| FY-2G卫星冬夏云量产品偏差分析 |
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| 引用本文： | 刘健,崔鹏,肖萌.FY-2G卫星冬夏云量产品偏差分析[J].应用气象学报,2017,28(2):177-188. |
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| 作者姓名： | 刘健 崔鹏 肖萌 |
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| 作者单位： | 国家卫星气象中心, 北京 100081 |
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| 基金项目： | 国家自然科学基金项目（41175022，61531019），国家科技支撑计划项目（2012BAC22B05），公益性行业（气象）科研专项（GYHY-201406032） |
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| 摘 要： | 开展卫星反演云量的精度评估是业务应用的基础，也是充分发挥卫星观测效益的前提。利用同类卫星产品EOS Aqua/MODIS云产品，选取2015年6月和12月共80个个例，包括43个白天个例，37个夜间个例。采用交叉比对方法对FY-2G云量产品进行相对偏差分析。结果表明:FY-2G与Aqua/MODIS计算云量总体趋势相当，无论从时间分布（白天和夜间）还是季节分布（6月和12月）上看，FY-2G与Aqua云量相对偏差较为稳定，FY-2G反演云量小于Aqua/MODIS反演云量。匹配个例中FY-2G平均云量为72.81%，Aqua/MODIS平均云量是76.19%，两者相关系数为0.74。两者绝对偏差小于5%的像元比例为72.34%；云量偏差绝对值小于15%的像元比例为79.51%。FY-2G与Aqua/MODIS云量偏差主要来自各自卫星的观测能力与所采用的云检测算法。与具有36个探测通道、星下点最低空间分辨率为0.01°×0.01°的Aqua/MODIS观测数据相比，FY-2G所具有的5通道、星下点最高空间分辨率为0.05°×0.05°的观测数据会出现对云，尤其是破碎云和薄卷云的漏检。两种具有不同时空属性的数据在匹配处理时采用的不同算法也会在比对分析中引入偏差。
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| 关 键 词： | FY-2G Aqua 云量 偏差 |
| 收稿时间： | 2016/10/26 0:00:00 |
| 修稿时间： | 2017/1/17 0:00:00 |
The Bias Analysis of FY-2G Cloud Fraction in Summer and Winter |
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| Liu Jian,Cui Peng and Xiao Meng.The Bias Analysis of FY-2G Cloud Fraction in Summer and Winter[J].Quarterly Journal of Applied Meteorology,2017,28(2):177-188. |
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| Authors: | Liu Jian Cui Peng and Xiao Meng |
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| Institution: | National Satellite Meteorological Center, Beijing 100081 |
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| 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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| Keywords: | FY-2G Aqua cloud fraction bias |
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