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Application of Cloud Phase Retrieval to Snowstorm in Akzo Using MODIS Data
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摘要: 该文选取新疆阿克苏地区2006年11月22—24日的EOS/MODIS卫星遥感资料，同时选取相关气象站的观测资料，应用三光谱云相态反演法，对阿克苏地区暴雪云团的变化过程进行了研究，通过三光谱亮温差点聚图对云相态变化的分析，清晰地展示出该天气系统的发生、发展到最终暴雪形成过程中的物理机制。反演结果与实际气象观测情况相一致；表明三光谱云相态反演法在理论和实际应用方面具有一定优势。Abstract: Using satellite remote sensing information, the distribution of cloud-top particle can be retrieved and the physical mechanism of certain weather phenomenon can be explained. Nowadays, cloud phase inversion method based on passive remote sensing instruments on 8 μm, 11 μm and 12 μm spectral bands (corresponding to three MODIS infrared spectral channels: Ch29, Ch31 and Ch32) are relatively practical, which is called tri-spectral cloud phase inversion method. EOS/MODIS satellite data in Akzo Prefecture of Xinjiang and observations from relevant weather station are analyzed to study a snowstorm. The change of cloud phase is revealed by tri-spectral brightness and temperature difference scatter diagram, which clearly shows the whole process of cloud clustering from water cloud to hybrid cloud, and finally to ice cloud. It also reveals the physical mechanism how this weather system occurs and finally develops to blizzard. It can be seen that the result from tri-spectral cloud phase inversion method is completely according with actual circumstance. Therefore, it can be concluded that tri-spectral cloud phase inversion method has a theoretical advantage and a wide application foreground in practice. Meanwhile, this method of MODIS data cloud phase recognition can be applied to analysis of blizzard clouds by FY-3 satellite.
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Key words:
- MODIS data;
- cloud phase retrieval;
- tri-spectral method
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图 1 2006年11月22—24日新疆库车县遥感影像图及所截分析窗
Fig. 1 Remote sensing images and analysis windows intercepted in Kuche County of Xinjiang from 22 to 24 in Nov 2006
图 2 2006年11月22—24日新疆库车县遥感数据分析窗的亮温差点聚图
Fig. 2 Scatter diagram of brightness temperature difference in remote sensing data analysis windows in Kuche County of Xinjiang from 22 to 24 in Nov 2006
表 1 不同像元的MODIS光谱通道 (反射率、辐射率) 特征 (单位：K)
Table 1 MODIS spectral channel characteristics of different pixels (reflectivity, emissivity)(unit:K)
分类 晴空 低云 薄卷云 深对流云 局地云团 积雪 通道1反射率 5.8～11.2 25.8～33.5 34.8～35.7 29.5～38.3 31.0～32.6 32.2～38.5 通道2反射率 7.0～13.8 29.2～37.8 37.2～38.5 31.7～41.2 33.2～34.6 34.7～40.7 通道6反射率 6.0～14.6 24.0～32.6 23.6～23.8 11.4～19.7 13.9～14.5 4.3～6.3 通道7反射率 4.6～12.3 18.6～22.2 16.0～16.5 6.7～12.7 8.7～8.4 1.4～2.7 通道18反射率 3.6～5.8 14.1～22.5 35.8～36.1 22.9～33.9 28.9～33.8 17.2～22.7 通道26辐射率 0.3～0.9 1.0～4.4 14.1～24.4 10.1～22.0 17.6～21.8 0.8～1.5 通道29辐射率 266.6～278.1 249.4～267.1 225.4～227.3 220.7～237.7 222.4～225.2 261.0～266.9 通道31辐射率 268.8～281.4 250.0～270.0 221.3～223.1 218.8～231.8 219.4～222.8 260.9～267.7 通道32辐射率 269.1～282.6 249.7～269.2 221.4～222.8 218.3～236.3 219.2～222.4 260.6～267.5 
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表 2 3个光谱离散度
Table 2 Three spectral dispersions
日期 天气现象 离散度/K R/% 通道29 通道31 通道32 2006-11-22 卷云积雨云晴空 12.81 2.58 2.54 45.6 2006-11-23 积雨云 8.03 8.41 8.35 3.5 2006-11-24 蔽光高层云 6.73 7.16 7.10 -7.1
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表 3 分析窗中光谱矩阵平均值及其分布
Table 3 The average spectral matrix and its distribution in analysis windows
日期 矩阵平均值/K 亮度温度差/K 亮温分布/K 冰云比例/% 通道29 通道31 通道32 BTD (29，31) ≤240 240～250 250～260 剔除 2006-11-22 257.0 257.2 256.9 -0.16 0 47 107 71 24.0 2006-11-23 245.8 245.1 244.7 0.73 56 139 30 0 73.3 2006-11-24 240.8 239.3 239.1 1.50 122 103 0 0 99.6
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