Jackson H.W. Chang 1, Nurul H.N. Maizan2, Fuei Pien Chee2, Jedol Dayou2

Preparatory Center for Science and Technology, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia
Energy, Vibration and Sound Research Group (e-VIBS), Faculty science and Natural Resources, Universiti Malaysia Sabah, Jalan UMS, 88400 Kota Kinabalu, Sabah, Malaysia

Received: October 22, 2016
Revised: January 15, 2018
Accepted: January 15, 2018
Download Citation: ||https://doi.org/10.4209/aaqr.2016.10.0455  

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Cite this article:
Chang, J.H., Maizan, N.H., Chee, F.P. and Dayou, J. (2018). Langley Calibration of Sunphotometer using Perez’s Clearness Index at Tropical Climate. Aerosol Air Qual. Res. 18: 1103-1117. https://doi.org/10.4209/aaqr.2016.10.0455


  • Cloud loading leads to overestimation and underestimation in Langley calibration.
  • Fictitious Langley plot is likely to happen when cloudy data is included.
  • High correlation on Langley plot is not robust enough for good Langley plot.
  • Gradual evolution pattern on Perez index is observed for good Langley plot.
  • Characterization of Perez index in time-series offers a way to improve Langley plot.


In the tropics, Langley calibration is often complicated by abundant cloud cover. The lack of an objective and robust cloud screening algorithm in Langley calibration is often problematic, especially for tropical climate sites where short, thin cirrus clouds are regular and abundant. Errors in this case could be misleading and undetectable unless one scrutinizes the performance of the best fitted line on the Langley regression individually. In this work, we introduce a new method to improve the sun photometer calibration past the Langley uncertainty over a tropical climate. A total of 20 Langley plots were collected using a portable spectrometer over a mid-altitude (1,574 m a.s.l.) tropical site at Kinabalu Park, Sabah. Data collected were daily added to Langley plots, and the characteristics of each Langley plot were carefully examined. Our results show that a gradual evolution pattern of the calculated Perez index in a time-series was observable for a good Langley plot, but days with poor Langley data basically demonstrated the opposite behavior. Taking advantage of this fact, the possibly contaminated data points were filtered by calculating the Perez derivative of each distinct air mass until a negative value was obtained. Any points that exhibited a negative derivative were considered bad data and discarded from the Langley regression. The implementation was completely automated and objective, rendering qualitative observation no longer necessary. The improved Langley plot exhibits significant improvement in addressing higher values for correlation, R, and lower values for aerosol optical depth, τa. The proposed method is sensitive enough to identify the occurrence of very short and thin cirrus clouds and is particularly useful for sun-photometer calibration over a tropical climate.

Keywords: Langley calibration; Perez model; Sunphotometer; Tropical climate; Aerosol optical depth.


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