Precipitation nowcasting is an important task in operational weather forecasts. The key challenge of the task is the radar echo map extrapolation. The problem is mainly solved by an optical-flow method in existing systems. However, the method cannot model rapid and nonlinear movements. Recently, a convolutional gated recurrent unit (ConvGRU) method is developed, which aims to model such movements based on deep learning techniques. Despite the promising performance, ConvGRU tends to yield blurring extrapolation images and fails to multi-modal and skewed intensity distribution. To overcome the limitations, we propose in this letter a generative adversarial ConvGRU (GA-ConvGRU) model. The model is composed of two adversarial learning systems, which are a ConvGRU-based generator and a convolution neural network-based discriminator. The two systems are trained by playing a minimax game. With the adversarial learning scheme, GA-ConvGRU can yield more realistic and more accurate extrapolation. Experiments on real data sets have been conducted and the results demonstrate that the proposed GA-ConvGRU significantly outperforms state-of-the-art extrapolation methods ConvGRU and optical flow.

中文翻译：

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用于降水临近预报的生成对抗性门控循环单元模型

降水临近预报是业务天气预报中的一项重要任务。该任务的主要挑战是雷达回波图外推。该问题主要通过现有系统中的光流方法来解决。然而，该方法不能对快速和非线性运动进行建模。最近，开发了一种卷积门控循环单元 (ConvGRU) 方法，旨在基于深度学习技术对此类运动进行建模。尽管性能良好，但 ConvGRU 往往会产生模糊的外推图像，并且无法实现多模态和倾斜的强度分布。为了克服这些限制，我们在这封信中提出了一种生成对抗 ConvGRU (GA-ConvGRU) 模型。该模型由两个对抗性学习系统组成，分别是基于 ConvGRU 的生成器和基于卷积神经网络的判别器。这两个系统是通过玩极小极大游戏来训练的。使用对抗性学习方案，GA-ConvGRU 可以产生更真实、更准确的外推。已经在真实数据集上进行了实验，结果表明，所提出的 GA-ConvGRU 显着优于最先进的外推方法 ConvGRU 和光流。