Radar echo extrapolation, as a widely used approach for precipitation nowcasting, plays a pivotal role in severe convective weather warnings. Previous studies have encountered the dilemma of extrapolation ambiguity, which leads to low availability of extrapolation results. To get over the hurdle of extrapolation ambiguity, we propose a vivid radar network for precipitation nowcasting called VRNet. We first implement a multiscale spatial feature fusion module to extract richer spatial feature information, which contributes to producing clear images when extrapolating result reconstructions. Furthermore, we construct a generative adversarial network (GAN) with a ConvLSTM unit to enhance the model’s spatiotemporal information representation capability. In addition, a weighted loss function based on radar echo intensity is designed to address the distribution characteristics of radar echo intensity, improving the global averaging strategy of the mean squared error loss function. Experimental results demonstrate that the proposed model outperforms the benchmark models in the radar echo extrapolation task, which obtains a higher accuracy rate and improves the clarity of the extrapolated image.

中文翻译：

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VRNet：用于降水临近预报的生动雷达网络

雷达回波外推作为一种广泛使用的临近降水预报方法，在强对流天气预警中发挥着关键作用。以往的研究都遇到过外推模糊的困境，导致外推结果的可用性较低。为了克服外推模糊性的障碍，我们提出了一种用于降水临近预报的生动雷达网络，称为 VRNet。我们首先实现多尺度空间特征融合模块来提取更丰富的空间特征信息，这有助于在外推结果重建时产生清晰的图像。此外，我们构建了带有 ConvLSTM 单元的生成对抗网络（GAN），以增强模型的时空信息表示能力。此外，针对雷达回波强度的分布特点，设计了基于雷达回波强度的加权损失函数，改进了均方误差损失函数的全局平均策略。实验结果表明，该模型在雷达回波外推任务中优于基准模型，获得了更高的准确率并提高了外推图像的清晰度。