Radar Reflectivity and Meteorological Factors Merging‐Based Precipitation Estimation Neural Network
Radar Reflectivity and Meteorological Factors Merging‐Based Precipitation Estimation Neural Network
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DOI：
10.1029/2021ea001811
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发表时间：
2021-09
影响因子：
3.1
通讯作者：
Yonghong Zhang;Shiwei Chen;Wei Tian;Shuai Chen
中科院分区：
文献类型：
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作者：
Yonghong Zhang;Shiwei Chen;Wei Tian;Shuai Chen
气象因子是影响地面降水的重要因素。然而，在基于多普勒雷达资料的定量降水估计（QPE）研究中，通常采用气象要素作为权重因子进行降水校正，忽略了气象要素在确定降水中的积极作用，限制了雷达QPE精度的提高。本研究探讨了一维卷积神经网络结合雷达数据和气象因子数据估计降水的有效性。气象因素的各种组合进行了测试的输入变量集。在0.01°的空间分辨率和6分钟的时间尺度上，对石家庄地区的拟议模式性能进行了评估。结果表明，与普通克里格插值、两个Z-R关系和反向传播神经网络相比，所提出的模型（RM-1DCNN）提供了更准确的降水估计。RM-1DCNN的均方根误差为0.642 mm/6 min，平均威胁分数超过55%，是所有方案中最好的。
The meteorological factors are important determinants of the surface rainfall. However, in studies of quantitative precipitation estimation (QPE) based on Doppler radar data, meteorological elements are usually used as the weighting factors to correct precipitation, and the active role of meteorological factors in determining rainfall is neglected, which limits the improvement of radar QPE accuracy. In this study, the effectiveness of applying one‐dimensional convolutional neural network together with radar data and meteorological factor data to estimate precipitation is explored. Various combinations of meteorological factors were tested for the set of input variables. The proposed model performance was evaluated over the Shijiazhuang area at the spatial resolution of 0.01° and at the 6‐min time scale. The results indicates that the proposed model (RM‐1DCNN) provides more accurate precipitation estimation compared to the Ordinary Kriging interpolation, two Z‐R relationships, and Back Propagation Neural Network. The root mean square error of the RM‐1DCNN with temperature was 0.642 mm per 6 min and the average Threat Score exceed 55%, which was the best among all schemes.