Retrieval of carbon dioxide vertical concentration profiles from satellite data using artificial neural networks

A. Roberto Carvalho, F. Manoel Ramos, J. Carlos Carvalho

Abstract


Abstract. In this paper, we derive vertical distributions of carbon dioxide atmospheric concentration from satellite data using a retrieval algorithm based on an artificial neural network (ANN) technique. Sensitivity studies were made to selec tthe most appropriate sensor channels. A MultiLayer Perceptron (MLP) ANN was implemented and tested for a large and diversified dataset. Here we focused on the retrieval of vertical Carbon Dioxide concentration profiles using SCIAMACHY channel 6 (1000-1700 nm) nadir measurements. The results show we can accurately and efficiently obtain carbon dioxide profiles by using this approach.

References


[1] C.M. Bishop, ”Neural Networks for Pattern Recognition“, Oxford University´Press, 1995.

[2] H. Bovensmann, J.P. Burrows, M. Buchwitz, J. Frerick, S. Noel, V.V. Rozanov, SCIAMACHY - Mission Objectives and Measurement Modes, J. Atmos. Sci., 56 (1999), 127–150.

[3] M. Buchwitz, J.P. Burrows, Retrieval of CH4, CO, and CO2 total column amounts from SCIAMACHY near-infrared nadir spectra: Retrieval algorithm and first results, in “Proceedings of SPIE 5235, Remote Sensing of Clouds and the Atmosphere VIII”, K. P. Schafer and A. Comeron and M. R. Carleer and

R. H. Picard (Editors), 375–388, 2004.

[4] M. Buchwitz, S. Noel, K. Bramstedt, V.V. Rozanov, M. Eisinger, H. Bovensmann, S. Tsvetkova, J.P. Burrows, Retrieval of trace gas vertical columns from SCIAMACHY/ENVISAT near-infrared nadir spectra: first preliminary results, Advances in Space Research, 34 (2004), 809–814.

[5] M. Buchwitz, et al., Atmospheric methane and carbon dioxide from SCIAMACHY satellite data: initial comparison with chemistry and transport models, Atmos. Chem. Phys., 5 (2005), 941–962.

[6] F. Chevallier, F. Cheruy, N. A. Scott, A. Chedin, A neural network approach for a fast and accurate computation of longwave radiative budget, J. of Applied Meteorology, 37 (1998), 1385–1397.

[7] C. Clerbaux, J. Hadji-Lazaro, S. Payan, C. Camy-Peyret and G. Megie, Retrieval of CO columns from IMG/ADEOS spectra, IEEE Transactions on Geo- science and Remote Sensing, 37 (1999), 1657–1661.

[8] C. Crevoisier, A. Chedin and N.A. Scott, AIRS channel selection for CO2 and other trace-gas retrievals, Q. J .Roy. Meteor. Soc., 129 (2004), 2719–2740.

[9] J. Escobar-Munoz, A. Chedin, F. Cheruy, N.A. Scott, Réseaux de neurones multi-couches pour la restitution de variables thermodynamiques atmosphériques á l’aide de sondeurs verticaux satellitaires, Comptes Rendus de

l’Académie des Sciences de Paris, 317 (1993), 911–918 (in French).

[10] L. Garand, D.S. Turner, Radiance and jacobian intercomparison of radiative transfer models applied to HIRS and AMSU channels, Journal of Geophysical Research, 106, No. D20, (2001), 24017–24031.

[11] S. Haykin, “Neural Networks. A Comprehensive Foundation”, Macmillan, New York, NY, 1994.

[12] S. Houweling, F.M. Breon, I. Aben, C. Rodenbeck, M. Gloor, M. Heimann, P. Ciais, Inverse modeling of CO2 sources and sinks using satellite data: A synthetic inter-comparison of measurement techniques and their performance as a function of space and time, Atmos. Chem. Phys., 4 (2004), 523–538.

[13] J. Lenoble, “Radiative Transfer in Scattering and Absorbing Atmospheres: Standard computational procedures”, A. Deepak Publishing, Hampton, Virginia, 1985.

[14] F. Rabier, N. Fourrie, D. Chafai, P. Prunet, Channel selection methods for infrared atmospheric sounding interferometer radiances, Quart J. Roy. Meteor. Soc., 111 (2002), 974–975.

[15] P.J. Rayner, R.M. Law., D.M. O’Brien, T.M. Butler, A.C. Dilley, Global observations of the carbon budget. 3. Initial assessment of the impact of satellite orbit, scan geometry, and cloud on measuring CO2 from space, Advances in Computer Science, Journal of Geophysical Research, 107 (2002), doi: 10.1029/2001JD000618.

[16] V.V. Rozanov, M. Buchwitz, K.U. Eichmann, R. de Beek, J.P. Burrows, SCIATRAN - a new radiative transfer model for geophysical applications in the 240-2400 nm spectral region: The pseudo-spherical version, New York: John Wiley & Sons, Advances in Space Research, 29, No. 11 (2002), 1831–1835.

[17] V.V. Rozanov, D. Diebel, R.J.D. Spurr, J. P. Burrows, GOMETRAN:A radiative transfer model for the satellite project GOME - the plane-parallel version, J. Geophys. Res., No. 102 (1997), 16683–16695, doi:10.1029/96JD01535.

[18] E.H. Shiguemori, J.D.S. da Silva, H.F. de Campos Velho, J.C. Carvalho, Neural Network based Models in the Inversion of Temperature Vertical Profiles from Satellite Data, Inverse Problems in Engineering, England, 14, No. 5 (2006), 543–556.

[19] P. Tans, et al., “Trends in Atmospheric Carbon Dioxide - Mauna Loa”, NOAA/ESRL, www.esrl.noaa.gov/gmd/ccgg/trends, 2009.

[20] S. Turquety, J. Hadji-Lazaro, First satellite ozone distributions retrieved from nadir high-resolution infrared spectra, Geophysical Research Letters., 29 (2002).

[21] S. Twomey, “Introduction to the Mathematics of Inversion in Remote Sensing and Indirect Measurements”, Dover Publications Inc., 243 pp., 1977.




DOI: https://doi.org/10.5540/tema.2010.011.03.0205

Article Metrics

Metrics Loading ...

Metrics powered by PLOS ALM

Refbacks

  • There are currently no refbacks.



Trends in Computational and Applied Mathematics

A publication of the Brazilian Society of Applied and Computational Mathematics (SBMAC)

 

Indexed in:

                       

         

 

Desenvolvido por:

Logomarca da Lepidus Tecnologia