Zhao Ziliang, Wang Zhangu, Zhao Guoyu, Zhao Jun
College of Transportation, Shandong University of Science and Technology, Shandong, 266590, Qingdao, China.
School of Future Technology, China University of Geosciences, Wuhan, 430074, China.
Sci Rep. 2024 Aug 16;14(1):19024. doi: 10.1038/s41598-024-68951-1.
Radar observation variables reflect the precipitation amount of strong convective precipitation processes, which accurate forecast is an important difficulty in weather forecasting. Current forecasting methods are mostly based on radar echo extrapolation, which has the insufficiency of input information and the ineffectiveness of model architecture. This paper presents a Bidirectional Long Short-Term Memory forecasting method for strong convective precipitation based on the attention mechanism and residual neural network (ResNet-Attention-BiLSTM). First, this paper uses ResNet to effectively extract the key information of extreme weather and solves the problem of regression to the mean of the prediction model by learning the residuals of the radar observation data. Second, this paper uses the attention mechanism to adaptively weight the fusion of the features to enhance the extraction of the important features of the precipitation image data. On this basis, this paper presents a novel spatio-temporal reasoning method for radar observations and establishes a precipitation forecasting model, which captures the past and future time-order relationship of the sequence data. Finally, this paper conducts experiments based on the real collected data of a strong convective precipitation process and compares its performance with the existing models, the mean absolute percentage error of this model was reduced by 15.94% (1 km), 18.72% (3 km), and 14.91% (7 km), and the coefficient of determination ( ) was increased by 10.89% (1 km), 9.61% (3 km), and 9.29% (7 km), which proves the state of the art and effectiveness of this forecasting model.
雷达观测变量反映了强对流降水过程的降水量，其准确预报是天气预报中的一个重要难点。当前的预报方法大多基于雷达回波外推，存在输入信息不足和模型架构有效性不足的问题。本文提出了一种基于注意力机制和残差神经网络的强对流降水双向长短期记忆预报方法（ResNet-Attention-BiLSTM）。首先，本文利用ResNet有效提取极端天气的关键信息，并通过学习雷达观测数据的残差解决了预测模型均值回归的问题。其次，本文利用注意力机制对特征融合进行自适应加权，增强对降水图像数据重要特征的提取。在此基础上，本文提出了一种新颖的雷达观测时空推理方法，建立了降水预报模型，该模型捕捉了序列数据的过去和未来时间顺序关系。最后，本文基于强对流降水过程的实际采集数据进行实验，并将其性能与现有模型进行比较，该模型的平均绝对百分比误差在1公里处降低了15.94%，在3公里处降低了18.72%，在7公里处降低了14.91%，决定系数（ ）在1公里处提高了10.89%，在3公里处提高了9.61%，在7公里处提高了9.29%，证明了该预报模型的先进性和有效性。
