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Strong convective storm nowcasting using a hybrid approach of convolutional neural network and hidden Markov model
Author(s): Wei Zhang; Ling Jiang; Lei Han
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Paper Abstract

Convective storm nowcasting refers to the prediction of the convective weather initiation, development, and decay in a very short term (typically 0 ~ 2 h) .Despite marked progress over the past years, severe convective storm nowcasting still remains a challenge. With the boom of machine learning, it has been well applied in various fields, especially convolutional neural network (CNN). In this paper, we build a servere convective weather nowcasting system based on CNN and hidden Markov model (HMM) using reanalysis meteorological data. The goal of convective storm nowcasting is to predict if there is a convective storm in 30min. In this paper, we compress the VDRAS reanalysis data to low-dimensional data by CNN as the observation vector of HMM, then obtain the development trend of strong convective weather in the form of time series. It shows that, our method can extract robust features without any artificial selection of features, and can capture the development trend of strong convective storm.

Paper Details

Date Published: 10 April 2018
PDF: 10 pages
Proc. SPIE 10615, Ninth International Conference on Graphic and Image Processing (ICGIP 2017), 106155E (10 April 2018); doi: 10.1117/12.2302689
Show Author Affiliations
Wei Zhang, Ocean Univ. of China (China)
Ling Jiang, Ocean Univ. of China (China)
Lei Han, 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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