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

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

机构地区：[1]National Hulunber Grassland Ecosystem Observation and Research Station, Institute of Agricultural Resources and Regional Planning, Chinese Academy of Agricultural Sciences [2]Guangzhou Institute of Geography [3]Department of Geography, University of Toronto [4]Hydrometeorology and Remote Sensing Laboratory, University of Oklahoma [5]Institute No. 503 of China Academy of Space Technology [6]School of Instrumentation Science and Opto-electronics Engineering, Beijing University of Aeronautics & Astronautics

出　　处：《Chinese Geographical Science》2014年第5期599-606,共8页中国地理科学（英文版）

基　　金：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)

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 learn- ing neural network was used to retrieve snow depth. The difference of brightness temperatures of TB18.7v and TB36.sv, TBI8.7H and TB36.sH, TB23,sv and TB89v, TBz3.8H 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.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 TB89 V, TB23.8H and TB89 H 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.

关 键 词：SNOWSTORM neural network snow depth passive microwave Advanced Microwave Scanning Radiometer-Earth Observing System （AMSR-E） 

分 类 号：P426.63[天文地球—大气科学及气象学]