Implementation of Perez-Dumortier Calibration Algorithm

  • Jedol DayouEmail author
  • Jackson Hian Wui Chang
  • Justin Sentian
Part of the SpringerBriefs in Applied Sciences and Technology book series (BRIEFSAPPLSCIENCES)


To avoid the unnecessary needs to travel to high altitude for sunphotometers calibration, Perez-Dumotier calibration algorithm has been used as an objective means to select the right intensity data so that the calibration can be performed at any altitude levels. The governing theory of the algorithm was discussed in the previous chapter. This chapter presents information on how to implement the Perez-Dumotier calibration algorithm using actual field measurement. The implementation of the filtration procedure in step-by-step is discussed to render better framework of the proposed calibration algorithm. The aerosol retrieval inversion uses the extraterrestrial constant obtained from the final Langley plot to calculate retrieved AOD. The implementation example uses irradiance-matched technique by i-SMARTS radiative transfer code to derive corresponding reference AOD for validation purposes. The reliability of the technique was substantiated by radiative closure experiment to verify the promising direct solar irradiance to accurately derive the reference AOD values.


Langley extrapolation Irradiance-matched SMARTS Radiative transfer model 


  1. Acker JG, Leptoukh G (2007) Online analysis enhances use of NASA Earth Science Data. Trans Am Geophys Union 88:14–17CrossRefGoogle Scholar
  2. Chang JHW, Dayou J, Sentian J (2014) Development of near-sea-level calibration algorithm for aerosol optical depth measurement using ground-based spectrometer. Aerosol Air Qual Res 14:386–395Google Scholar
  3. Chang JHW, Dayou J, Sentian J (2013) Diurnal Evolution of Solar Radiation in UV, PAR and NIR Bands in High Air Masses. Nature, Environ Pollut Technol 12:1–6Google Scholar
  4. Holben BN, Eck TF, Slutske I et al (1998) AERONET—a federated instrument network and data archive for aerosol characterization. Remote Sens Environ 66:1–16CrossRefGoogle Scholar
  5. Kaskaoutis DG, Kambezidis HD (2008) The role of aerosol models of the SMARTS code in predicting the spectral direct-beam irradiance in an urban area. Renew Energy 33:1532–1543. doi: 10.1016/j.renene.2007.09.006 CrossRefGoogle Scholar
  6. Knobelspiesse KD, Pietras C, Fargion GS et al (2004) Maritime aerosol optical thickness measured by handheld sun photometers. Remote Sens Environ 93:87–106CrossRefGoogle Scholar
  7. Seidel FC, Kokhanovsky AA, Schaepman ME (2012) Fast retrieval of aerosol optical depth and its sensitivity to surface albedo using remote sensing data. Atmos Res 116:22–32. doi: 10.1016/j.atmosres.2011.03.006 CrossRefGoogle Scholar
  8. Shettle EP, Fenn RW (1979) Models for the aerosols of the lower atmosphere and the effects of humidity variations on their optical properties. AIR FORCE Geophys, LAB HANSCOM AFB MAGoogle Scholar
  9. Sumari SM, Darus FM, Kantasamy N et al (2009) Compositions of rainwater and aerosols at global atmospheric watch in Danum Valley, Sabah. Malaysia J Anal Sci 13:107–119Google Scholar
  10. Trivitayanurak W, Palmer PI, Barkley MP et al (2012) The composition and variability of atmospheric aerosol over Southeast Asia during 2008. Atmos Chem Phys 12:1083–1100. doi: 10.5194/acp-12-1083-2012 CrossRefGoogle Scholar
  11. Utrillas MP, Martinez-Lozano JA, Cachorro VE et al (2000) Comparison of aerosol optical thickness retrieval from spectroradiometer measurements and from two radiative transfer models. Sol Energy 68:197–205CrossRefGoogle Scholar

Copyright information

© The Author(s) 2014

Authors and Affiliations

  • Jedol Dayou
    • 1
    Email author
  • Jackson Hian Wui Chang
    • 2
  • Justin Sentian
    • 3
  1. 1.School of Science and TechnologyUniversiti Malaysia SabahKota KinabaluMalaysia
  2. 2.School of Science and TechnologyUniversiti Malaysia SabahKota KinabaluMalaysia
  3. 3.School of Science and TechnologyUniversiti Malaysia SabahTaman Putera Jaya TelipokMalaysia

Personalised recommendations