Chen Yingying, Tang Renmao, Zhou Yuquan, et al. Interpretation of cloud classification using the color image composed by three-channel data. J Appl Meteor Sci, 2011, 22(6): 691-697.
Citation: Chen Yingying, Tang Renmao, Zhou Yuquan, et al. Interpretation of cloud classification using the color image composed by three-channel data. J Appl Meteor Sci, 2011, 22(6): 691-697.

Interpretation of Cloud Classification Using the Color Image Composed by Three-channel Data

  • Received Date: 2010-11-05
  • Rev Recd Date: 2011-10-08
  • Publish Date: 2011-12-31
  • Clouds are the result of atmosphere dynamical and thermo-dynamical process. Different cloud types reflect different weather situations and cloud microphysical structure features. Satellite image offers large-area and all-day information of the cloud formation, gathering or disperse. Cloud classification is one of the most important objectives of the satellite image research.At present, only one single channel can be used to identify cloud category at one time. A quick, direct and accurate method of the interpretation of cloud classification has not yet been developed. It becomes particularly important to get the composed information of cloud optical thickness, effective particle radius and cloud top height information quickly.In order to better use the multiple spectral data of Medium Resolution Spectral Imager (MERSI) on FY-3A meteorological satellite to carefully analyze the macro and micro physical parameters of cloud system, according to the fact that the 0.65, 1.6 μm and 11.25 μm channel on MERSI is respectively sensitive to cloud optical thickness, effective particle radius and cloud top height based on the Santa Barbara DISORT Atmospheric Radiative Transfer, SBDART, which is that larger 0.65 μm reflectance means larger optical thickness, larger 1.6 μm reflectance means smaller effective particle radius, larger black body temperature of cloud top means lower cloud top height or warmer surface under clear air, the method of three color compositions is used to the explanation and interpretation of the cloud classification by FY-3A meteorological satellite data on 20 June 2009. The color is composed of red for visible reflectance, green for near infrared reflectance, and blue for the infrared brightness temperature. Redder means larger optical thickness, greener represents the smaller cloud top particles, and bluer means lower cloud tops or warmer surface under clear air.The technology makes different cloud types show in different colors, which is beneficial to directly distinguish cloud pixel from clear sky area, and the land boundary also can be recognized. In this case, cloud located at typhoon eye mainly shows orange-red, while spiral clouds band shows orange-yellow, indicating the reduced optical thickness compared with the typhoon eye. Mixed cloud system covering the Yangtze River Basin shows clear multiple-layer features, low layer cloud is yellow-green, middle layer cloud is dark-red, and high layer cloud shows orange-red. Over the sea, thin dark-yellow cirrus covers the marine stratiform cloud, which is white. Blue means clear air, but there is difference between sea and land because of the different temperature and reflectance.Besides the advantage of multiple spectrums, the 1000 m even 250 m resolution of FY-3A meteorological satellite makes the cloud detailed structure more clearly. The two advantages have greatly improved the accuracy of cloud classification.Meanwhile, the characteristic values for typical cloud, providing empirical values for the initial clustering center of fuzzy clustering method.
  • Fig. 1  Reflection function as a function of optical thickness and effective particle radius (unit:μm) at a wavelength for 0.65 μm and 1.6 μm when θ0=20°, θ=40°, ϕ=0°

    Fig. 2  Color image composed by three-channel data at 10:45 20 June 2009

    Fig. 3  Cloud classification daily product of FY-3A VIRRX

    Fig. 4  Images of typhoon cloud system (a), continental multiple-layer cloud system (b) with matine stratiform, cirrus and clear-air land, ocean (c)

    Table  1  Brightness temperature as a function of cloud top height solar zenith angle θ0=20°, satellite zenith angle θ=40°, relative azimuth angle ϕ=0°

    云底高度~云顶高度/km 1~4 2~5 3~6 4~7 5~8 6~9 7~10
    云顶黑体亮温/K 272.05 266.14 260.03 253.85 247.63 241.4 235.15
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    Table  2  Reflectance of some main types of the cloud and underlying surface[15]

    云和地面目标物 可见光通道反射率
    积雨云 (大而厚) 0.92
    积雨云 (小,云顶在6 km左右) 0.86
    卷层云 (厚,下面有中低云和降水) 0.74
    卷层云 (单独出现在陆地上空) 0.32
    积云 (出现在陆地上空,云量>80%) 0.69
    中云 (高层、高积云,中等厚度) 0.68
    层积云 (出现在陆地上空,云量>80%) 0.68
    层积云 (出现在洋面上空,成片) 0.60
    层云 (厚,出现在洋面上空) 0.64
    层云 (薄,出现在洋面上空) 0.42
    卷云 (薄,单独出现在陆地上空) 0.36
    晴天积云 (出现在陆地上空,云量>80%) 0.29
    陆地 (植被) 0.18
    海洋、湖泊、河流 0.07~0.09
    DownLoad: Download CSV

    Table  3  Effective particle radius of different cloud types

    云类 层云 (海洋上空) 层云 (陆地上空) 晴天积云 海洋积云 积雨云 浓积云 高层云
    re/μm 17 10 6.7 25 33 40 8
    DownLoad: Download CSV

    Table  4  Characteristic three-channel values of the typical cloud system pixel

    编码 RGB色彩模式强度值 R0.65 R1.64 T11.25 云的分类解释判读
    A (248,98,37) 0.975 0.311 209.298 积雨云(台风眼墙)
    B (139,98,36) 0.532 0.311 208.344 厚卷云(台风螺旋云带)
    C (234,92,30) 0.918 0.289 202.21 积雨云(局地对流)
    D (182,75,69) 0.707 0.227 233.568 中云(浓积云)
    E (178,229,128) 0.691 0.793 263.753 厚低云(云体深厚的水云)
    F (108,95,60) 0.406 0.3 227.657 薄卷云
    G (78,122,193) 0.284 0.4 288.383 薄低云(水云)
    H (35,42,192) 0.110 0.105 288.044 晴天海洋
    I (44,89,218) 0.146 0.278 296.566 晴天陆地
    DownLoad: Download CSV
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    • Received : 2010-11-05
    • Accepted : 2011-10-08
    • Published : 2011-12-31

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