Vol.33, NO.5, 2022
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Article Navigation >  Journal of Applied Meteorological Science  > 2022  >  33(5): 581-593
Yin Xiaoyan, Hu Zhiqun, Zheng Jiafeng, et al. Filling in the dual polarization radar echo occlusion based on deep learning. J Appl Meteor Sci, 2022, 33(5): 581-593. DOI:  10.11898/1001-7313.20220506.
Citation: Yin Xiaoyan, Hu Zhiqun, Zheng Jiafeng, et al. Filling in the dual polarization radar echo occlusion based on deep learning. J Appl Meteor Sci, 2022, 33(5): 581-593. DOI:  10.11898/1001-7313.20220506.
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Filling in the Dual Polarization Radar Echo Occlusion Based on Deep Learning

DOI: 10.11898/1001-7313.20220506
  • 1.

    State Key Laboratory of Severe Weather, Chinese Academy of Meteorological Sciences, Beijing 100081

  • 2.

    School of Atmospheric Sciences, Chengdu University of Information Technology, Chengdu 610225

  • 3.

    Key Laboratory of Atmosphere Sounding, China Meteorological Administration, Chengdu 610225

  • Received Date: 2022-03-06
  • Rev Recd Date: 2022-06-15
  • Publish Date: 2022-09-15
    Abstract
  • Radar beam blockage is an important error source that affects the quality of weather radar data. The S-band dual-polarization radar in Guangzhou has multi-azimuth occlusion at low elevation and is partially occluded at high elevation. Based on deep learning methods such as convolutional neural network, two echo filling networks, i.e., VEF(vertical echo-filling) and HEF(horizontal echo-filling) are constructed. Based on this architecture, echoes from the unblocked area are used to construct training datasets and fill the reflectivity ZH and differential reflectivity ZDR in the occlusion area. For the area with only 0.5° elevation occlusion, multi-modal modeling is carried out based on VEF architecture by using 3D data from multiple upper elevations, radial directions and gates. Considering that the radar beam broadens with distance and to avoid the influence of the melting layer, the radar beam is divided into four sections according to the oblique distance of 0.5° elevation, and the vertical echo-filling model is trained respectively. For the area with high occlusion elevation, multi-mode modeling is carried out based on HEF architecture using the data of multiple adjacent radial directions and gates with the same elevation. According to the number of occlusion radial, two types of horizontal echo-filling models, three radials echo-filling model and five radials echo-filling model are constructed respectively. Finally, the models are evaluated by three cases and three indicators:Explained variance, mean absolute error and correlation coefficient. The maximum explained variance of ZH vertical echo-filling model is 0.91, the minimum mean absolute error is 1.72 dB, and the maximum correlation coefficient is 0.96. The maximum explained variance of ZDR vertical echo-filling model is 0.87, the minimum mean absolute error is 0.12 dB, and the maximum correlation coefficient is 0.92. The maximum explained variance of ZH horizontal fill model is 0.92, the minimum mean absolute error is 1.69 dB, and the maximum correlation coefficient is 0.96. The maximum explained variance of ZDR horizontal echo-filling model is 0.92, the minimum mean absolute error is 0.12 dB, and the maximum correlation coefficient is 0.96. The deep learning echo-filling model can be used to correct the echoes of Guangzhou S-band dual-polarization radar occlusion area effectively, and the quality of weather radar data is improved.
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    References(33)

  • Fig. 1  Occlusion coefficient of 0.5° elevation(a) and 1.5° elevation(b) of Guangzhou CINRAD/SAD calculated from geographic elevation data

    (the distance between adjacent rings is 30 km)

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    Fig. 2  VEF(a) and HEF(b) network architecture

    DownLoad: Full-Size Img PowerPoint

    Fig. 3  Plots of predicted and observed echo reflectivity of each section with ZH and ZDR vertical echo-filling models

    (color mark denotes Gaussian kernel density estimation)

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    Fig. 4  Plots of predicted and observed echo reflectivity of different fill radial numbers with ZH and ZDR horizontal echo-filling models

    (color mark denotes Gaussian kernel density estimation)

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    Fig. 5  PPI images of echo-filling result comparison of 0.5° elevation at 1136 UTC 25 Aug 2019

    (red ellipse denotes the obvious blocked area, the same as in after)
    (a)observed ZH, (b)predicted ZH, (c)observed ZDR, (d)predicted ZDR

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    Fig. 6  PPI images of echo-filling result comparison of 0.5° elevation at 0854 UTC 6 Jun 2020

    (a)observed ZH, (b)predicted ZH, (c)observed ZDR, (d)predicted ZDR

    DownLoad: Full-Size Img PowerPoint

    Fig. 7  PPI images of echo-filling result comparison of 0.5° elevation at 0354 UTC 8 May 2019

    (a)observed ZH, (b)predicted ZH, (c)observed ZDR, (d)predicted ZDR

    DownLoad: Full-Size Img PowerPoint

    Table  1  Evaluation of each section in ZH and ZDR vertical echo-filling models

    填补量 距离段/km 解释方差 平均绝对偏差/dB 相关系数
    ZH [20, 54) 0.9116 1.7947 0.9572
    [54, 68) 0.9155 1.7251 0.9634
    [68, 88) 0.8956 1.7210 0.9526
    [88, 120] 0.8206 2.2630 0.9208
    ZDR [20, 54) 0.8762 0.1520 0.9240
    [54, 68) 0.8564 0.1204 0.8971
    [68, 88) 0.8538 0.1228 0.9083
    [88, 120] 0.8308 0.1440 0.8683
    DownLoad: Download CSV

    Table  2  Evaluation of different fill radial numbers in ZH and ZDR horizontal echo-filling models

    填补量 遮挡径向数 解释方差 平均绝对偏差/dB 相关系数
    ZH 3 0.9228 1.6973 0.9615
    5 0.8342 2.1143 0.9227
    ZDR 3 0.9254 0.1189 0.9639
    5 0.8542 0.1275 0.9333
    DownLoad: Download CSV

    Table  3  Evaluation of echo-filling of each section in the unblocked area at 1136 UTC 25 Aug 2019

    填补量 距离段/km 解释方差 平均绝对偏差/dB 相关系数
    ZH [20, 54) 0.9031 1.0245 0.9567
    [54, 68) 0.9094 1.0267 0.9632
    [68, 88) 0.8250 2.0311 0.9102
    [88, 120] 0.7851 2.1401 0.8816
    ZDR [20, 54) 0.9015 0.0315 0.9211
    [54, 68) 0.9242 0.0309 0.9012
    [68, 88) 0.8240 0.0353 0.8743
    [88, 120] 0.7789 0.0328 0.8674
    DownLoad: Download CSV

    Table  4  Evaluation of echo-filling of each section in the unblocked area at 0854 UTC 6 Jun 2020

    填补量 距离段/km 解释方差 平均绝对偏差/dB 相关系数
    ZH [20, 54) 0.9311 1.4654 0.9457
    [54, 68) 0.9594 2.0431 0.9341
    [68, 88) 0.8950 2.1143 0.9019
    [88, 120] 0.8854 2.3496 0.8942
    ZDR [20, 54) 0.9592 0.0312 0.9732
    [54, 68) 0.9321 0.0207 0.9643
    [68, 88) 0.9034 0.0236 0.9325
    [88, 120] 0.8811 0.0413 0.9078
    DownLoad: Download CSV

    Table  5  Evaluation of echo-filling of each section in the unblocked area at 0354 UTC 8 May 2019

    填补量 距离段/km 解释方差 平均绝对偏差/dB 相关系数
    ZH [20, 54) 0.9330 1.0309 0.9681
    [54, 68) 0.9121 1.0331 0.9616
    [68, 88) 0.9345 2.0154 0.9473
    [88, 120] 0.8708 2.3480 0.9126
    ZDR [20, 54) 0.9865 0.0333 0.9618
    [54, 68) 0.9618 0.0243 0.9679
    [68, 88) 0.9097 0.0251 0.9402
    [88, 120] 0.8602 0.0381 0.9231
    DownLoad: Download CSV
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    • Received : 2022-03-06
    • Accepted : 2022-06-15
    • Published : 2022-09-15
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