Outgoing Longwave Radiation over Iraq using Atmospheric Infrared Sounder

Authors

1 Department of Atmospheric Sciences, College of Science, Al-Mustansiriyah University, Baghdad, Iraq

2 Department of Atmospheric Sciences, College of Science, Al-Mustansiriyah University, Baghdad, Iraq+Department of Environment and Water, Ministry of Science and Technology, Baghdad, Iraq

Abstract

The present paper involves Outgoing longwave radiation (OLR) under clear-sky condition modeling employing three measured meteorological parameters (Air surface temperature, Relative humidity and Cloud fraction). Dataset retrieved from NASA Atmospheric Infrared Sounder (AIRS), from 2003 to 2015 was employed to develop two models to estimate OLR values in Iraq using the multiple linear regression (MLR) and Artificial Neural Network (ANN) approach. For the entire period, the mentioned meteorological parameters were highly correlated with estimated OLR. Comparisons among selected cities (Mosul, Baghdad, and Basrah) for the year 2016 showed close agreement between the estimated and measured OLR. Mosul at the north of the Iraq, showed the lowest root mean square error (RMSE) and correlation coefficient (R) ranged between (1.3853 and 4.4966) and (0.9929 and 0.9993), respectively for the two developed models (MLR and ANN) respectively, indicating model's efficiency and accuracy. Statistical analysis in term of β showed that surface temperature (1.823 to 2.311) tended to provide a high contribute to OLR values. These results indicate the advantage of using the AIRS data and both of correlation analysis and computing system to investigate the impact of the meteorological parameters on OLR over the study area.

Keywords

References

  1. Mahakur, M., Prabhu, A., Sharma, A. K., Rao, V. R., Senroy, S., Singh, R. and Goswami, B. N.,2013. "A high-resolution outgoing longwave radiation dataset from Kalpana-1 satellite during 2004–2012". Current Science, Vol.105, No.8, p. 1124-1133.
  2. Chan, K. P., 2013." Analysis of outgoing longwave radiation (OLR) in different timescales over Africa and Atlantic Ocean".  PhD. thesis submitted to Imperial College London. England. pp. 226.
  3. Mitchell, J. F., 1989. " The greenhouse effect and climate change". Reviews of Geophysics, Vol.27, No.1, p. 115-139.
  4. Huang, Y., Ramaswamy, V. and Soden, B.,2007. "An investigation of the sensitivity of the clear‐sky outgoing longwave radiation to atmospheric temperature and water vapor". Journal of Geophysical Research: Atmospheres, Vol.112(D5).
  5. Griggs, J. A. and Harries, J. E., 2007. " Comparison of spectrally resolved outgoing longwave radiation over the tropical Pacific between 1970 and 2003 using IRIS, IMG, and AIRS". Journal of climate, Vol.20, No.15, p.3982-4001.
  6. Bühler, S., von Engeln, A., Brocard, E., John, V., Kuhn, T. and Erikson, P., 2004 " The impact of humidity and temperature variations on clear–sky outgoing longwave radiation". Journal of Geophysical Research. Submitted.
  7. Lim, E. S., Das, U., Pan, C. J., Abdullah, K. and Wong, C. J.,2013. " Investigating variability of outgoing longwave radiation over peninsular Malaysia using wavelet transform". Journal of Climate, Vol.26, No.10, p. 3415-3428. ‏
  8. Chaudhari, H. S., Shinde, M. A. and Oh, J. H. ,2010." Understanding of anomalous Indian summer monsoon rainfall of 2002 and 1994". Quaternary International, Vol.213, No.1, p. 20-32.
  9. Susskind, J., Molnar, G. and Iredell, L.,2011. " Contributions to climate research using the AIRS Science Team version-5 products". In SPIE Optical Engineering Applications Vol. 8154.
  10. [Metz, Helen Chapin., 1993. "Iraq: A country study." Federal Research Division, Library of Congress.‏
  11. AL-Salihi, Ali M., Zehraa M. Hassan., 2015. "Temporal and Spatial Variability and Trend Investigation of Total Ozone Column over Iraq employing remote sensing Data". International Letters of Chemistry, Physics and Astronomy, Vol. 53, p. 1-18.‏
  12. “Giovanni.” [Online]. Available: https://giovanni.gsfc.nasa.gov/giovanni/. [Accessed: 02-Jan-2017].
  13. Shad, R., Mesgari, M. S. and Shad, A.,2009. " Predicting air pollution using fuzzy genetic linear membership kriging in GIS". Computers, Environment and Urban Systems, Vol.41, No.2, P. 472-481.
  14. Schroeder, L. D., Sjoquist, D. L. and Stephan, P. E.,2016. " Understanding regression analysis: An introductory guide". Sage Publications. pp. 96.
  15. Lin, C. C. J. and Seng, Z. P.,2009. " Development of the On-Site Earthquake Early Warning Systems for Taiwan Using Neural Networks". Intelligent Engineering Systems through Artificial Neural Networks. ASME Press. Vol.19, P. 107-113.
Iranica Journal of Energy & Environment
Volume 8, Issue 2
April 2017
Pages 160-169

Files

Share

How to cite

Statistics