalnay, E., Patil, D. J., and Yorke, A.: A local ensemble Kalman filter for atmospheric data assimilation, Tellus A, 56, 415–428, 2004.

Pagowski, M. and Grell, G. A.: Experiments with the assimilation of fine aerosols using an Ensemble Kalman Filter, J. Geophys. Res., 117, D21302, https://doi.org/10.1029/2012JD018333, 2012.

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.

Painemal, D. and Zuidema, P.: Assessment of MODIS cloud effective radius and optical thickness retrievals over the Southeast Pacific with VOCALS-REx in situ measurements, J. Geophys. Res., 116, D24206, https://doi.org/10.1029/2011jd016155, 2011.

Painemal, D., Minnis, P., Ayers, J. K., and O'Neill, L.: GOES-10 microphysical retrievals in marine warm clouds: Multi-instrument validation and daytime cycle over the southeast Pacific, J. Geophys. Res., 117, D19212, https://doi.org/10.1029/2012jd017822, 2012.

Park, R. S., Song, C. H., Han, K. M., Park, M. E., Lee, S.-S., Kim, S.-B., and Shimizu, A.: A study on the aerosol optical properties over East Asia using a combination of CMAQ-simulated aerosol optical properties and remote-sensing data via a data assimilation technique, Atmos. Chem. Phys., 11, 12275–12296, https://doi.org/10.5194/acp-11-12275-2011, 2011.

Park, M. E., Song, C. H., Park, R. S., Lee, J., Kim, J., Lee, S., Woo, J.-H., Carmichael, G. R., Eck, T. F., Holben, B. N., Lee, S.-S., Song, C. K., and Hong, Y. D.: New approach to monitor transboundary particulate pollution over Northeast Asia, Atmos. Chem. Phys., 14, 659–674, https://doi.org/10.5194/acp-14-659-2014, 2014.

Parrish, D. F. and Derber, J. C.: The National Meteorological Center's spectral statistical-interpolation analysis scheme, Mon. Weather Rev., 120, 1747–1763, 1992.

Penenko, V. V. and Obraztsov, N. N.: A variational initialization method for the fields of meteorological elements, Soviet Meteor. Hydrol., 11, 1–11, 1976.

Penenko, V. V.: Some aspects of mathematical modelling using the models together with observational data, Bull. Nov. Comp. Center, Series Num. Model. Atmosph., 4, 31–52, 1996.

Penenko, V. V.: Variational methods of data assimilation and inverse problems for studying the atmosphere, ocean, and environment, Num. Analys. Appl., 2, 341–351, 2009.

Penenko, V. V., Baklanov, A., and Tsvetova, E.: Methods of sensitivity theory and inverse modeling for estimation of source term, Future Generation Computer Systems, 18, 661–671, 2002.

Penenko, V., Baklanov, A., Tsvetova, E., and Mahura, A.: Direct and inverse problems in a variational concept of environmental modeling, Pure Appl. Geophys., 169, 447–465, 2012.

Petersen, G., Iverfeldt, A., and Munthe, J.: Atmospheric mercury species over central and northern Europe.Model calculations and comparison with observations from the nordic air and precipitation network for 1987 and 1988, Atmos. Environ., 29, 47–67, 1995.

Pham, D. T., Verron, J., and Roubaud, M. C.: A singular evolutive extended Kalman filter for data assimilation in oceanography, J. Mar. Syst., 16, 323–340, 1998.

Quélo, D., Mallet, V., and Sportisse, B.: Inverse modeling of NOx emissions at regional scale over northern France: Preliminary investigation of the second order sensitivity, J. Geophys. Res., 110, D24310, https://doi.org/10.1029/2005JD006151, 2006.

Rabier, F., Järvinen, H., Klinker, E., Mahfouf, J.-F., and Simmons, A.: The ECMWF operational implementation of four-dimensional variational assimilation. I: Experimental results with simplified physics, Q. J. Roy. Meteorol. Soc., 126, 1143–1170, 2000.

Raut, J., Chazette, P., and Fortain, A.: Link between aerosol optical, microphysical and chemical measurements in an underground railway station in Paris, Atmos. Environ., 43, 860–868, 2009a.

Raut, J., Chazette, P., and Fortain, A.: New approach using lidar measurements to characterize spatiotemporal aerosol mass distribution in an underground railway station in Paris, Atmos. Environ., 43, 575–583, 2009b.

Rayner, R. J.: Optimizing CO2 observing networks in the presence of model error: results from TransCom 3, Atmos. Chem. Phys., 4, 413–421, https://doi.org/10.5194/acp-4-413-2004, 2004.

Reale, O., Lau, K. M., and da Silva, A.: Impact of an interactive aerosol on the African easterly jet in the NASA GEOS-5 global forecasting system, Weather Forecast., 26, 504–519, 2011.

Reale, O., Lau, K. M., da Silva, A., and Matsui, T.: Impact of assimilated and interactive aerosol on tropical cyclogenesis. Geophys. Res. Lett., 41, 3282–3288, 2014.

Remer, L. A., Kaufman, Y., Tanré, D., Mattoo, S., Chu, D., Martins, J. V., Li, R. R., Ichoku, C., Levy, R., and Kleidman, R.: The MODIS aerosol algorithm, products, and validation, J. Atmos. Sci., 62, 947–973, 2005.

Rodgers, C. D.: Inverse Methods for Atmospheric Sounding: Theory and Practice, World Scientific Publishing, 2000.

Rodwell, M. J., Richardson, D. S., Hewson, T. D., and Haiden, T.: A new equitable score suitable for verifying precipitation in numerical weather prediction, Q. J. Roy. Meteorol. Soc., 136, 1344–1363, 2010.

Roustan, Y. and Bocquet, M.: Inverse modelling for mercury over Europe, Atmos. Chem. Phys., 6, 3085–3098, https://doi.org/10.5194/acp-6-3085-2006, 2006.

Ruiz, J. J., Pulido, M., and Miyoshi, T.: Estimating model parameters with ensemble-based data assimilation: A review, J. Meteorol. Soc. Japan, 91, 453–469, 2013.

Saide, P. E., Carmichael, G. R., Spak, S. N., Minnis, P., and Ayers, J. K.: Improving aerosol distributions below clouds by assimilating satellite-retrieved cloud droplet number, Proc. Natl. Aca. Sci., 109, 11939–11943, https://doi.org/10.1073/pnas.1205877109, 2012a.

Saide, P. E., Spak, S. N., Carmichael, G. R., Mena-Carrasco, M. A., Yang, Q., Howell, S., Leon, D. C., Snider, J. R., Bandy, A. R., Collett, J. L., Benedict, K. B., de Szoeke, S. P., Hawkins, L. N., Allen, G., Crawford, I., Crosier, J., and Springston, S. R.: Evaluating WRF-Chem aerosol indirect effects in Southeast Pacific marine stratocumulus during VOCALS-REx, Atmos. Chem. Phys., 12, 3045–3064, https://doi.org/10.5194/acp-12-3045-2012, 2012b.

Saide, P. E., Carmichael, G. R., Liu, Z., Schwartz, C. S., Lin, H. C., da Silva, A. M., and Hyer, E.: Aerosol optical depth assimilation for a size-resolved sectional model: impacts of observationally constrained, multi-wavelength and fine mode retrievals on regional scale analyses and forecasts, Atmos. Chem. Phys., 13, 10425–10444, https://doi.org/10.5194/acp-13-10425-2013, 2013.

Saide, P. E., Kim, J., Song, C. H., Choi, M., Cheng, Y., and Carmichael, G. R.: Assimilating next generation geostationary aerosol optical depth retrievals can improve air quality simulations, Geophys. Res. Lett., 2014, GL062089, https://doi.org/10.1002/2014gl062089, 2014.

Saide, P. E., Spak, S. N., Pierce, R. B., Otkin, J. A., Schaack, T. K., Heidinger, A. K., da Silva, A. M., Kacenelenbogen, M., Redemann, J., and Carmichael, G. R.: Central American biomass burning smoke can increase tornado severity in the U.S, Geophys. Res. Lett., 2014, GL062826, https://doi.org/10.1002/2014gl062826, 2015.

Sartelet, K. N., Debry, E., Fahey, K. M., Roustan, Y., Tombette, M., and Sportisse, B.: Simulation of aerosols and gas-phase species over Europe with the Polyphemus system. Part I: model-to-data comparison for 2001, Atmos. Environ., 29, 6116–6131, 2007.

Schere, K., Flemming, J., Vautard, R., Chemel, C., Colette, A., Hogrefe, C., Bessagnet, B., Meleux, F., Mathur, R., Roselle, S., Hu, R.-M., Sokhi, R. S., Rao, S. T., and Galmarini, S.: Trace gas/aerosol boundary concentrations and their impacts on continental-scale AQMEII modeling domains, Atmos. Environ., 53, 38–50, https://doi.org/10.1016/j.atmosenv.2011.09.043, 2012.

Schroedter-Homscheidt, M., Elbern, H., and Holzer-Popp, T.: Observation operator for the assimilation of aerosol type resolving satellite measurements into a chemical transport model, Atmos. Chem. Phys., 10, 10435–10452, https://doi.org/10.5194/acp-10-10435-2010, 2010.

Schubert, S. D., Rood, R. B., and Pfaendtner, J.: An Assimilated dataset for Earth science applications, Bull. Am. Meteorol. Soc., 74, 2331–2342, 1993.

Schutgens, N. A. J., Miyoshi, T., Takemura, T., and Nakajima, T.: Applying an ensemble Kalman filter to the assimilation of AERONET observations in a global aerosol transport model, Atmos. Chem. Phys., 10, 2561–2576, https://doi.org/10.5194/acp-10-2561-2010, 2010.

Schwartz, C. S., Lu, Z., Liu, H.-C., and McKeen, S. A.: Simultaneous three-dimensional variational assimilation of surface fine particulate matter and MODIS aerosol optical depth, J. Geophys. Res., 117, D13202, https://doi.org/10.1029/2011JD017383, 2012.

Schwartz, C. S., Liu, Z., Lin, H.-C., and Cetola, J. D.: Assimilating aerosol observations with a "hybrid" variational-ensemble data assimilation system, J. Geophys. Res. Atmos., 119, 4043–4069, https://doi.org/10.1002/2013JD020937, 2014.

Schwinger, J. and Elbern, H.: Chemical state estimation for the middle atmosphere by four-dimensional variational data assimilation: A posteriori validation of error statistics in observation space, J. Geophys. Res., 115, D18307, https://doi.org/10.1029/2009JD013115, 2010.

SDS-WAS: Sand and dust storm warning advisory and assessment system (SDS-WAS), Science and implementation plan: 2015–2020, WMO Research Department, Atmospheric Research and Environment Branch, July 2014.

Seinfeld, J. H. and Pandis, S. N.: Atmospheric Chemistry and Physics – from Air Pollution to Climate Change, Chapter 23: Atmospheric Chemical Transport Models, Wiley-Interscience, New York, NY, 2006.

Semane, N., Peuch, V.-H., Pradier, S., Desroziers, G., El Amraoui, L., Brousseau, P., Massart, S., Chapnik, B., and Peuch, A.: On the extraction of wind information from the assimilation of ozone profiles in Météo-France 4-D-Var operational NWP suite, Atmos. Chem. Phys., 9, 4855–4867, https://doi.org/10.5194/acp-9-4855-2009, 2009.

Shutts, G. J.: A kinetic energy backscatter algorithm for use in ensemble prediction systems, Q. J. Roy. Meteorol. Soc., 139, 2117–2144, 2005.

Singh, K. and Sandu, A.: Variational chemical data assimilation with approximate adjoints, Comput. Geosci., 40, 10–18, 2012.

Smit, H. G., Straeter, W., Johnson, B. J., Oltmans, S. J., Davies, J., Tarasick, D. W., Hoegger, B., Stubi, R., Schmidlin, F. J., Northam, T., Thompson, A. M., Witte, J. C., Boyd, I., and Posny, F.: Assessment of the performance of ECC-ozonesondes under quasi-flight conditions in the environmental simulation chamber: Insights from the Juelich Ozone Sonde Intercomparison Experiment (JOSIE), J. Geophys. Res., 112, D19306, https://doi.org/10.1029/2006JD007308, 2007.

Steinbacher, M., Zellweger, C., Schmarzenbach, B., Bugmann, S., Buchmann, B., Ordôñez, C., Prevot, A. S. H., and Hueglin, C.: Nitrogen oxide measurements at rural sites in Switzerland: Bias of conventional measurement techniques, J. Geophys. Res., 112, D11307, https://doi.org/10.1029/2006JD007971, 2007.

Storch, R. B., Pimentel, L. C. G., and Orlande, H. R. B.: Identification of atmospheric boundary layer parameters by inverse problem, Atmos. Environ., 41, 1417–1425, 2007.

Streets, D. G., Canty, T., Carmicahel, G. R., de Foy, B., Dickerson, R. R., Duncan, B. N., Erwards, D. P., Haynes, J. A., Henze, D. K., Houyoux, M. R., Jacob, D. J., Krotkov, N. A., Lamsal, L. N., Liu, Y., Lu, Z., Martin, R. V., Pfister, G. G., Pinder, R. W., Salawitch, R. J., and Wecht, K. J.: Emissions estimation from satellite retrievals: A review of current capability, Atmos. Environ., 77, 1011–1042, https://doi.org/10.1016/j.atmosenv.2013.05.051, 2013.

Sudo, K., Takahashi, M., and Akimoto, H.: CHASER: a global chemical model of the troposphere 2. Model results and evaluation, J. Geophys. Res., 107, 4586, https://doi.org/10.1029/2001JD001114, 2002.

Takemura, T., Okamoto, H., Maruyama, Y., Numaguti, A., Higurashi, A., and Nakajima, T.: Global three-dimensional simulation of aerosol optical thickness distribution of various origins, J. Geophys. Res., 105, 17853–17873, 2000.

Takemura, T., Nakajima, T., Dubovik, O., Holben, B., and Kinne, S.: Single-scattering albedo and radiative forcing of various aerosol species with a global three-dimensional model, J. Climate, 15, 333–352, 2002.

Takemura, T., Nozawa, T., Emori, S., Nakajima, T., and Nakajima, T.: Simulation of climate response to aerosol direct and indirect effects with aerosol transport-radiation model, J. Geophys. Res., 110, D02202, https://doi.org/10.1029/2004JD005029, 2005.

Talagrand, O. and Courtier, P.: Variational assimilation of meteorological observation with the adjoint vorticity equation. i: Theory, Q. J. Roy. Meteorol. Soc., 113, 1311–1328, 1987.

Talbot, R., Dibb, J., Scheuer, E., Seid, G., Russo, R., Sandholm, S., Tan, D., Singh, H., Blake, D., Blake, N., Atlas, E., Sachse, G., Jordan, C., and Avery, M.: Reactive nitrogen in Asian continental outflow over the western Pacific: Results from the NASA Transport and Chemical Evolution over the Pacific (TRACE-P) airborne mission, J. Geophys. Res., 108, 8803, https://doi.org/10.1029/2002JD003129, 2003.

Tombette, M., Mallet, V., and Sportisse, B.: PM10 data assimilation over Europe with the optimal interpolation method, Atmos. Chem. Phys., 9, 57–70, https://doi.org/10.5194/acp-9-57-2009, 2009.

Tørseth, K., Aas, W., Breivik, K., Fjæraa, A. M., Fiebig, M., Hjellbrekke, A. G., Lund Myhre, C., Solberg, S., and Yttri, K. E.: Introduction to the European Monitoring and Evaluation Programme (EMEP) and observed atmospheric composition change during 1972–2009, Atmos. Chem. Phys., 12, 5447–5481, https://doi.org/10.5194/acp-12-5447-2012, 2012.

Uppala, S. M., Kallberg, P. W., Simmons, A. J., Andrae, U., Da Costa Bechtold, V., Fiorino, M., Gibson, J. K., Haseler, J., Hernandez, A., Kelly, G. A., Li, X., Onogi, K., Saarinen, S., Sokka, N., Allan, R. P., Andersson, E., Arpe, K., Balmaseda, M. A., Beljaars, A. C. M., Van De Berg, L., Bidlot, J., Bormann, N., Caires, S., Chevallier, F., Dethof, A., Dragosavac, M., Fisher, M., Fuentes, M., Hagemann, S., Holm, E., Hoskins, B. J., Isaksen, L., Janssen, P. A. E. M., Jenne, R., McNally, A. P., Mahfouf, J. F., Morcrette, J.-J., Rayner, N. A., Saunders, R. W., Simon, P., Sterl, A., Trenberth, K. E., Untch, A., Vasiljevic, D., Viterbo, P., and Woollen, J.: The ERA-40 re-analysis, Q. J. Roy. Meteorol. Soc., 131, 2961–3012, 2005.

van Leeuwen, P. J.: Particle filtering in geophysical systems, Mon. Weather Rev., 137, 4089–4114, 2009.

Veefkind, J. P., Aben, I., McMullan, K., Förster, H., De Vries, J., Otter, G., Claas, J., Eskes, H. J., de Haan, J. F., Kleipool, Q., van Weele, M., Hasekamp, O., Hoogeveen, R., Landgraf, J., Snel, R., Tol, P., Ingman, P., Voors, R., Kruizinga, B., Vink, R., Visser, H., and Levelt, P. F.: TROPOMI on the ESA Sentinel-5 Precursor: A GMES mission for global observations of the atmospheric composition for climate, air quality and ozone layer applicationsm Remote Sens. Environ., 120, 70–83, 2012.

Verlaan, M. and Heemink, A. W.: Tidal flow forecasting using reduced rank square root filters, Stoch. Hydrol. Hydraul., 11, 349–368, 1997.

Vira, J. and Sofiev, M.: On variational data assimilation for estimating the model initial conditions and emission fluxes for short-term forecasting of SOx concentrations, Atmos. Environ., 46, 318–328, 2012.

Vira, J. and Sofiev, M.: Assimilation of surface NO2 and O3 observations into the SILAM chemistry transport model, Geosci. Model Dev., 8, 191–203, https://doi.org/10.5194/gmd-8-191-2015, 2015.

Wang, X., Hamill, T. M., and Bishop, C. H.: A comparison of hybrid ensemble transform Kalman-optimum interpolation and ensemble square root filter analysis schemes, Mon. Weather Rev., 135, 1055–1076, 2007.

Wang, X., Mallet, V., Berroir, J. P., and Herlin, I.: Assimilation of OMI NO2 retrievals into a regional chemistry-transport model for improving air quality forecasts over Europe, Atmos. Environ., 45, 485–492, 2011.

Wang, Y., Sartelet, K. N., Bocquet, M., and Chazette, P.: Assimilation of ground versus lidar observations for PM10 forecasting, Atmos. Chem. Phys., 13, 269–283, https://doi.org/10.5194/acp-13-269-2013, 2013.

Wang, Y., Sartelet, K. N., Bocquet, M., and Chazette, P.: Modelling and assimilation of lidar signals over Greater Paris during the MEGAPOLI summer campaign, Atmos. Chem. Phys., 14, 3511–3532, https://doi.org/10.5194/acp-14-3511-2014, 2014a.

Wang, Y., Sartelet, K. N., Bocquet, M., Chazette, P., Sicard, M., D'Amico, G., Léon, J. F., Alados-Arboledas, L., Amodeo, A., Augustin, P., Bach, J., Belegante, L., Binietoglou, I., Bush, X., Comerón, A., Delbarre, H., Garc\\'ia-V\\'izcaino, D., Guerrero-Rascado, J. L., Hervo, M., Iarlori, M., Kokkalis, P., Lange, D., Molero, F., Montoux, N., Muñoz, A., Muõz, C., Nicolae, D., Papayannis, A., Pappalardo, G., Preissler, J., Rizi, V., Rocadenbosch, F., Sellegri, K., Wagner, F., and Dulac, F.: Assimilation of lidar signals: application to aerosol forecasting in the western Mediterranean basin, Atmos. Chem. Phys., 14, 12031–12053, https://doi.org/10.5194/acp-14-12031-2014, 2014b.

Weaver, A. and Courtier, P.: Correlation modelling on the sphere using a generalized diffusion equation, Q. J. Roy. Meteorol. Soc., 127, 1815–1846, 2001.

Whitaker, J. S. and Hamill, T. M.: Ensemble data assimilation without perturbed observations, Mon. Weather Rev., 130, 1913–1924, 2002.

Wiedinmyer, C., Akagi, S. K., Yokelson, R. J., Emmons, L. K., Al-Saadi, J. A., Orlando, J. J., and Soja, A. J.: The Fire INventory from NCAR (FINN): a high resolution global model to estimate the emissions from open burning, Geosci. Model Dev., 4, 625–641, https://doi.org/10.5194/gmd-4-625-2011, 2011.

Williams, E. J., Fehsenfeld, F. C., Jobson, B. T., Kuster, W. C., Goldan, P. D., Stutz, J., and McClenny, W. A.: Comparison of Ultraviolet Absorbance, Chemiluminescence, and DOAS Instruments for Ambient Ozone Monitoring, Environ. Sci. Technol., 40, 5755–5762, https://doi.org/10.1021/es0523542, 2006.

Winker, D. M., Pelon, J., and McCormick, M. P.: The CALIPSO mission: Spaceborne lidar for observation of aerosols and clouds, Proc. SPIE Int. Soc. Opt. Eng., 4893, 1–11, 2003.

Wu, L., Mallet, V., Bocquet, M., and Sportisse, B.: A comparison study of data assimilation algorithms for ozone forecasts, J. Geophys. Res., 113, D20310, https://doi.org/10.1029/2008JD009991, 2008.

Wu, L., Bocquet, M., and Chevallier, M.: Optimal Reduction of the Ozone Monitoring Network over France, Atmos. Environ., 44, 3071–3083, 2010.

Wu, L. and Bocquet, M.: Optimal Redistribution of the Background Ozone Monitoring Stations over France, Atmos. Environ., 45, 772–783, 2011.

Wu, W.-S., Purser, J., and Parrish, D.: Three-dimensional variational analysis with spatially inhomogeneous covariances, Mon. Weather Rev., 130, 2905–2916, 2002.

Yu, H., Dickinson, R. E., Chin, M., Kaufman, Y. J., Holben, B. N., Geogdzhayev, I. V., 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, https://doi.org/10.1029/2002jd002717, 2003.

Yumimoto, K. and Takemura, T.: The SPRINTARS version 3.80/4D-Var data assimilation system: development and inversion experiments based on the observing system simulation experiment framework, Geosci. Model Dev., 6, 2005–2022, https://doi.org/10.5194/gmd-6-2005-2013, 2013.

Yumimoto, K., Uno, I., Sugimoto, N., Shimizu, A., Hara, Y., and Takemura, T.: Size-resolved adjoint inversion of Asian dust, Geophys. Res. Lett., 39, L24808, https://doi.org/10.1029/2012GL053890, 2012.

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, https://doi.org/10.1029/2007JD008782, 2008.

Zhang, Y.: Online-coupled meteorology and chemistry models: history, current status, and outlook, Atmos. Chem. Phys., 8, 2895–2932, https://doi.org/10.5194/acp-8-2895-2008, 2008.

Zhang, Y., Bocquet, M., Mallet, V., Seigneur, C., and Baklanov, A.: Real-time air quality forecasting, Part I: History, techniques, and current status, Atmos. Environ., 60, 632–655, 2012a.

Zhang, Y., Bocquet, M., Mallet, V., Seigneur, C., and Baklanov, A.: Real-time air quality forecasting, Part II: State of the science, current research needs, and future prospects, Atmos. Environ., 60, 656–676, 2012b.

Zyryanov, D., Foret, G., Eremenko, M., Beekmann, M., Cammas, J.-P., D'Isidoro, M., Elbern, H., Flemming, J., Friese, E., Kioutsioutkis, I., Maurizi, A., Melas, D., Meleux, F., Menut, L., Moinat, P., Peuch, V.-H., Poupkou, A., Razinger, M., Schultz, M., Stein, O., Suttie, A. M., Valdebenito, A., Zerefos, C., Dufour, G., Bergametti, G., and Flaud, J.-M.: 3-D evaluation of tropospheric ozone simulations by an ensemble of regional Chemistry Transport Model, Atmos. Chem. Phys., 12, 3219–3240, https://doi.org/10.5194/acp-12-3219-2012, 2012.