1 research outputs found
A Three-dimensional Convolutional-Recurrent Network for Convective Storm Nowcasting
Very short-term convective storm forecasting, termed nowcasting, has long
been an important issue and has attracted substantial interest. Existing
nowcasting methods rely principally on radar images and are limited in terms of
nowcasting storm initiation and growth. Real-time re-analysis of meteorological
data supplied by numerical models provides valuable information about
three-dimensional (3D), atmospheric, boundary layer thermal dynamics, such as
temperature and wind. To mine such data, we here develop a
convolution-recurrent, hybrid deep-learning method with the following
characteristics: (1) the use of cell-based oversampling to increase the number
of training samples; this mitigates the class imbalance issue; (2) the use of
both raw 3D radar data and 3D meteorological data re-analyzed via multi-source
3D convolution without any need for handcraft feature engineering; and (3) the
stacking of convolutional neural networks on a long short-term memory
encoder/decoder that learns the spatiotemporal patterns of convective
processes. Experimental results demonstrated that our method performs better
than other extrapolation methods. Qualitative analysis yielded encouraging
nowcasting results.Comment: 13 pages, 11 figures, accepted by 2019 IEEE International Conference
on Big Knowledge The copyright of this paper has been transferred to the
IEEE, please comply with the copyright of the IEE