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 spatio-temporal prediction tasks.

降水临近预报的目标是预测局部区域在相对较短的时间内未来的降雨强度，是一项长期的科学挑战，具有巨大的社会和经济影响。用于降水预报的雷达回波外推方法以雷达回波图像作为输入，旨在通过学习历史图像来生成未来的雷达回波图像。为了有效处理雷达回波的复杂且高度非平稳演化，我们建议将运动分解为光流场运动和形态变形。遵循这个想法，我们引入了流动变形网络（FDNet），这是一种对两个并行交叉路径中的流动和变形进行建模的神经网络。流编码器捕获连续图像之间的光流场运动，变形编码器将形状的变化与雷达回波的平移运动区分开。我们在两个真实世界的雷达回波数据集上评估了所提出的网络架构。与最近的方法相比，我们的模型实现了最先进的预测结果。据我们所知，这是第一个具有流和变形分离的网络架构，用于模拟降水临近预报的雷达回波的演变。我们相信这项工作的总体思想不仅可以激发更有效的方法，而且可以应用于其他类似的时空预测任务。