FDNet: A Deep Learning Approach with Two Parallel Cross Encoding Pathways for Precipitation Nowcasting

• URL: http://arxiv.org/abs/2105.02585v1
• Date: Thu, 6 May 2021 11:18:24 GMT
• Title: FDNet: A Deep Learning Approach with Two Parallel Cross Encoding Pathways for Precipitation Nowcasting
• Authors: Bi-Ying Yan and Chao Yang and Feng Chen and Kohei Takeda and Changjun Wang
• Abstract summary: We introduce Flow-Deformation Network (FDNet), a neural network that models flow and deformation in two parallel cross pathways. We evaluate the proposed network architecture on two real-world radar echo datasets.
• Score: 7.0521806281607615
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: With the goal of predicting the future rainfall intensity in a local region over a relatively short period time, precipitation nowcasting has been a long-time scientific challenge with great social and economic impact. The radar echo extrapolation approaches for precipitation nowcasting take radar echo images as input, aiming to generate future radar echo images by learning from the historical images. To effectively handle complex and high non-stationary evolution of radar echoes, we propose to decompose the movement into optical flow field motion and morphologic deformation. Following this idea, we introduce Flow-Deformation Network (FDNet), a neural network that models flow and deformation in two parallel cross pathways. The flow encoder captures the optical flow field motion between consecutive images and the deformation encoder distinguishes the change of shape from the translational motion of radar echoes. We evaluate the proposed network architecture on two real-world radar echo datasets. Our model achieves state-of-the-art prediction results compared with recent approaches. To the best of our knowledge, this is the first network architecture with flow and deformation separation to model the evolution of radar echoes for precipitation nowcasting. We believe that the general idea of this work could not only inspire much more effective approaches but also be applied to other similar spatiotemporal prediction tasks

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