| 摘要: |
| 为了提高使用SSM/I 资料反演全球海面风速的精度, 发展了一个新型的神经网络方法。在这个方法中, 使用高风速、中、低风速状态和天气状态分类的方法分别训练神经网络, 然后根据其类别的不同使用不同的神经网络计算风速。此方法较好地去除了由于高风速和云天天气状态下训练样本数据的缺少所产生的误差, 改进了在高风速状态下反演风速值比实际风速偏低的情况, 使得反演的高风速值被校正到了正常位置。本方法反演海面风速的值与浮标实测风速值之间的均方根误差达到1.60m/s。
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| 关键词: 神经网络, SSM/I 资料, 海面风速 |
| DOI：10.11693/hyhz200902003003 |
| 分类号: |
| 基金项目:国家863 计划资助项目, 2001AA633060 号和国家自然科学基金资助项目, 40276050 号 |
附件 |
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| AN IMPROVED ALGORITHM FOR SEA SURFACE WIND SPEED RETRIEVAL OF A CLASSIFIED NEURAL NETWORK |
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MENG Lei, HE Yi-Jun, WU Yu-Mei
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Institute of Oceanology, Chinese Academy of Sciences
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| Abstract: |
| A new neural network algorithm is developed to improve the retrieval precision of the global sea surface wind speed from the SSM/I brightness data. At first, the data in different conditions, such as high-speed and low-speed winds, and clear and cloudy weather, are used to train different neural networks. Then these neural networks are used independently to retrieve the sea surface wind speed. Compared with the buoy wind, the RMS (root mean square) error of the retrieving is about 1.60m/s. This method reduces the bias resulted from the lack of quality data in high-speed wind, and cloudy weather on the neural network algorithm.
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| Key words: Neural network, SSM/I data, Sea surface wind speed |
