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Acta Optica Sinica, Volume. 40, Issue 11, 1101002(2020)

Cloud Detection and Parameter Inversion Using Multi-Directional Polarimetric Observations

Yuyang Chang1,2, Bin Sun1、*, Chan Huang1,2, Jin Hong1, and Yanli Qiao1
Author Affiliations
  • 1Key Laboratory of Optical Calibration and Characterization, Anhui Institute of Optics and Fine Mechanics, Chinese Academy of Sciences, Hefei, Anhui 230031, China
  • 2University of Science and Technology of China, Hefei, Anhui 230026, China
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    AbstractGet PDF(in Chinese)
    Figures&Tables (12)
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    References (30)
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    Figures & Tables(12)
    Sensitivity of reflectance versus cloud optical thickness at 865 nm under different conditions. (a) Different surface albedos, the droplet effective radius of 10 μm; (b) different phases and effective radii, the surface albedo of 0.1

    Fig. 1. Sensitivity of reflectance versus cloud optical thickness at 865 nm under different conditions. (a) Different surface albedos, the droplet effective radius of 10 μm; (b) different phases and effective radii, the surface albedo of 0.1

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    Simulated geometric normalized polarized radiance above ice clouds with different heights

    Fig. 2. Simulated geometric normalized polarized radiance above ice clouds with different heights

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    Flowchart of the cloud detection algorithm

    Fig. 3. Flowchart of the cloud detection algorithm

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    Cloud detection results. (a) Reflectance at 865 nm (for the ocean) and 670 nm (for land); (b) cloud detection results obtained by our algorithm; (c) POLDER’s cloud mask products; (d) comparison of the consistency between both of the results

    Fig. 4. Cloud detection results. (a) Reflectance at 865 nm (for the ocean) and 670 nm (for land); (b) cloud detection results obtained by our algorithm; (c) POLDER’s cloud mask products; (d) comparison of the consistency between both of the results

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    normalized polarized radiance at 865 nm above ice clouds and water clouds. (a) Simulation; (b) POLDER's measured values

    Fig. 5. normalized polarized radiance at 865 nm above ice clouds and water clouds. (a) Simulation; (b) POLDER's measured values

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    Simulated normalized polarized radiance above a two-layer cloud

    Fig. 6. Simulated normalized polarized radiance above a two-layer cloud

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    Flowchart of the cloud thermodynamic phase judgement

    Fig. 7. Flowchart of the cloud thermodynamic phase judgement

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    Results of cloud thermodynamic phase judgement. (a) Thermodynamic phase distribution obtained by our method; (b) POLDER's thermodynamic phase products; (c) comparison of judgmental consistency

    Fig. 8. Results of cloud thermodynamic phase judgement. (a) Thermodynamic phase distribution obtained by our method; (b) POLDER's thermodynamic phase products; (c) comparison of judgmental consistency

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    Comparison of the inversion results of cloud optical thickness. (a) Cloud optical thickness obtained by our method; (b) POLDER's cloud optical thickness products; (c) relative deviation; (d) distributions of cloud optical thickness along longitudes in different latitudes; (e) correlation of inversion results

    Fig. 9. Comparison of the inversion results of cloud optical thickness. (a) Cloud optical thickness obtained by our method; (b) POLDER's cloud optical thickness products; (c) relative deviation; (d) distributions of cloud optical thickness along longitudes in different latitudes; (e) correlation of inversion results

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    • Table 1. Statistics on the consistency between the cloud detection results gained from this paper and POLDER's cloud mask products

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      Table 1. Statistics on the consistency between the cloud detection results gained from this paper and POLDER's cloud mask products

      Number of total pixelConsistency of cloudy pixelsConsistency of clear pixels
      4589593.3%92.9%

    • Table 2. Statistics of the consistency between the cloud phase judgement results obtained by our method and POLDER's products

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      Table 2. Statistics of the consistency between the cloud phase judgement results obtained by our method and POLDER's products

      Number of total pixelConsistency of “WC” pixelsConsistency of “IC” pixelsConsistency of “mixed phase” pixels
      2659087.4%76.6%22.8%

    • Table 3. Look up table for deriving cloud optical thickness

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      Table 3. Look up table for deriving cloud optical thickness

      ParameterValue
      Wavelength λ /nm670 (for land); 865 (for the ocean)
      Cloud thermodynamic phase for water cloudMie model and modified Gamma distribution with reff=10 μm
      Cloud thermodynamic phase of ice cloudYang13 model with reff=20 μm
      Optical thickness τ0,1,2,3,4,5,6,7,8,9,10,12,14,16,18,20,25,30,40,50
      Solar zenith θ0/(°)0,5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90
      Viewing zenith θV/(°)0,5,10,15,20,25,30,35,40,45,50,55,60,65,70,75,80,85,90
      Relative azimuth φ/(°)0, 45,90,135,180
      Albedo of land0,0.1,0.2,0.3,0.4,0.5,0.6,0.7,0.8,0.9,1.0
      BRDF of the oceanCos and Munk's model (1956)
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    Yuyang Chang, Bin Sun, Chan Huang, Jin Hong, Yanli Qiao. Cloud Detection and Parameter Inversion Using Multi-Directional Polarimetric Observations[J]. Acta Optica Sinica, 2020, 40(11): 1101002

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    Paper Information

    Category: Atmospheric Optics and Oceanic Optics

    Received: Jan. 9, 2020

    Accepted: Mar. 4, 2020

    Published Online: Jun. 10, 2020

    The Author Email: Sun Bin (sunbin09@mail.ustc.edu.cn)

    DOI:10.3788/AOS202040.1101002

    Topics

    laser devices and laser physics
    Lasers and Laser Optics
    Laser physics
    laser manufacturing
    Instrumentation, Measurement and Metrology