A spatiotemporal convolutional network approach for convection storm nowcasting

Wei Zhang; Hongling Liu; Lei Han

doi:10.1117/12.2557332

3 January 2020 A spatiotemporal convolutional network approach for convection storm nowcasting

Wei Zhang, Hongling Liu, Lei Han

Proceedings Volume 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019); 113731P (2020) https://doi.org/10.1117/12.2557332
Event: Eleventh International Conference on Graphics and Image Processing, 2019, Hangzhou, China

Abstract

The purpose of convective storms nowcasting is to predict whether a region will experience strong convective weather in the next 30 minutes. Unlike traditional prediction, we study the problem of convective storm nowcasting from the perspective of machine learning. First, this paper divides the research area into a number of position-fixed small cells, then transforms the nowcasting problem into a question: Is there a radar echo > 35 dBZ in a cell within 30 minutes? Secondly, the problem of nowcasting is formulated as a kind of spatiotemporal classification learning. From this point of view, this paper introduces a sliding oversampling method to mitigate the class imbalance issue of convective storm nowcasting. A spatiotemporal convolutional network nowcasting method is proposed which is in less computational cost and easier to train than recurrent neural network. In experiments, this spatiotemporal convolution network can better captures temporal and spatial correlations, compared with previous studies, therefore results in better predictive performance. Although no sophisticated tracking algorithms are used, storm movement trends and storm growth can be predicted with reasonable skill.

Citation Download Citation

Wei Zhang, Hongling Liu, and Lei Han "A spatiotemporal convolutional network approach for convection storm nowcasting", Proc. SPIE 11373, Eleventh International Conference on Graphics and Image Processing (ICGIP 2019), 113731P (3 January 2020); https://doi.org/10.1117/12.2557332

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