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A Neural Network Method for Monitoring Snowstorm:A Case Study in Southern China

MAO Kebiao MA Ying XIA Lang SHEN Xinyi SUN Zhiwen HE Tianjue ZHOU Guanhua

MAO Kebiao, MA Ying, XIA Lang, SHEN Xinyi, SUN Zhiwen, HE Tianjue, ZHOU Guanhua. A Neural Network Method for Monitoring Snowstorm:A Case Study in Southern China[J]. 中国地理科学, 2014, (5): 599-606. doi: 10.1007/s11769-014-0675-4
引用本文: MAO Kebiao, MA Ying, XIA Lang, SHEN Xinyi, SUN Zhiwen, HE Tianjue, ZHOU Guanhua. A Neural Network Method for Monitoring Snowstorm:A Case Study in Southern China[J]. 中国地理科学, 2014, (5): 599-606. doi: 10.1007/s11769-014-0675-4
MAO Kebiao, MA Ying, XIA Lang, SHEN Xinyi, SUN Zhiwen, HE Tianjue, ZHOU Guanhua. A Neural Network Method for Monitoring Snowstorm:A Case Study in Southern China[J]. Chinese Geographical Science, 2014, (5): 599-606. doi: 10.1007/s11769-014-0675-4
Citation: MAO Kebiao, MA Ying, XIA Lang, SHEN Xinyi, SUN Zhiwen, HE Tianjue, ZHOU Guanhua. A Neural Network Method for Monitoring Snowstorm:A Case Study in Southern China[J]. Chinese Geographical Science, 2014, (5): 599-606. doi: 10.1007/s11769-014-0675-4

A Neural Network Method for Monitoring Snowstorm:A Case Study in Southern China

doi: 10.1007/s11769-014-0675-4
基金项目: Under the auspices of National Program on Key Basic Research Project (No. 2010CB951503), National Key Technology R & D Program of China (No. 2013BAC03B00), National High Technology Research and Development Program of China (No. 2012AA120905)
详细信息
    通讯作者:

    MAO Kebiao

A Neural Network Method for Monitoring Snowstorm:A Case Study in Southern China

Funds: Under the auspices of National Program on Key Basic Research Project (No. 2010CB951503), National Key Technology R & D Program of China (No. 2013BAC03B00), National High Technology Research and Development Program of China (No. 2012AA120905)
More Information
    Corresponding author: MAO Kebiao
  • 摘要: It has been observed that low temperature, rainfall, snowfall, frost have never occurred over the past 50 years in the southern China, and weather in this area is very complex, so the monitoring equipments are few. Optical and thermal infrared remote sensing is influenced much by clouds, so the passive microwave Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) data are the best choice to monitor and analyze the development of disaster. In order to improve estimation accuracy, the dynamic learning neural network was used to retrieve snow depth. The difference of brightness temperatures of TB18.7V and TB36.5V, TB18.7H and TB36.5H, TB23.8V and TB89V, TB23.8H and TB89H are made as four main input nodes and the snow depth is the only one output node of neural network. The mean and the standard deviation of retrieval errors are about 4.8 cm and 6.7 cm relative to the test data of ground measurements. The application analysis indicated that the neural network can be utilized to monitor the change of snow intensity distribution through passive microwave data in the complex weather of the southern China.
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  • 收稿日期:  2013-02-26
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A Neural Network Method for Monitoring Snowstorm:A Case Study in Southern China

doi: 10.1007/s11769-014-0675-4
    基金项目:  Under the auspices of National Program on Key Basic Research Project (No. 2010CB951503), National Key Technology R & D Program of China (No. 2013BAC03B00), National High Technology Research and Development Program of China (No. 2012AA120905)
    通讯作者: MAO Kebiao

摘要: It has been observed that low temperature, rainfall, snowfall, frost have never occurred over the past 50 years in the southern China, and weather in this area is very complex, so the monitoring equipments are few. Optical and thermal infrared remote sensing is influenced much by clouds, so the passive microwave Advanced Microwave Scanning Radiometer-Earth Observing System (AMSR-E) data are the best choice to monitor and analyze the development of disaster. In order to improve estimation accuracy, the dynamic learning neural network was used to retrieve snow depth. The difference of brightness temperatures of TB18.7V and TB36.5V, TB18.7H and TB36.5H, TB23.8V and TB89V, TB23.8H and TB89H are made as four main input nodes and the snow depth is the only one output node of neural network. The mean and the standard deviation of retrieval errors are about 4.8 cm and 6.7 cm relative to the test data of ground measurements. The application analysis indicated that the neural network can be utilized to monitor the change of snow intensity distribution through passive microwave data in the complex weather of the southern China.

English Abstract

MAO Kebiao, MA Ying, XIA Lang, SHEN Xinyi, SUN Zhiwen, HE Tianjue, ZHOU Guanhua. A Neural Network Method for Monitoring Snowstorm:A Case Study in Southern China[J]. 中国地理科学, 2014, (5): 599-606. doi: 10.1007/s11769-014-0675-4
引用本文: MAO Kebiao, MA Ying, XIA Lang, SHEN Xinyi, SUN Zhiwen, HE Tianjue, ZHOU Guanhua. A Neural Network Method for Monitoring Snowstorm:A Case Study in Southern China[J]. 中国地理科学, 2014, (5): 599-606. doi: 10.1007/s11769-014-0675-4
MAO Kebiao, MA Ying, XIA Lang, SHEN Xinyi, SUN Zhiwen, HE Tianjue, ZHOU Guanhua. A Neural Network Method for Monitoring Snowstorm:A Case Study in Southern China[J]. Chinese Geographical Science, 2014, (5): 599-606. doi: 10.1007/s11769-014-0675-4
Citation: MAO Kebiao, MA Ying, XIA Lang, SHEN Xinyi, SUN Zhiwen, HE Tianjue, ZHOU Guanhua. A Neural Network Method for Monitoring Snowstorm:A Case Study in Southern China[J]. Chinese Geographical Science, 2014, (5): 599-606. doi: 10.1007/s11769-014-0675-4
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