SpatioTemporal Inference Network for Precipitation Nowcasting With Multimodal Fusion

Qizhao Jin, Xinbang Zhang, Xinyu Xiao, Ying Wang, Gaofeng Meng, Shiming Xiang, Chunhong Pan

IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · 13 October 2023

In one sentence. STIN uses spatiotemporally-aware convolutions to fuse temperature, humidity and wind with precipitation for nowcasting, improving on shared-kernel baselines.

Abstract

Precipitation plays a significant role in global water and energy cycles, largely affecting many aspects of human life, such as transportation and agriculture. Recently, meteorologists have tried to predict precipitation with deep learning methods by learning from much historical meteorological data. Under this paradigm, the task of precipitation nowcasting is formulated as a spatiotemporal sequence forecasting problem. However, current studies suffer from two inherent drawbacks of the definition of the problem. First, considering that the weather patterns vary in spatial and temporal dimensions, a spatiotemporally shared kernel is not optimal for capturing features across different regions and seasons. Second, these methods isolate the precipitation from other meteorological elements, such as temperature, humidity, and wind. The disability of cross-model learning prevents the possibility of the promotion of precipitation prediction. Therefore, this paper proposes a SpatioTemporal Inference Network (STIN) to produce precipitation prediction from multi-modal meteorological data with spatiotemporal specific filters. Specifically, we first design a Spatiotemporal-Aware Convolutional layer (STAConv), in which kernels are generated conditioned on the incoming spatiotemporally features vector. Replacing normal convolution with STAConv enables the extraction of spatiotemporal specific information from the meteorological data. Based on the STAConv, the Spatiotemporal-Aware Convolutional Neural Network (STACNN) is further proposed, fusing the multi-modal information, including temperature, humidity, and wind. Then, an encoder-decoder framework composed of RNN layers is built to extract representative temporal dynamics from multi-modal information. To investigate the practicality of the proposed method, we employ STIN to predict the following precipitation intensity. Extensive experiments on three meteorological datasets demonstrate the effectiveness of our model on precipitation nowcasting.

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