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首页> 外文期刊>IEEE Geoscience and Remote Sensing Letters >A Generative Adversarial Gated Recurrent Unit Model for Precipitation Nowcasting
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A Generative Adversarial Gated Recurrent Unit Model for Precipitation Nowcasting

机译：一种降水垂封的生成的对抗性络合单元模型

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摘要

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.
机译：降水垂圈是运营天气预报中的重要任务。任务的主要挑战是雷达回波地图外推。问题主要通过现有系统中的光学流量方法解决。但是，该方法不能模拟快速和非线性运动。最近，开发了一种卷积门控复发单元（CONCRU）方法，旨在基于深度学习技术模拟这种运动。尽管表现有前途，但CONCRU倾向于产生模糊的外推图像，并且不能对多模态和偏斜强度分布。为了克服这个限制，我们提出了这封信是一种生成的对抗性concregru（Ga-concregru）模型。该模型由两个对抗性学习系统组成，这些系统是基于Concregru的发电机和基于卷积神经网络的鉴别器。这两个系统通过播放最小游戏来培训。随着对抗性学习计划，GA-CONCRGU可以产生更现实和更准确的外推。已经进行了实际数据集的实验，结果表明，所提出的GA-CONCRGU显着优于最先进的外推方法CONCRGU和光学流。

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