Identification of the maximum thickness of the dust layer based on CALIPSO satellite observations Case study: Khuzestan province

Document Type : Research Article

Authors

1 Department of natural geography, Faculty of Earth sciences, Shahid Beheshti University, Tehran, Iran.

2 Department of Space Physics, Institute of Geophysics, University of Tehran, Tehran, Iran.

3 Department of Remote Sensing & GIS, Faculty of Earth sciences, Shahid Beheshti University, Tehran, Iran.

Abstract

The main goal of this study is to identify the maximum thickness of the dust layer by using the vertical half-pipe method using CALIOP lidar data. In order to implement this method, Khuzestan province was considered as the target area. In this regard, 6 study samples that the CALIPSO satellite orbit had passed over Khuzestan province during October 2016 and 2017 were used.
Based on the results of this research, the maximum thickness of the dust layer in the study samples in the completely flat and plain land in the west of the province is more than in the east. In addition, in the east of the province, the values of the maximum thickness of the dust layer mainly correspond to the lower latitudes of the region, where the ground level is lower. In general, the findings of the research show that the maximum thickness of the dust layer can be extracted from the lidar data using the airborne vertical half-track method, although the output of this method is more accurate when the dust layer has uniform and dense horizontal and vertical coverage. In addition to extracting the thickness of the dust layer, the size and density of the particles were also studied using the color ratio index. The values of this index in each of the study samples confirmed the presence of fine dust particles in the region. This index was also calculated after applying the horizontal averaging method of 5 km on the redistribution data of lidar waves. The 5 km horizontal averaging method has the ability to effectively reduce the noise of space lidar data and increase the accuracy of cloud detection from aerials, which reveals the border between clean and polluted air and also the knowledge of the density of aerials. Based on this, the spatial distribution of the dust layer and changes in its thickness can be seen. The results of this research can be used to predict the concentration, extent, and maximum height of the dust layer.
According to the findings of the research, the proposed method has the ability to extract the maximum thickness of the dust layer using spatial lidar data, although the output of this method is more accurate when the dust layer has uniform and dense horizontal and vertical coverage. In this research, in addition to extracting the thickness of the dust layer, the size and density of the particles were also studied using the color ratio index. This index was calculated after applying the horizontal averaging method of 5 km on the redistribution data of radar waves. Based on this, the mentioned method clearly showed the density and concentration of dust particles through the distribution of lidar wave rescattering values at different heights, and the presence of dust particles in the atmosphere of the region was confirmed in all study samples. Calculating the particle size index in the atmosphere and its undeniable role in detecting dust particles is another result of this research. The values of this index in each of the studied samples in the height ranges where the thickness of the dust layer was extracted confirmed the presence of fine dust particles in the region.

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References

Eric, E., Wandjie, B. B. S., Lenouo, A., Monkam, D., & Manatsa, D. (2020). African summer monsoon active and break spells cloud properties: Insight from CloudSat-CALIPSO. Atmospheric Research, 237(August 2019), 104842.
Hostetler, C. A., Liu, Z., Reagan, J., Vaughan, M., Winker, D., Osborn, M., Hunt, W. H., Powell, K. A., & Trepte, C. (2006). CALIOP algorithm theoretical basis document, calibration and level 1 data products. Cloud-Aerosol Lidar Infrared Pathfinder Satellite Observations PC-SCI-201, April, 1–66.
Hu, Y., Winker, D., Vaughan, M., Lin, B., Omar, A., Trepte, C., Flittner, D., Yang, P., Nasiri, S.L., & Baum, B. (2009). CALIPSO/CALIOP Cloud Phase Discrimination Algorithm. J. Atmos. Oceanic Technol. 26, 2293–2309.
Iorio, T. Di, Sarra, A., Junkermann, W., Cacciani, M., Fiocco, G., & Fua, D. (2003). Tropospheric aerosols in the Mediterranean : 1 . Microphysical and optical properties. 108, 1–10.
Iorio, T., Di, Sarra, A., Sferlazzo, D. M., Cacciani, M., Meloni, D., Monteleone, F., & Fua, D. (2009). Seasonal evolution of the tropospheric aerosol vertical profile in the central Mediterranean and role of desert dust. 114, 1–9.
Kuma, P. (2010). Visualising Data from CloudSat and CALIPSO Satellites. 1–73.
Liu, Z., Omar, A., Vaughan, M., Hair, J., Kittaka, C., Hu, Y., Powell, K., Trepte, C., Winker, D., Hostetler, C., Ferrare, R., & Pierce, R. (2008). CALIPSO lidar observations of the optical properties of Saharan dust : A case study of long-range transport. 113, 1–20.
Liu, Z.Y., Vaughan, M., Winker, D., Kittaka, C., Getzewich, B., Kuehn, R., Omar, A., Powell, K., Trepte, C., & Hostetler, C. (2009). The CALIPSO Lidar Cloud and Aerosol Discrimination: Version 2 Algorithm and Initial Assessment of Performance. J. Atmos. Oceanic Technol. 26, 1198–1213.
Liu, B., Ma, Y., Gong, W., & Zhang, M. (2017). Observations of aerosol color ratio and depolarization ratio over Wuhan. Atmospheric Pollution Research, 8(6), 1113–1122.
Liu, B., Ma, Y., Liu, J., Gong, W., Wang, W., & Zhang, M. (2018). Graphics algorithm for deriving atmospheric boundary layer heights from CALIPSO data. 5075–5085.
Liu, J., Huang, J., Chen, B., Zhou, T., Yan, H., Jin, H., Huang, Z., & Zhang, B. (2014). Comparisons of PBL heights derived from CALIPSO and ECMWF reanalysis data over China. Journal of Quantitative Spectroscopy and Radiative Transfer, 1–11.
Omar, A., Liu, Z., Vaughan, M., Thornhill, K., Kittaka, C., Ismail, S., Hu, Y., Chen, G., Powell, K., Winker, D., Trepte, C., Winstead, E., & Anderson, B. (2010). Extinction ‐ to ‐ backscatter ratios of Saharan dust layers derived from in situ measurements and CALIPSO overflights during NAMMA. 115, 1–21.
Omar, A. H., Tackett, J., & Al-dousari, A. (2022). CALIPSO Observations of Sand and Dust Storms and Comparisons of Source Types near Kuwait City. 1–16.
Su, T., Li, J., Li, C., Xiang, P., Lau, A. K.-H., Guo, J., Yang, D., & Miao, Y. (2017). An intercomparison of long-term planetary boundary layer heights retrieved from CALIPSO, ground-based lidar, and radiosonde measurements over Hong Kong. J. Geophys. Res. Atmos., 122, 3929–3943.
Tesche, M., Ansmann, A., Müller, D., Althausen, D., Heese, B., Freudenthaler, V., Wiegner, M., Pisani, G., Knippertz, P., Tesche, M., Ansmann, A., Müller, D., Althausen, D., Mattis, I., Heese, B., Freudenthaler, V., Wiegner, M., & Esselborn, M. (2009). Tellus B : Chemical and Physical Meteorology Vertical profiling of Saharan dust with Raman lidars and airborne HSRL in southern Morocco during SAMUM
Vaughan, M. A., Winker, D. M., & Powell, K. A. (2005). CALIOP Algorithm Theoretical Basis Document Part 2 : Feature Detection and Layer Properties Algorithms. Science, September, 1–87.
Vaughan, M.A., Powell, K.A., Kuehn, R.E., Young, S.A., Winker, D.M., Hostetler, C.A., Hunt, W.H., Liu, Z.Y., McGill, M.J., & Getzewich, B.J. (2009). Fully Automated Detection of Cloud and Aerosol Layers in the CALIPSO Lidar Measurements. J. Atmos. Ocean. Technol. 26, 2034–2050.
Winker, D. M., Hunt, W. H., & McGill, M. J. (2007). Initial performance assessment of CALIOP. Geophysical Research Letters, 34(19), 1–5.
Winker, D. M., Vaughan, M. A., Omar, A., Hu, Y., Powell, K. A., Liu, Z., Hunt, W. H., & Young, S. A. (2009). Overview of the CALIPSO mission and CALIOP data processing algorithms. Journal of Atmospheric and Oceanic Technology, 26(11), 2310–2323.
Zhang, W., Guo, J., Miao, Y., Liu, H., Zhang, Y., Li, Z., & Zhai, P. (2016). Planetary boundary layer height from CALIOP compared to radiosonde over China. Atmos. Chem. Phys., 16, 9951–9963.
Journal of the Earth and Space Physics
Volume 50, Issue 1
online publication date: 5 May 2024
May 2024
Pages 149-164

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