A spatiotemporal convolutional network approach for convection storm nowcasting

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.

[1]  Vladlen Koltun,et al.  An Empirical Evaluation of Generic Convolutional and Recurrent Networks for Sequence Modeling , 2018, ArXiv.

[2]  Dit-Yan Yeung,et al.  Convolutional LSTM Network: A Machine Learning Approach for Precipitation Nowcasting , 2015, NIPS.

[3]  Yann Dauphin,et al.  Convolutional Sequence to Sequence Learning , 2017, ICML.

[4]  Nitish Srivastava,et al.  Unsupervised Learning of Video Representations using LSTMs , 2015, ICML.

[5]  J. Schaefer.  The critical success index as an indicator of Warning skill , 1990 .

[6]  G. Foote,et al.  Determination of the Boundary Layer Airflow from a Single Doppler Radar , 1990 .

[7]  Guigang Zhang,et al.  Deep Learning , 2016, Int. J. Semantic Comput..

[8]  R. Rinehart,et al.  Three-dimensional storm motion detection by conventional weather radar , 1978, Nature.

[9]  Jürgen Schmidhuber,et al.  Long Short-Term Memory , 1997, Neural Computation.

[10]  M. Dixon,et al.  TITAN: Thunderstorm Identification, Tracking, Analysis, and Nowcasting—A Radar-based Methodology , 1993 .

[11]  Juanzhen Sun,et al.  Use of NWP for Nowcasting Convective Precipitation: Recent Progress and Challenges , 2014 .

[12]  Xiaogang Wang,et al.  Spatial As Deep: Spatial CNN for Traffic Scene Understanding , 2017, AAAI.

[13]  Dit-Yan Yeung,et al.  Deep Learning for Precipitation Nowcasting: A Benchmark and A New Model , 2017, NIPS.

[14]  Philip S. Yu,et al.  PredRNN: Recurrent Neural Networks for Predictive Learning using Spatiotemporal LSTMs , 2017, NIPS.

[15]  Lior Wolf,et al.  A Dynamic Convolutional Layer for short rangeweather prediction , 2015, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[16]  P. Cochat,et al.  Et al , 2008, Archives de pediatrie : organe officiel de la Societe francaise de pediatrie.

[17]  Lei Han,et al.  A Machine Learning Nowcasting Method based on Real-time Reanalysis Data , 2016, ArXiv.

[18]  Yoshua Bengio,et al.  On the Properties of Neural Machine Translation: Encoder–Decoder Approaches , 2014, SSST@EMNLP.

[19]  Kevin W. Manning,et al.  Experiences with 0–36-h Explicit Convective Forecasts with the WRF-ARW Model , 2008 .

[20]  Lei Han,et al.  3D Convective Storm Identification, Tracking, and Forecasting—An Enhanced TITAN Algorithm , 2009 .

[21]  Jan Handwerker,et al.  Cell tracking with TRACE3D—a new algorithm , 2002 .

[22]  Shih-Fu Chang,et al.  Temporal Action Localization in Untrimmed Videos via Multi-stage CNNs , 2016, 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).

[23]  Geoffrey E. Hinton,et al.  Rectified Linear Units Improve Restricted Boltzmann Machines , 2010, ICML.

[24]  Jianmin Wang,et al.  PredCNN: Predictive Learning with Cascade Convolutions , 2018, IJCAI.

[25]  Quoc V. Le,et al.  Sequence to Sequence Learning with Neural Networks , 2014, NIPS.

[26]  Yoshua Bengio,et al.  Learning long-term dependencies with gradient descent is difficult , 1994, IEEE Trans. Neural Networks.

[27]  Razvan Pascanu,et al.  On the difficulty of training recurrent neural networks , 2012, ICML.