Skip to main content
SpringerLink
Log in

Enhancement of OMI aerosol optical depth data assimilation using artificial neural network

  • Original Article
  • Published:
Neural Computing and Applications Aims and scope Submit manuscript

Abstract

A regional chemical transport model assimilated with daily mean satellite and ground-based aerosol optical depth (AOD) observations is used to produce three-dimensional distributions of aerosols throughout Europe for the year 2005. In this paper, the AOD measurements of the Ozone Monitoring Instrument (OMI) are assimilated with Polyphemus model. In order to overcome missing satellite data, a methodology for preprocessing AOD based on neural network (NN) is proposed. The aerosol forecasts involve two-phase process assimilation and then a feedback correction process. During the assimilation phase, the total column AOD is estimated from the model aerosol fields. The main contribution is to adjust model state to improve the agreement between the simulated AOD and satellite retrievals of AOD. The results show that the assimilation of AOD observations significantly improves the forecast for total mass. The errors on aerosol chemical composition are reduced and are sometimes vanished by the assimilation procedure and NN preprocessing, which shows a big contribution to the assimilation process.

This is a preview of subscription content, log in via an institution to check access.

Access this article

Log in via an institution

Price excludes VAT (USA)
Tax calculation will be finalised during checkout.

Instant access to the full article PDF.

Institutional subscriptions

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5
Fig. 6
Fig. 7
Fig. 8
Fig. 9
Fig. 10
Fig. 11
Fig. 12
Fig. 13

Similar content being viewed by others

References

  1. Ali A, Amin SE, Ramadan HH, Tolba MF (2012) Ozone Monitoring Instrument aerosol products: a comparison study with ground-based airborne sun photometer measurements over Europe. Int J Remote Sens 33(20):6321–6341

    Article  Google Scholar 

  2. Ali A, Amin SE, Ramadan HH, Tolba MF (2011) Ozone monitoring instrument aerosol products: algorithm modeling and validation with ground based measurements over Europe. The 2011 international conference on computer engineering and systems (ICCES’ 2011)

  3. Alvera-Azcrate A, Barth A, Rixen M, Beckers J (2005) Reconstruction of incomplete oceanographic data sets using empirical orthogonal functions: application to the Adriatic Sea surface temperature. Ocean Model 9(4):325–346

    Article  Google Scholar 

  4. Balgovind R, Dalcher A, Ghil M, Kalnay E (1983) A stochastic—dynamic model for the spatial structure of forecast error statistics. Mon Weather Rev 111:701–722

    Article  Google Scholar 

  5. Boutahar J, Lacour S, Mallet V, Quelo D, Roustan Y, Sportisse BM (2004) Development and validation of a fully modular platform for numerical modelling of air pollution: POLAIR. Int J Environ Pollut 22(1/2):17–28

    Google Scholar 

  6. Boylan JW, Russell AG (2006) PM and light extinction model performance metrics, goals, and criteria for three-dimensional air quality models. Atmos Environ 40:4946–4959

    Article  Google Scholar 

  7. Chai T, Carmichael GR, Tang Y, Sandu A, Hardesty M, Pilewskie P, Whitlow S, Browell EV, Avery MA, Nede Lec P (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. http://dx.doi.org/10.1029/2006JD007763

  8. Chameides WL, Yu H, Liu SC, Bergin M, Zhou X, Mearns L, Wang G, Kiang CS, Saylor RD, Luo C, Huang Y, Steiner A, Giorgi F (1999) Case study of the effects of atmospheric aerosols and regional haze on agriculture, an opportunity to enhance crop yields in China through emission controls? Proc Nat Acad Sci USA 96(24):13626–13633

    Article  Google Scholar 

  9. Chin M, Rood R, Lin S-J, Muller JF, Thompson AM (2000) Atmospheric sulfur cycle in the global model GOCART: model description and global properties. J Geophys Res 105:24671–24688

    Article  Google Scholar 

  10. Collins WD, Rasch PJ, Eaton BE, Khattatov BV, Lamarque J-F, Zender CS (2001) Simulating aerosols using a chemical transport model with assimilation of satellite aerosol retrievals: methodology for INDOEX. J Geophys Res Atmosph 106(D7):7313–7336

    Article  Google Scholar 

  11. Chung CE, Ramanathan V, Kim D, Podgorny IA (2005) Global anthropogenic aerosol direct forcing derived from satellite and ground-based observations. J Geophys Res 110:D24207. doi:10.1029/2005JD006356

    Article  Google Scholar 

  12. Debry E, Fahey K, Sartelet K, Sportisse B, Tombette M (2007) Technical note: a new SIze REsolved Aerosol Model. Atmos Chem Phys 7:1537–1547

    Article  Google Scholar 

  13. Debry E, Fahey K, Sartelet K, Sportisse B, Tombette M (2007) Technical note: a new Size Resolved Aerosol Model. Atmos Chem Phys 7:1537–1547. Available at http://www.atmos-chem-phys.net/7/1537/2007

    Google Scholar 

  14. Dubovik O, Holben BN, Eck TF, Smirnov A, Kaufman YJ, King MD, Tanré D, Slutsker I (2002) Variability of absorption and optical properties of key aerosol types observed in worldwide locations. J Atmos Sci 59:590–608

    Google Scholar 

  15. Dunlea EJ, Herndon SC, Nelson DD, Volkamer RM, San Martini F, Sheehy PM, Zahniser MS, Shorter JH, Wormhoudt JC, Lamb BK, Allwine EJ, Gaffney JS, Marley NA, Grutter M, Marquez C, Blanco S, Cardenas B, Retama A, Ramos Villegas CR, Kolb CE, Molina LT, Molina MJ (2007) Evaluation of nitrogen dioxide chemiluminescence monitors in a polluted urban environment. Atmos Chem Phys 7:2691–2704. Available at http://dx.doi.org/10.5194/acp-7-2691-2007. doi:10.5194/acp-7-2691-2007

    Google Scholar 

  16. Elbern H, Schmidt H, Ebel A (1997) Variational data assimilation for tropospheric chemistry modeling. J Geophys Res 102(13):15967–15985

    Article  Google Scholar 

  17. Emmons LK, Walters S, Hess PG, Lamarque J-F, Pfister GG, Fillmore D, Granier C, Guenther A, Kinnison D, Laepple T, Orlando J, Tie X, Tyndall G, Wiedinmyer C, Baughcum SL, Kloster S (2009) Description and evaluation of the Model for Ozone and Related chemical Tracers, version 4 (MOZART-4). Geosci Model Dev 2:1157–1213. doi:10.5194/gmdd-2-1157-2009

    Article  Google Scholar 

  18. Fahey KM, Pandis SN (2001) Optimizing model performance: variable size resolution in cloud chemistry modeling. Atmos Environ 35:4471–4478

    Article  Google Scholar 

  19. Hess M, Koepke P, Schult I (1998) Optical properties of aerosols and clouds: the software package OPAC. Bull Am Met Soc 79:831–844

    Google Scholar 

  20. Hollingsworth A, Lonnberg P (1986) The statistical structure of short-range forecast errors as determined from radiosonde data. Part I: the wind field. Tellus 38A:111–136

    Article  Google Scholar 

  21. Huneeus N (2007) Assimilation variationnelle d’observations satellitaires dans un mod`ele atmosph’erique d’a’erosols. Ph.D. thesis, Universit’e des Sciences et Technologies de Lille

  22. Iglesias P, Jorquera H, Palma W (2006) Data analysis using regression models with missing observations and long-memory: an application study. Comput Stat Data Anal 50:2028–2043

    Google Scholar 

  23. IPCC (2007) Climate change. The physical science basis contribution of working group I to the fourth assessment report of the intergovernmental panel on climate change, the physical science basis. Cambridge University Press, Cambridge

  24. Jacobson MZ (2005) Fundamentals of atmospheric modeling. Cambridge University Press, Cambridge

    Book  MATH  Google Scholar 

  25. Jeuken A, Eskes H, van Velthoven P, Kelder H, Holm E (1999) Assimilation of total ozone satellite measurements in a three dimensional tracer transport model. J Geophys Res 104:5551–5563

    Article  Google Scholar 

  26. Josef C, Gunnar D, Wolfgang K, Thomas T, Joachim H (2001) PM2.5 measurements in ambient aerosol: comparison between Harvard impactor (HI) and the tapered element oscillating microbalance (TEOM) system. Sci Total Environ 278:191–197

    Article  Google Scholar 

  27. Kalnay E (2003) Atmospheric modeling, data assimilation and predictability. Cambridge University Press, Cambridge

    Google Scholar 

  28. Kerridge BJ, Siddans R, Reburn WJ, Remedios JJ, Richards NAD, Dudhia A et al (2001) Definition of mission objectives and observational requirements for an Atmospheric Chemistry Explorer mission, Final Report, ESA Contract 13048/98/NL/GD. ESTEC, Noordwijk

    Google Scholar 

  29. Khattatov BV, Lamarque J-F, Lyjak LV, Menard R, Levelt P, Tie X, Brasseur GP, Gille JC (2000) Assimilation of satellite observations of long-lived chemical species in global chemistry transport models. J Geophys Res Atmos 105(23):29135–29144

    Article  Google Scholar 

  30. Kinne S, Schultz M, Textor C, Guibert S, Balkanski Y, Bauer SE, Berntsen T, Berglen TF, Boucher O, Chin M, Collins (2006) An AeroCom initial assessment optical properties in aerosol component modules of global models. Atmos Chem Phys 6:1815–1834. Available at http://www.atmos-chem-phys.net/6/1815/2006/

  31. Korsakissok I, Mallet V (2009) Comparative study of Gaussian dispersion formulas within the Polyphemus Platform: evaluation with Prairie Grass and Kincaid experiments. J Appl Meteorol Climatol 48:2459–2473

    Article  Google Scholar 

  32. Lamarque JF, Khattatov BV, Gille JC (2002) Constraining tropospheric ozone column through data assimilation. J Geophys Res Atmos 107(D22):ACH9/1ACH9/11

  33. Levelt PF, Hilsenrath E, Leppelmeier GW, van den Oord GHJ, Bhartia PK, Tamminen J, de Haan JF, Veefkind JP (2006) Science objectives of the ozone monitoring instrument. IEEE Trans Geosci Remote Sens 44(5):1199–1208

    Article  Google Scholar 

  34. Lorenc AC (1986) Analysis methods for numerical weather prediction. Q J Royal Meteorol Soc 112(474):1177–1194

    Article  Google Scholar 

  35. Mallet V, Quelo D, Sportisse B, Ahmed de Biasi M, Debry E, Korsakissok I, Wu L, Roustan Y, Sartelet K, Tombette M, Foudhil H (2007) Technical note: the air quality modeling system Polyphemus. Atmos Chem Phys 7:5479–5487

    Article  Google Scholar 

  36. Middleton P, Stockwell WR, Carter WPL (1990) Aggregation and analysis of volatile organic compound emissions for regional modeling. Atmos Environ 24A(5):1107–1133

    Article  Google Scholar 

  37. Niu T, Gong SL, Zhu GF, Liu HL, Hu XQ, Zhou CH, Wang YQ, Zhang XY (2007) Data assimilation of dust aerosol observations for CUACE/dust forecasting system. Atmos Chem Phys 7:8309–8332 Discussion

    Article  Google Scholar 

  38. Penner JE, Chang SY, Chin M, Chuang CC, Feichter J, Feng Y, Geogdzhayev IV, Ginoux P, Herzog M, Higurashi A, Koch D, Land C, Lohmann U, Mishchenko M, Nakajima T, Pitari G, Soden B, Tegen I, Stowe L (2002) A comparison of model- and satellite-derived Aerosol Optical Depth and Reactivity. J Atmos Sci 59:441–460

    Article  Google Scholar 

  39. Pope CA III, Burnett RT, Thun MJ, Calle EE, Krewski D, Ito K, Thurston GD (2002) Lung cancer, cardiopulmonary mortality and long-term exposure to fine particulate air pollution. J Am Med Assoc 287(9):1132–1141

    Article  Google Scholar 

  40. Quelo D, Krysta M, Bocquet M, Isnard O, Minier Y, Sportisse B (2007) Validation of the Polyphemus platform on the ETEX, Chernobyl and Algeciras cases. Atmos Environ 41(26):5300–5315

    Article  Google Scholar 

  41. Ramanathan V, Carmichael G (2008) Global and regional climate changes due to black carbon. Nat Geosci 1(4):221–227

    Article  Google Scholar 

  42. Ramanathan V, Crutzen PJ, Kiehl JT, Rosenfeld D (2001) Atmosphere aerosols, climate and the hydrological cycle (Washington, DC, United States). Science 294(5549):2119–2124

    Article  Google Scholar 

  43. Sandu A, Daescu D, Carmichael GR (2003) Direct and adjoint sensitivity analysis of chemical kinetic systems with KPP: I theory and software tools. Atmos Environ 37:5083–5096

    Article  Google Scholar 

  44. Sartelet KN, Debry E, Fahey KM, Roustan Y, Tombette M, Sportisse B (2007) 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

    Article  Google Scholar 

  45. Schell B, Ackermann IJ, Hass H, Binkowski FS, Ebel A (2001) Modeling the formation of secondary organic aerosol within a comprehensive air quality model system. J Geophys Res 106:28275–28293

    Article  Google Scholar 

  46. Simpson D, Winiwarter W, Borjesson G, Cindebry S, Ferreiro A, Guenther A, Hewitt CN, Janson R, Khalil MAK, Owen S, Pierce TE, Puxbaum H, Shearer M, Skiba U, Steinbrecher R, Tarrason L, Oquist MG (1999) Inventorying emissions from nature in Europe. J Geophys Res D104(7):8113–8152

    Article  Google Scholar 

  47. Sinyuk A, Torres O, Dubovik O (2003) Combined use of satellite and surface observations to infer the imaginary part of the refractive index of Saharan dust. Geophys Res Lett 30(2):1081. doi:10.1029/2002GL016189

    Google Scholar 

  48. Steinbacher M, Zellweger C, Schwarzenbach B, Bugmann S, Buchmann B, Ordo`n˜ez C, Prevot ASH, Hueglin C (2007) Nitrogen oxides measurements at rural sites in Switzerland: Bias of conventional measurement techniques. J Geophys Res D11307. doi:10.1029/2006JD007971

  49. Stockwell W, Kirchner F, Kuhn M (1997) A new mechanism for regional chemistry modeling. J Geophys Res 102:25847–25879

    Article  Google Scholar 

  50. Strader R, Gurciullo C, Pandis SN, Kumar N, Lurmann FW (1998) Development of gas-phase chemistry, secondary organic aerosol, and aqueous-phase chemistry modules for PM modeling. Final report for CRC Project A21-1 prepared for the Coordinating Research Council, Atlanta, GA by Sonoma Technology, Inc. Petaluma, CA, STI-97510-1822-FR

  51. Tombette M, Sportisse B (2007) Aerosol modeling at a regional scale: model-to-data comparison and sensitivity analysis over Greater Paris. Atmos Environ 41:6941–6950

    Article  Google Scholar 

  52. Torres O, Decae R, Veefkind JP, De Leeuw G (2002) OMI Aerosol retrieval algorithm. OMI algorithm theoretical basis document: clouds, aerosols, and surface UV irradiance, vol 3, version 2. In: Stammes P (ed) OMI-ATBD-03. Available at http://eospso.gsfc.nasa.gov/eoshomepage/forscientists/atbd/docs/OMI/ATBD-OMI-03.pdf

  53. Wang J, Nair US, Christopher SA (2004) GOES 8 aerosol optical thickness assimilation in a mesoscale model: online integration of aerosol radiative effects. J Geophys Res 109. doi:10.1029/2004JD004827

  54. Wu D, Hartman A, Ward N, Eisen JA (2008) An automated phylogenetic tree-based small subunit RNA taxonomy and alignment pipeline (STAP). PLoS ONE 3(7):e2566. doi:10.1371/journal.pone.0002566

    Article  Google Scholar 

  55. Yu H, Kaufman YJ, Chin M, Feingold G, Remer LA, Anderson TL, Balkanski Y, Belloin N, Boucher O, Christopher S, de Cola P, Kahn R, Koch D, Loeb N, Reddy MS, Schultz M, Takemura T, Zhou M (2006) A review of measurement-based assessments of the aerosol direct radiative effect and forcing. Atmos Chem Phys 6:613–666

    Article  Google Scholar 

  56. Zhang L, Brook JR, Vet R (2003) A revised parameterization for gaseous dry deposition in air-quality models. Atmos Chem Phys 3(6):2067

    Article  Google Scholar 

Download references

Author information

Authors and Affiliations

  1. Scientific Computing Department, Ain Shams University, Cairo, Egypt

    A. Ali, S. E. Amin, H. H. Ramadan & M. F. Tolba

Authors
  1. A. Ali
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. S. E. Amin
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. H. H. Ramadan
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. M. F. Tolba
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to A. Ali.

Rights and permissions

Reprints and permissions

About this article

Cite this article

Ali, A., Amin, S.E., Ramadan, H.H. et al. Enhancement of OMI aerosol optical depth data assimilation using artificial neural network. Neural Comput & Applic 23, 2267–2279 (2013). https://doi.org/10.1007/s00521-012-1178-9

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00521-012-1178-9

Keywords

Search

Navigation