2449 cloud classification samples are artificially selected from infrared channel 1, infrared channel 2 and water vapor channel of VISSR on FY-2C geostationary meteorological satellite during 2005-2009. Different linear combination of three channels are selected as feature values, which are brightness temperature of IR1, IR2, WV, and brightness temperature difference of IR1 to IR2, IR1 to WV, IR2 to WV. According to statistical theory, the sample probability distribution is assumed to help to remove some apparent unreasonable data such as outliers, and to understand cloud normal features better. It is found that the brightness temperature difference of IR1 to IR2 is most sensitive to the amount of thin cirrus cloud in 6 selected values. On the other hand, the error of the BP neural network model mostly comes from the contradiction of this feature too. A nested BP artificial neural network model is designed, and it's composed of two layers. The first layer includes five features of brightness temperature of IR1, IR2, WV, and brightness temperature difference of IRl to WV, IR2 to WV that are used to classify each pixel to one of four categories such as clear, mixed cloud, thick cirrus cloud and strong convective cloud. And the second layer includes just one feature, brightness temperature difference of IRl to IR2 that are used to classify mixed cloud to low-level cloud, mid-level cloud or thin cirrus cloud. Finally, every pixel is classified into one of total 6 categories corresponding to each color. Both layers adopt a BP neural network, the most widely used algorithm for generating classifiers, with one hidden layer and the additional momentum method, not only accelerating the training speed, but also reducing the redundancy of the networks.rnError analysis shows that the accuracy rates of the nested BP artificial neural network for types of mid-level cloud and thin cirrus cloud have increased by 42. 6% and 11. 3%, respectively. The mean square error and normalized mean square error of the whole classification model have decreased by 6. 1 % and 44. 7% with the correlation coefficient increasing by 3. 4%. By comparison of the classification results from 3 tests of tropical, subtropical areas and tropical cyclone, it shows that the nested model identifies thin cirrus cloud more accurately than the traditional model. Therefore, the results of the nested model are more reasonable than the traditional model.%采用2005-2009年FY-2C静止气象卫星可见光和红外自旋扫描辐射计的红外1(IR1)、红外2(IR2)和水汽(WV)亮温资料,选取2449个云分类样本.设计两层嵌套的前向传递后向反馈(BP)人工神经网络模型,第1层网络选取IR1,IR2,WV亮温及IR1与WV亮温差和IR2与WV亮温差5个特征量,第2层网络选取特征量IR1与IR2亮温差,两层网络都采用一层隐含层且带有附加动量法的简单网络,降低了网络的冗余度.误差分析表明:嵌套BP人工神经网络模型的分类准确率在中云和薄卷云这两类上分别提高了42.7％和11.3％,整个分类模型的平均平方误差和标准化平均平方误差分别降低了6.1％和44.7％,相关系数提高了3.4％.通过3个个例的对比分析发现,嵌套模型的分类结果比传统模型的分类结果更合理,特别是在中低云和薄卷云的云量和位置分辨能力上有了较大提高.
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