Improvement and Application of Artificial Neural Networks to Cloud Classification

Zhang Zhenhua; Miao Chunsheng; Zeng Zhihua; Shi Chunxiang

Vol.23, NO.3, 2012

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Article Navigation > Journal of Applied Meteorological Science > 2012 > 23(3): 355-363

Zhang Zhenhua, Miao Chunsheng, Zeng Zhihua, et al. Improvement and application of artificial neural networks to cloud classification. J Appl Meteor Sci, 2012, 23(3): 355-363.

Citation:

Zhang Zhenhua, Miao Chunsheng, Zeng Zhihua, et al. Improvement and application of artificial neural networks to cloud classification. J Appl Meteor Sci, 2012, 23(3): 355-363.

Zhang Zhenhua, Miao Chunsheng, Zeng Zhihua, et al. Improvement and application of artificial neural networks to cloud classification. J Appl Meteor Sci, 2012, 23(3): 355-363.

Citation:

Zhang Zhenhua, Miao Chunsheng, Zeng Zhihua, et al. Improvement and application of artificial neural networks to cloud classification. J Appl Meteor Sci, 2012, 23(3): 355-363.

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Improvement and Application of Artificial Neural Networks to Cloud Classification

1.
College of Atmospheric Science, Nanjing University of Information Science & Technology, Nanjing 210044
2.
Shanghai Typhoon Institute, CMA, Shanghai 200030
3.
National Meteorological Information Center, Beijing 100081

Received Date: 2011-09-17
Rev Recd Date: 2012-02-06
Publish Date: 2012-06-30

Abstract

Abstract

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 IR1 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 IR1 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.Error 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.
- cloud classification;
- FY-2C meteorological satellite;
- BP neural network

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References(30)

Figures and Tables

Fig. 1 The probability distribution of cloud sample features

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Fig. 2 The diagram of the BP artificial neural network model

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Fig. 3 The independent features of cloud samples

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Fig. 4 The diagram of the nested BP neural network model

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Fig. 5 The comparison of accuracy rates of the nested and traditional models

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Fig. 6 The comparison of cloud classification products by the nested and traditional BP neural network models

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Table 1 Specifications of VISSR channels

通道标号	通道名称	光谱范围/μm	空间分辨率/km
1	IR1	10.3~11.3	5
2	IR2	11.5~12.5	5
3	WV	6.3~7.6	5
4	IR4	3.5~4.0	5
5	VIS	0.55~0.90	1.25

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Table 2 The information of classes and samples

分类	样本数	备注
强对流云	515	积雨云和发展旺盛的对流云
厚卷云	244	较厚的密卷云和积雨云的卷云毡
薄卷云	232	毛卷云、卷积云和较薄的密卷云
中云	214	高层云、高积云
低云	404	层积云、层云、雨层云
海洋	435	晴空下的海洋
陆地	405	晴空下的陆地

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Table 3 The selected features of the cloud classification

云分类特征	描述
IR1亮温	红外1通道的云顶亮温
IR2亮温	红外2通道的云顶亮温
WV亮温	水汽通道的云顶亮温
IR1与IR2亮温差	红外1和红外2通道的分裂窗差
IR1与WV亮温差	红外1和水汽通道的亮温差
IR2与WV亮温差	红外2和水汽通道的亮温差

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Table 4 The accuracy rates of test samples (unit: %)

云分类	晴空	低云	中云	薄卷云	厚卷云	对流云
晴空	97.379	2.620	0	0	0	0
低云	3.465	92.574	3.960	0	0	0
中云	0	31.250	67.708	0	1.042	0
薄卷云	0	9.538	5.660	82.915	1.887	0
厚卷云	0	0	0.806	0	87.903	11.290
对流云	0	0	0	0	6.179	93.820

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Table 5 The parameters of each cloud classifier

网络类型	输出层		隐含层		附加动量
网络类型	步长	层数	神经元个数	步长	附加动量
传统BP网络	0.1	1	16	1	0.7
嵌套网络第1层	0.1	1	9	1	0.7
嵌套网络第2层	0.1	1	6	1	0.7

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Table 6 Accuracy rates of the traditional and nested models

网络类型	E_MS	E_NMS	r
传统网络	0.0479	0.1133	0.9385
嵌套网络	0.045	0.0627	0.9708

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