Vol.28, NO.2, 2017
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Article Navigation >  Journal of Applied Meteorological Science  > 2017  >  28(2): 177-188
Liu Jian, Cui Peng, Xiao Meng. The bias analysis of FY-2G cloud fraction in summer and winter. J Appl Meteor Sci, 2017, 28(2): 177-188. DOI:  10.11898/1001-7313.20170205.
Citation: Liu Jian, Cui Peng, Xiao Meng. The bias analysis of FY-2G cloud fraction in summer and winter. J Appl Meteor Sci, 2017, 28(2): 177-188. DOI:  10.11898/1001-7313.20170205.
shu

The Bias Analysis of FY-2G Cloud Fraction in Summer and Winter

DOI: 10.11898/1001-7313.20170205

  • National Satellite Meteorological Center, Beijing 100081

  • Received Date: 2016-10-26
  • Rev Recd Date: 2017-01-17
  • Publish Date: 2017-03-31
    Abstract
  • Evaluation of satellite retrieval cloud fraction is fundamental for good use in operational weather analyses application. Cloud fraction relative biases between FY-2G and Aqua/MODIS data are investigated in order to validate FY-2G cloud fraction. In order to understand the accuracy of FY-2G cloud fraction better, cases contain both clear and cloud pixels in the target area are selected, and 80 matched cases are analyzed. Results show that the cloud fraction of FY-2G has the same distribution pattern with Aqua/MODIS. The mean cloud fraction of FY-2G is 72.81%, and according to MODIS data it is 76.19%. Among 80 selected cases, 45 cases are in June and 35 cases are in December of 2015. In June, the mean cloud fraction of FY-2G and Aqua is 68.12% and 70.78%, respectively. In December, the mean cloud fraction of FY-2G and Aqua is 78.84% and 83.14%. FY-2G's cloud fraction is smaller than that of Aqua. For all cases, there are 79.15% pixels that their absolute relative bias between FY-2G and Aqua is smaller than 15%. In June, there are 77.78% pixels that the absolute relative bias between FY-2G and Aqua is smaller than 15%, while it is 81.24% in December. The cloud fraction correlation coefficient between FY-2G and Aqua is 0.74 through the year, 0.76 in June and 0.72 in December.During daytime, the mean cloud fraction of FY-2G and Aqua is 70.42% and 72.21%, respectively. There are 67.74% pixels that the absolute deviation between FY-2G and Aqua is smaller than 5%. The cloud fraction correlation coefficient between FY-2G and Aqua is 0.754. For night time cases, FY-2G mean cloud fraction is 75.59% and Aqua is 80.81%. The cloud fraction correlation coefficient between FY-2G and Aqua is 0.73. There are 72.34% pixels that their cloud fraction absolute deviation between FY-2G and Aqua is smaller than 5% during nighttime.Results show that the cloud fraction bias between FY-2G and Aqua is mainly caused by cloud detection accuracy. The cloud detection bias between FY-2G and Aqua mainly comes from different satellite observation ability and cloud detection algorithm. Compared with Aqua/MODIS data that has 36 channels with the lowest 0.01°×0.01° nadir spatial resolution, FY-2G has 5 channels with the highest 0.05°×0.05° spatial resolution. FY-2G's cloud detection easily makes mistakes when it has broken cloud, thinner cirrus or not all covered by cloud in the view. At the same time, different data processing methods within data match processing also cause bias between different kinds of satellite data.
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  • Fig. 1  The space distribution of matched sample between FY-2G and Aqua

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    Fig. 2  The mean cloud fraction of matched cases of FY-2G and Aqua

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    Fig. 3  Histogram of absolute CFR bias between FY-2G and Aqua in Jun 2015(a) and Dec 2015(b)

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    Fig. 4  Spectral response function for FY-2G and Aqua/MODIS visible channel 1

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    Fig. 5  The image of IR1 brightness temperature (a) with CFR (b) of FY-2G at 1600 UTC 26 Jun 2015 and brightness temperature (c) with CFR (d) of Aqua at 1610 UTC 26 Jun 2015

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    Fig. 6  The cloud fraction of FY-2G and Aqua/MODIS at 1800 UTC 31 Dec 2015

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    Fig. 7  The scatter diagram of Aqua/MODIS cloud fraction calculated by the nearest distance (NN) and average area (MEAN) methods at 1800 UTC 31 Dec 2015

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    Fig. 8  The histogram of Aqua/MODIS cloud fraction calculated by the nearest distance (NN) and average area (MEAN) methods at 1800 UTC 31 Dec 2015

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    Table  1  EOS/MODIS bands used in MODIS cloud mask algorithm (from reference [20])

    通道序号 中心波长/μm 是否用于云检测 云检测中的作用
    1 0.659 是 云检测
    2 0.865 是 云检测
    3 0.470 是 烟尘检测
    4 0.555 是 积雪/冰检测
    5 1.240 是 烟尘检测
    6 1.640 是 Terra积雪/冰检测
    7 2.130 是 Aqua积雪/冰检测
    8 0.415 是 沙漠上云检测
    9 0.443 是 太阳耀斑区晴空检测
    10 0.490 否
    11 0.531 否
    12 0.565 否
    13 0.653 否
    14 0.681 否
    15 0.750 否
    16 0.865 否
    17 0.905 是 太阳耀斑区晴空检测
    18 0.936 是 太阳耀斑区晴空检测
    19 0.940 否
    26 1.375 是 薄卷云、高云检测
    20 3.750 是 陆地、太阳耀斑区晴空检测，积雪/冰、沙尘检测
    21 3.959 否 烟尘检测
    22 3.959 是 云检测
    23 4.050 否
    24 4.465 否
    25 4.515 否
    27 6.715 是 高云检测
    28 7.325 是 云检测
    29 8.550 是 云、沙尘、积雪检测
    30 9.730 否
    31 11.030 是 云、沙尘、积雪检测，陆地、太阳耀斑区晴空检测
    32 12.020 是 薄卷云检测，云、沙尘检测
    33 13.335 是 云检测
    34 13.635 否
    35 13.935 是 高云检测
    36 14.235 否
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    Table  2  Statistic properties of matched cloud fraction (CFR) cases between FY-2G and Aqua in Jun 2015

    个例序号 FY-2G观测时间* 样本量 CFR平均值/% 相关系数 标准差 绝对差小于5%样本百分比/%
    FY-2G Aqua FY-2G Aqua
    1 03T04:00 4080 53.42 47.99 0.76 45.25 40.40 46.25
    2 03T16:00 14624 48.80 69.11 0.54 46.36 37.67 78.97
    3 04T08:00 2736 82.96 86.54 0.83 33.89 25.23 67.50
    4 04T20:00 3186 59.72 58.76 0.81 43.91 43.91 59.76
    5 05T07:00 3465 75.54 87.69 0.63 36.44 22.47 53.16
    6 05T19:00 4233 74.97 71.45 0.79 37.84 35.98 52.49
    7 06T06:00 4524 77.54 83.45 0.86 36.24 21.96 54.24
    8 06T18:00 3608 77.41 83.76 0.92 39.48 33.27 80.85
    9 09T05:00 7564 90.82 88.09 0.82 26.31 26.52 78.22
    10 09T17:00 6600 87.62 94.50 0.58 27.14 13.21 71.58
    11 10T04:00 5039 37.66 52.75 0.77 45.59 41.55 27.81
    12 10T16:00 3456 68.06 75.55 0.86 18.90 32.78 56.17
    13 11T08:00 3589 88.24 89.94 0.78 28.35 22.46 72.89
    14 11T20:00 4346 88.68 80.02 0.77 27.00 32.43 67.19
    15 12T07:00 4554 67.62 74.14 0.89 44.28 34.45 59.71
    16 12T19:00 5106 82.18 78.91 0.82 32.95 30.74 45.48
    17 13T08:00 6104 79.36 60.54 0.62 12.57 22.07 60.15
    18 13T20:00 4806 80.01 81.08 0.93 36.83 31.22 68.46
    19 14T07:00 7027 84.42 72.11 0.80 35.12 34.77 23.68
    20 14T19:00 3240 88.12 89.51 0.88 28.38 25.58 82.50
    21 15T06:00 8176 83.85 78.42 0.66 33.00 34.59 59.16
    22 15T18:00 7252 68.34 72.36 0.85 43.69 35.45 57.62
    23 16T05:00 5848 73.81 85.41 0.83 41.59 23.86 62.98
    24 18T05:00 4958 47.66 34.99 0.79 44.24 44.31 74.46
    25 18T17:00 3471 63.67 68.02 0.90 44.72 35.41 45.10
    26 19T04:00 4560 44.80 46.24 0.79 44.63 38.96 49.18
    27 19T21:00 4144 93.30 90.12 0.79 21.58 23.28 79.80
    28 20T08:00 4601 61.36 50.77 0.81 44.03 39.62 43.90
    29 20T20:00 3000 75.06 82.78 0.85 39.79 31.03 66.36
    30 21T07:00 9748 57.05 65.33 0.79 44.94 37.97 76.71
    31 21T19:00 6965 27.21 35.11 0.82 39.94 41.24 41.52
    32 22T06:00 6231 96.88 98.45 0.65 14.28 6.14 90.98
    33 22T18:00 5886 35.52 65.87 0.65 45.03 38.76 77.54
    34 24T06:00 3400 70.43 45.58 0.72 42.44 39.57 43.82
    35 24T18:00 4080 87.36 91.87 0.72 28.73 16.73 69.00
    36 25T05:00 14700 73.57 73.30 0.66 39.69 35.45 85.32
    37 25T17:00 13431 48.24 57.96 0.74 46.02 38.74 90.85
    38 26T04:00 4970 58.53 52.68 0.87 46.20 40.82 52.26
    39 26T16:00 3744 93.92 96.89 0.57 21.32 11.17 88.07
    40 27T08:00 6137 80.98 97.76 0.63 35.03 6.07 69.94
    41 27T20:00 5080 89.23 97.52 0.62 28.18 11.67 84.55
    42 29T08:00 8256 2.34 1.02 0.73 12.08 7.15 94.63
    43 29T20:00 7885 89.78 95.92 0.67 26.99 13.10 80.28
    44 30T07:00 4788 11.44 8.25 0.67 24.45 16.58 70.24
    45 30T19:00 13527 38.11 66.72 0.61 45.38 42.39 92.86
     注：*观测时间为世界时，下同。
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    Table  3  Statistic properties of matched CFR cases between FY-2G and Aqua in Dec 2015

    个例序号 FY-2G观测时间* 样本量 CFR平均值/% 相关系数 标准差 绝对差小于5%样本百分比/%
    FY-2G Aqua FY-2G Aqua
    1 01T06:00 1620 89.02 92.31 0.59 20.36 22.61 76.96
    2 04T20:00 1230 55.05 72.91 0.74 45.58 39.07 54.88
    3 05T07:00 4857 45.03 65.66 0.56 47.07 40.40 47.79
    4 06T03:00 3612 74.09 83.79 0.67 38.77 31.42 61.70
    5 06T20:00 2646 95.24 95.18 0.72 18.92 16.84 88.51
    6 07T07:00 2193 95.78 94.71 0.65 17.45 17.68 86.82
    7 08T18:00 3149 81.97 95.34 0.52 36.05 19.25 79.10
    8 09T05:00 3010 75.85 78.14 0.81 40.40 38.00 78.27
    9 09T17:00 2844 92.40 93.68 0.84 25.21 22.31 92.26
    10 12T04:00 2310 43.58 62.31 0.76 47.37 43.4 60.39
    11 12T16:00 1769 69.36 82.8 0.71 42.36 30.67 62.63
    12 14T07:00 2329 74.93 86.81 0.57 39.56 26.79 61.75
    13 15T06:00 2021 92.46 96.03 0.65 22.88 13.67 84.17
    14 16T05:00 2610 94.69 94.75 0.81 19.47 18.77 89.51
    15 16T19:00 1247 92.16 92.17 0.62 23.9 20.77 78.43
    16 17T06:00 1410 41.82 45.72 0.75 46.01 42.92 55.53
    17 18T05:00 1690 67.94 63.44 0.88 41.71 46.19 67.16
    18 18T17:00 1290 88.06 92.76 0.86 30.06 22.5 85.35
    19 19T04:00 2065 91.37 92.94 0.71 24.68 18.87 80.48
    20 20T08:00 1073 79.10 80.58 0.87 38.38 37.24 82.47
    21 20T20:00 3889 84.90 83.59 0.81 33.54 31.24 74.67
    22 21T07:00 2067 84.19 90.92 0.80 34.87 23.71 81.28
    23 24T06:00 1891 72.69 75。00 0.94 43.28 41.15 88.42
    24 24T18:00 2881 97.95 92.03 0.56 10。00 24.77 87.82
    25 25T05:00 2009 92.79 96.13 0.72 24.14 16.88 90.54
    26 25T17:00 1480 97.29 94.24 0.52 11.37 18.7 85.88
    27 26T04:00 1768 76.65 79.19 0.94 41.22 38.57 87.56
    28 27T05:00 2090 72.03 69.08 0.87 41.39 39.51 63.06
    29 27T17:00 1692 73.85 80.22 0.75 38.48 29.03 53.25
    30 28T16:00 1802 98.52 97.62 0.60 9.71 10.52 92.17
    31 29T20:00 2257 82.87 87.87 0.78 34.21 30.13 81.44
    32 30T07:00 2472 75.27 85.67 0.74 39.63 29.84 69.98
    33 30T19:00 2520 50.26 55.16 0.64 43.90 44.25 50.63
    34 31T06:00 2448 88.67 90.56 0.77 28.83 24.89 81.94
    35 31T18:00 1519 71.46 70.72 0.56 40.47 40.08 55.23
    DownLoad: Download CSV

    Table  4  Pixels of cases on different cloud fraction level between FY-2G and Aqua/MODIS on 8 Dec and 25 Dec in 2015

    云量/% 12-08 12-25
    FY-2G像元数 Aqua像元数 FY-2G像元数 Aqua像元数
    [0, 10) 423 96 17 4
    [10, 20) 39 8 17 2
    [20, 30) 26 18 8 8
    [30, 40) 35 11 22 6
    [40, 50) 34 12 11 11
    [50, 60) 45 4 16 2
    [60, 70) 41 20 31 15
    [70, 80) 33 80 23 10
    [80, 100] 2473 2972 1335 1422
    DownLoad: Download CSV
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    • Received : 2016-10-26
    • Accepted : 2017-01-17
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