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Proceedings Paper

A deep belief network approach using VDRAS data for nowcasting
Author(s): Lei Han; Jie Dai; Wei Zhang; Changjiang Zhang; Hanlei Feng
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Paper Abstract

Nowcasting or very short-term forecasting convective storms is still a challenging problem due to the high nonlinearity and insufficient observation of convective weather. As the understanding of the physical mechanism of convective weather is also insufficient, the numerical weather model cannot predict convective storms well. Machine learning approaches provide a potential way to nowcast convective storms using various meteorological data. In this study, a deep belief network (DBN) is proposed to nowcast convective storms using the real-time re-analysis meteorological data. The nowcasting problem is formulated as a classification problem. The 3D meteorological variables are fed directly to the DBN with dimension of input layer 6*6*80. Three hidden layers are used in the DBN and the dimension of output layer is two. A box-moving method is presented to provide the input features containing the temporal and spatial information. The results show that the DNB can generate reasonable prediction results of the movement and growth of convective storms.

Paper Details

Date Published: 10 April 2018
PDF: 8 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106155F (10 April 2018); doi: 10.1117/12.2303475
Show Author Affiliations
Lei Han, Ocean Univ. of China (China)
Zhejiang Normal Univ. (China)
Jie Dai, Ocean Univ. of China (China)
Wei Zhang, Ocean Univ. of China (China)
Changjiang Zhang, Zhejiang Normal Univ. (China)
Hanlei Feng, Ocean Univ. of China (China)


Published in SPIE Proceedings Vol. 10615:
Ninth International Conference on Graphic and Image Processing (ICGIP 2017)
Hui Yu; Junyu Dong, Editor(s)

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