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首页> 外文会议>International conference on graphic and image processing >Strong Convective Storm Nowcasting Using a Hybrid Approach of Convolutional Neural Network and Hidden Markov Model
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Strong Convective Storm Nowcasting Using a Hybrid Approach of Convolutional Neural Network and Hidden Markov Model

机译：卷积神经网络与隐马尔可夫模型混合的强对流风暴临近预报

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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.
机译：对流风暴临近预报是指在很短的时间内（通常为0〜2 h）预测对流天气的开始，发展和衰减。尽管过去几年取得了显着进展，但严重的对流风暴临近预报仍然是一个挑战。随着机器学习的繁荣，它已经在各个领域得到了很好的应用，尤其是卷积神经网络（CNN）。本文利用再分析气象数据，建立了基于CNN和隐马尔可夫模型（HMM）的服务器对流天气临近预报系统。对流风暴临近预报的目的是预测30分钟内是否有对流风暴。本文将CNN作为HMM的观测向量，将VDRAS再分析数据压缩为低维数据，然后以时间序列的形式获得强对流天气的发展趋势。结果表明，我们的方法可以提取鲁棒的特征，而无需人为地选择特征，并且可以捕捉到强对流风暴的发展趋势。

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