Exploring Geometric Deep Learning For Precipitation Nowcasting
Shan Zhao Sudipan Saha Zhitong Xiong Niklas Boers Xiao Xiang Zhu
Precipitation nowcasting (up to a few hours) remains a challenge due to the
highly complex local interactions that need to be captured accurately.
Convolutional Neural Networks rely on convolutional kernels convolving with
grid data and the extracted features are trapped by limited receptive field,
typically expressed in excessively smooth output compared to ground truth. Thus
they lack the capacity to model complex spatial relationships among the grids.
Geometric deep learning aims to generalize neural network models to
non-Euclidean domains. Such models are more flexible in defining nodes and
edges and can effectively capture dynamic spatial relationship among
geographical grids. Motivated by this, we explore a geometric deep
learning-based temporal Graph Convolutional Network (GCN) for precipitation
nowcasting. The adjacency matrix that simulates the interactions among grid
cells is learned automatically by minimizing the L1 loss between prediction and
ground truth pixel value during the training procedure. Then, the spatial
relationship is refined by GCN layers while the temporal information is
extracted by 1D convolution with various kernel lengths. The neighboring
information is fed as auxiliary input layers to improve the final result. We
test the model on sequences of radar reflectivity maps over the Trento/Italy
area. The results show that GCNs improves the effectiveness of modeling the
local details of the cloud profile as well as the prediction accuracy by
achieving decreased error measures.