面向降雨预报的雷达回波预测序列外推方法

雷达回波外推方法广泛应用于降雨预报中。针对雷达回波中的预测精度不够高的问题，提出了一种基于循环神经网络的深度学习模型DIPredRNN。该模型通过引入空间和通道的双注意力机制，将长时间的时间信息和通道信息结合起来，提高了时间记忆的长期依赖；通过引入隐藏状态和输入的交互框架，保留了更多的特征，提高了时间记忆的短期依赖。该模型在HKO-7数据集和四川数据集上同经典模型以及诸多先进模型进行实验对比，该模型从外推图像、MSE、SSIM、CSI-30～50 dbz多个指标对比中都取得最佳效果。实验证明了DIPredRNN提高了雷达回波预测效果，拥有先进的性能。

Research on extrapolation of radar echo prediction sequence for rainfall prediction

The radar echo extrapolation method is widely used in rainfall forecasting. Addressing the issue of insufficient prediction accuracy in radar echoes, this paper proposed a deep learning model DIPredRNN based on recurrent neural networks. This model combined long-term temporal and channel information by introducing a dual attention mechanism of space and channel, improed the long-term dependence of time memory. By introducing an interactive framework of hidden states and inputs, it retained more features and improved the short-term dependence of temporal memory. This model was experimentally compared with classical models and many advanced models on the HKO-7 and Sichuan datasets. The model achieved the best results in comparing multiple indicators such as extrapolated images, MSE, SSIM, CSI-30~50 dbz. The experiment proves that the proposed DIPredRNN network improves the radar echo prediction performance and has advanced performance.