Radar extrapolation has been one of the most important means for nowcasting. Most current models achieve good performance in high-frequency sequences (e.g., video, more than 24 fps), while the temporal resolution of radar echo sequences is much lower (1 frame every 6 min) and the transforms are much more complex. The spatiotemporal characters with some similarities would not change a lot in video sequences; however, the radar echo sequences include more intangible changes (e.g., the echo evolution of generation or vanish, and so on), which leads to unique distinct spatial and temporal characters, respectively. Therefore, the singular peculiarity would be mitigated, leading to a rapid decline in precision and sharpness during the extrapolation process. In general, temporal feature extraction is utilized to understand the variation in pixel locations, while spatial feature extraction is employed to capture the distribution variation of specific regions. In this work, we propose a feature decomposition network, termed as RadarNet to improve the extrapolation precision. The parallel independent encoders are used to enhance multi-scale spatial feature extraction and temporal motion feature capture of radar echoes, respectively. In addition, we design a specialized cross fusion mechanism to achieve the inputs of the decoder which may enhance the performance of the extrapolation. The extrapolation experiments are conducted on real radar echo datasets from Shijiazhuang and Nanjing that demonstrate the effectiveness of our model.

中文翻译：

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RadarNet：用于雷达外推的并行时空编码器网络


雷达外推一直是临近预报的最重要手段之一。当前大多数模型在高频序列（例如视频，超过 24 fps）中实现了良好的性能，而雷达回波序列的时间分辨率要低得多（每 6 分钟 1 帧）并且变换要复杂得多。具有一定相似性的时空特征在视频序列中不会发生太大变化；然而，雷达回波序列包含更多无形的变化（例如，回波的产生或消失的演变等），这分别导致了独特的独特的空间和时间特征。因此，奇异性会被减弱，导致外推过程中精度和锐度迅速下降。一般来说，时间特征提取用于了解像素位置的变化，而空间特征提取用于捕获特定区域的分布变化。在这项工作中，我们提出了一种特征分解网络（称为 RadarNet）来提高外推精度。并行独立编码器分别用于增强雷达回波的多尺度空间特征提取和时间运动特征捕获。此外，我们设计了一种专门的交叉融合机制来实现解码器的输入，这可以增强外推的性能。外推实验在石家庄和南京的真实雷达回波数据集上进行，证明了我们模型的有效性。