06Oct 2018

ENHANCING VERTICAL RESOLUTION OF SATELLITE ATMOSPHERIC PROFILE DATA: A MACHINE LEARNING APPROACH.

  • Department of Meteorology and Oceanography, Andhra University, Visakhapatnam, India.
  • The Energy and Resources Institute, New Delhi, India.
Crossref Cited-by Linking logo

  • Abstract
  • Keywords
  • References
  • Cite This Article as
  • Corresponding Author

We developed a statistical approach using the Artificial Neural Networks (ANN) to improve the vertical resolution of tropospheric relative humidity profiles (RH) from 20 pressure levels to 171 pressure levels. The model is based on an unconventional method in which we used the Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) Global Positioning System Radio Occultation (GPS RO) data and the corresponding observed values of RH data. The model was developed using 3 years COSMIC daily data during 2007-2009 over the north Indian Ocean and produced high vertical resolution RH (171 pressure levels) output data from the coarse resolution inputs (20 pressure levels). We achieved the best performance in generating high vertical resolution data with a Pearson’s correlation coefficient (CC) of greater than 0.94 and scatter index (SI) of less than 0.1 throughout all pressure levels. Thus, the present approach is an efficient method to achieve the better vertical resolution of RH data from geostationary satellites.

  1. A technical document from SAC, ISRO on specifications of INSAT-3D, October, 2010.
  2. Sun, A. Reale, A, D.J. Seidel, and D.C.Hunt, ?Comparing radiosonde and COSMIC atmospheric profile data to quantify differences among radiosonde types and the effects of imperfect collocation on comparison statistics?, J. Geophys. Res., 115, D23104, doi: 10.1029/2010jd014457, 2010.
  3. Borst, R.A. ?Artificial neural networks in mass appraisal?, Journal of Property Tax Assessment & Administration?, 1(2):5-15, 1995.
  4. Butler, Charles T., R. Meredith, and A. P. Stogryn. "Retrieving atmospheric temperature parameters from DMSP SSM/T‐1 data with a neural network."?Journal of Geophysical Research: Atmospheres101, no. D3 (1996): 7075-7083.
  5. Document on Algorithm Theoretical Basis Definition (ATBD) for the geophysical parameter retrieval from INSAT-3D Imager and Sounder channels data. IMDPS PR ATBD Document, Feb 2007.
  6. R.Kursinski, G. Hajj, J.T. Schofield, R.P. Linfield, and K. Hardy, ?Observing Earth's atmosphere with radio occultation measurements using the Global Positioning System?, Journal of Geophysical Research, vol. 102, no.D19, pp. 23429-65, 1997.
  7. Badran, S. Thiria and M. Crepon, ?Wind ambiguity removal by the use of neural network techniques?, J. Geophys. Res., vol. 96, 20 521?20 529, 1991.
  8. Gettelman, A., V. P. Walden, L. M. Miloshevich, W. L. Roth, and B. Halter. "Relative humidity over Antarctica from radiosondes, satellites, and a general circulation model."?Journal of Geophysical Research: Atmospheres111, no. D9 (2006).
  9. Gille, J. C. and House, F. B, ?On the inversion of limb and radiance measurements I: Temperature and thickness?. Journal of the Atmospheric Sciences, 28:1,427?1,442, 1971.
  10. Hajj, George A., E. R. Kursinski, L. J. Romans, W. I. Bertiger, and S. S. Leroy. "A technical description of atmospheric sounding by GPS occultation."?Journal of Atmospheric and Solar-Terrestrial Physics64, no. 4 (2002): 451-469.
  11. Jagadheesha, D., B. Manikiam, Neerja Sharma, and P. K. Pal. "Atmospheric stability index using radio occultation refractivity profiles."?Journal of earth system science120, no. 2 (2011): 311-319.
  12. Zhang, E. Fu, D. Silcock, Y.Wang and Y. Kuleshov, ?An investigation of atmospheric temperature profiles in the Australian region using collocated GPS radio occultation and radiosonde data?, Atmos. Meas. Tech., vol.4, pp. 2087?2092, 2011.
  13. Kursinski, E. R., Hajj, G. A., Leroy, S. S., and Herman, B. ?The GPS radio occultation technique? Atmos. Ocean. Sci., 11, 53?114, 2000
  14. Murphy, B.J., Haase, J.S., Muradyan, P., Garrison, J.L. & Wang, K.-N. ?Airborne GPS radio occultation refractivity profiles observed in tropical storm environments?. Journal of Geophysical Research, 120, 1690-1790, doi: 10.1002/2014JD022931.2015.
  15. S and Ali.M.M, ?A neural network approach to improving the vertical resolution of atmospheric temperature profiles from geostationary satellites?, IEEE geosciences and remote sensing letters, 2012.DOI: 10.1109/LGRS.2012.2191763.
  16. Pelliccia, Fabrizio, StefaniaBonafoni, PatriziaBasili, PieroCiotti, and NazzarenoPierdicca. "Atmospheric profiling in the inter-tropical ocean area based on neural network approach using GPS radio occultations."?Open Atmospheric Science Journal4 (2010): 202-209.
  17. Poli, P., and J. Joiner, C. Reigber, H. L?hr, and P. Schwintzer, Eds., ?Assimilation experiments of onedimensionalvariational analyses with GPS/MET refractivity. First CHAMP Mission Results for Gravity, Magnetic and Atmospheric Studies? Springer, 515?520, 2003.
  18. A Anthes, C. Rocken, and Y.H. Kuo, ?Applications of COSMIC to meteorology and climate, Terrestrial?, Atmospheric and Oceanic Sciences, vol. 11, no. 1, pp. 115-56, 2000.
  19. Rumelhart D.E., Hinton G.E. and Williams R.J. Nature, 323, 533-536.
  20. Rumelhart DE, McClelland JL, and the PDP Research Group ?Parallel distributed processing. Exploration in the microstructure of cognition?, vol 1-2. MIT Press, Cambridge, 1986.
  21. Smith E L and Weintraub, S ?The constants in the equation for atmospheric refractive index at radio frequencies? IRE 41 1035?1037, 1953.
  22. Stanley Q.kidder, Thomas H.Vonderhaar, ?Satellite Meteorology, An Introduction?, 1995.
  23. Tae-Kwon Wee, Ying-HwaKuo, David H. Bromwich, et al. ?Assimilation of GPS Radio Occultation Refractivity Data from CHAMP and SAC-C Missions over High Southern Latitudes with MM5 4DVAR?. Monthly Weather Review, Vol.136, No.8, p.2923. 2008.
  24. Terrestrial, Atmospheric and Oceanic sciences journal, volume 11, No. 1 March, 2011.
  25. Ulrich Foelsche,?Michael Borsche,?Andrea K. Steiner,?Andreas Gobiet,?Barbara Pirscher,?Gottfried Kirchengast,?Jens Wickert,?Torsten Schmidt, ?Observing upper troposphere-lower stratosphere climate with radio occultation data from the CHAMP satellite,? Climate Dynamics, doi: 10.1007/s00382-007-0337-7, 2007.
  26. Krasnopolsky and H. Schiller, ?Some neural network applications in environmental sciences part I: Forward and inverse problems in satellite remote sensing?, Neural Networks, vol.16, pp. 321?334, 2003.
  27. Krasnopolsky, L. C. Breaker and W. H. Gemmill, ?A neural network as a nonlinear transfer function model for retrieving surface wind speeds from the Special Sensor Microwave Imager?, J.Geophys. Res., vol.100, pp.11 033?11 045, 1995.
  28. Zhang, K., E. Fu, D. Silcock, Y. Wang, and Y. Kuleshov. "An investigation of atmospheric temperature profiles in the Australian region using collocated GPS radio occultation and radiosonde data."?Atmospheric Measurement Techniques4, no. 10 (2011): 2087-2092.

[Venugopal T and Venkatramana K (2018); ENHANCING VERTICAL RESOLUTION OF SATELLITE ATMOSPHERIC PROFILE DATA: A MACHINE LEARNING APPROACH. Int. J. of Adv. Res. 6 (Oct). 542-550] (ISSN 2320-5407). www.journalijar.com

Venugopal Thandlam
1. Department of Meteorology and Oceanography, Andhra University, Visakhapatnam, India

DOI:

Article DOI: 10.21474/IJAR01/7836      
DOI URL: https://dx.doi.org/10.21474/IJAR01/7836

Download Full Paper

Download PDF No. of Downloads: 33 | No. of Views: 147