王善昊,胡志群,王福增,陈杰鑫. 2024. 基于ConvLSTM融合RMAPS-NOW数据的雷达回波外推研究. 气象学报,82(X):1-14. DOI: 10.11676/qxxb2024.20230135
引用本文: 王善昊,胡志群,王福增,陈杰鑫. 2024. 基于ConvLSTM融合RMAPS-NOW数据的雷达回波外推研究. 气象学报,82(X):1-14. DOI: 10.11676/qxxb2024.20230135
Wang Shanhao, Hu Zhiqun, Wang Fuzeng, Chen Jiexin. 2024. A study on radar echo extrapolation based on ConvLSTM fusion of RMAPS-NOW data. Acta Meteorologica Sinica, 82(X):1-14. DOI: 10.11676/qxxb2024.20230135
Citation: Wang Shanhao, Hu Zhiqun, Wang Fuzeng, Chen Jiexin. 2024. A study on radar echo extrapolation based on ConvLSTM fusion of RMAPS-NOW data. Acta Meteorologica Sinica, 82(X):1-14. DOI: 10.11676/qxxb2024.20230135

基于ConvLSTM融合RMAPS-NOW数据的雷达回波外推研究

A study on radar echo extrapolation based on ConvLSTM fusion of RMAPS-NOW data

  • 摘要: 雷达回波外推是临近预报、人工影响天气作业及效果评估的主要参考依据之一,快速准确的回波外推技术,一直是雷达气象领域的研究热点。近年来,基于深度学习的时空序列预测模型在雷达回波外推中得到了广泛应用。然而,这些外推网络架构的输入大多用16级伪彩色雷达回波强度产品转化而来的灰度图,丢失了许多回波细节,并且随着外推时间延长,误差不可避免地增大。回波的生消、移动、演变与天气背景紧密相关,因此,将北京城市气象研究院研发的新一代快速更新多尺度资料分析和预报系统的临近数值预报子系统(RMAPS-NOW)初始零场的部分物理量产品融入华北雷达拼图原始数据,构建多个雷达单元(Radar cells),并将这些雷达单元作为输入,基于卷积长短期记忆网络(ConvLSTM),设计了一个多通道雷达回波外推网络架构(MR-ConvLSTM)。另外,考虑到卷积算法的平滑性,构建了自定义损失函数,增加回波强度的时空权重进行时空衰减订正。选取(40.65°—41.65°N,114°—115.4°E)内2018—2021年的6—9月共13000组华北雷达组合反射率因子拼图及RMAPS-NOW初始零场数据,其中的80%共10400组为训练集,20%共2600组为测试集。引入的物理量包括多个高度层的uv风(1350 m),相对湿度(RH,150 m),水平散度(1350 m)等,基于ConvLSTM,以及MR-ConvLSTM加自定义损失函数,分别训练得到5个雷达回波外推模型。采用临界成功指数(CSI)、命中率(POD)、虚警率(FAR)作为评价指标,利用测试集对所有模型进行评估。基于引入物理量的MR-ConvLSTM训练得到的模型在20、30、35 dBz反射率阈值下,比未引入物理量的基于ConvLSTM的外推模型CSI值平均高4.67%、13.8%、5.98%,POD值平均高3.1%、7.68%、8.38%,FAR值平均低6.37%、8.54%、10.17%,同时引入3种物理量(RH、uv)的外推模型在不同阈值的各项指标中综合表现最好,其CSI、POD值在3种不同阈值下比未引入物理量模型平均高16.01%、13.38%,FAR值平均低14.88%。从模型应用的个例可视化也可以看出,引入物理量后有效提升了雷达回波外推的准确度,证明基于MR-ConvLSTM架构训练的雷达回波外推模型有较强的泛化能力。

     

    Abstract: Radar echo extrapolation is one of the main reference criteria for nowcasting, weather modification operation and evaluation of its effectiveness. Therefore, rapid and accurate echo extrapolation technology is always a research hotspot in the field of radar meteorology. In recent years, deep learning-based spatiotemporal sequence prediction models have been widely applied in radar echo extrapolation. However, most of the inputs to these extrapolation networks are only grayscale images converted from the radar echo intensity products shown with 16-level color code pseudo colors, and thus some echo details are lost. The error inevitably increases with the extrapolation time. The initiation, disappearance, movement, and evolution of echoes are closely related to weather background. Based on this consideration, some physical products in the initial zero field of the rapid-refresh multi-scale analysis and prediction system-nowcasting (RMAPS-NOW) developed by the Institute of Urban Meteorology, CMA, are integrated with the raw data of the North China radar mosaic to construct multiple radar cells. Based on convolutional long short-term memory (ConvLSTM) network, a multi-channel radar echo extrapolation (MR-ConvLSTM) network is designed by using the radar cells as inputs. In addition, considering the smoothness of the convolutional algorithm, a self-defined loss function is designed to increase the spatiotemporal weight of the echo intensity for spatiotemporal attenuation correction. A total of 13000 samples of radar mosaic and RMAPS-NOW data during June—September from 2018 to 2021 over the area of (40.65°—41.65°N, 114°—115.4°E) are selected. 80% of the samples, i.e., 10400 samples, are used as the training data; and 20% of the samples, i.e. 2600, are used as the test data. The introduced physical products include u, v wind (1350 m), relative humidity (150 m), and horizontal divergence (1350 m), etc. at multiple altitude levels. Based on ConvLSTM and MR-ConvLSTM with the self-defined loss function, 5 extrapolation models are then trained respectively. Using critical success index (CSI), hit rate (POD), and false alarm rate (FAR) as evaluation indexes, the models are evaluated by the test data. At the values of reflectivity threshold 20, 30, and 35 dBz, the average values of CSI calculated by the MR-ConvLSTM-based and the self-defined function models with integrated physical products are 4.67%, 13.8%, 5.98% higher and the average values of POD are 3.1%, 7.68%, 8.38% higher, while average values of FAR are 6.37%, 8.54%, 10.17% lower than those by the ConvLSTM-based model without integrated physical products, respectively. The model with three physical products introduced (RH, u-wind, v-wind) performs the best for all the indexes, and average CSI and POD are respectively 16.01% and 13.38% higher while FAR is 14.88% lower than those without physical products. From the visualization cases of model application, it can also be seen that the introduction of physical quantities effectively improves the accuracy of radar echo extrapolation. These results indicate that the MR-ConvLSTM models and self-defined loss function have robust generalization ability.

     

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