Vol.35, NO.6, 2024
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Article Navigation >  Journal of Applied Meteorological Science  > 2024  >  35(6): 654-666
Zheng Yu, Xu Fen, Wang Yaqiang. Boundary layer convergence line identification algorithm for weather radar based on R2CNN. J Appl Meteor Sci, 2024, 35(6): 654-666. DOI:  10.11898/1001-7313.20240602.
Citation: Zheng Yu, Xu Fen, Wang Yaqiang. Boundary layer convergence line identification algorithm for weather radar based on R2CNN. J Appl Meteor Sci, 2024, 35(6): 654-666. DOI:  10.11898/1001-7313.20240602.
shu

Boundary Layer Convergence Line Identification Algorithm for Weather Radar Based on R2CNN

DOI: 10.11898/1001-7313.20240602
  • 1.

    Nanjing Joint Institute for Atmospheric Sciences, Nanjing 210041

  • 2.

    Key Laboratory of Transportation Meteorology, CMA, Nanjing 210041

  • 3.

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

  • Received Date: 2024-07-26
  • Rev Recd Date: 2024-09-25
  • Publish Date: 2024-11-30
    Abstract
  • Boundary layer convergence lines are recognized as one of the critical mesoscale weather systems triggered convection, and also affect low-altitude flight safety. The accurate and detailed identification of these lines is considered essential for revealing their formation, evolution, and interaction mechanisms with other weather systems. However, existing automatic identification technologies are limited in their ability to adapt the diverse characteristics of these lines, such as scale, intensity, and shape. The rotational region-based convolutional neural network (R2CNN) is employed to enhance the accuracy, robustness, and generalization of the identification process. A comprehensive identification dataset has been constructed for model training, considering the diversity of weather radar models and resolutions. Relevant parameters are adjusted to derive the optimized recognition model. The intersection over union (IoU) with confidence levels are employed to comprehensively assess and validate the identification results. Results indicate that the boundary layer convergence line recognition algorithm developed achieves a higher hit rate and a lower false alarm rate at lower IoU thresholds. At a confidence level of 0.7, the threat score (TS) reaches its maximum value.Compared to the existing Machine Intelligence Gust Front Algorithm (MIGFA), the model proposed in this study demonstrates significant advantages in reducing false alarms, improving hit rates, and achieving a balanced recognition frequency. Therefore, it is more suitable for operational applications and dissemination. This research not only provides a more effective method for identifying boundary layer convergence lines but also contributes to the improvement of low-altitude flight safety and advances meteorological detection technologies. The proposed method addresses limitations of existing technologies by effectively managing the diverse characteristics of boundary layer convergence lines. By incorporating rotational bounding boxes in the detection process, R2CNN model enhances the detection accuracy for objects with arbitrary orientations, which is particularly beneficial for meteorological phenomena that do not align with the standard axis. The constructed dataset includes a diverse collection of radar images from various models and resolutions, ensuring that the model is trained on a wide range of data and can generalize effectively to new, unseen data. Extensive experiments are conducted to evaluate the model's performance under different IoU thresholds and confidence levels. Findings demonstrate that at lower IoU thresholds, the model maintains high detection performance, indicating its robustness in practical applications where precise localization may be challenging. Furthermore, the superior performance of the proposed model compared to MIGFA indicates its potential for widespread adoption by meteorological agencies for better monitoring and forecasting.
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  • Fig. 1  Performance of boundary layer convergence line identification based on testing dataset

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    Fig. 2  Observation and identified gust front characteristics by S-band weather radar of Yichang at 1034 UTC 4 Aug 2017

    (the black box denotes gust front region identified by algorithm)

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    Fig. 3  Observation and identified boundary layer convergence line by X-band weather radar of Luhe, Nanjing at 0740 UTC 8 Jul 2022

    (the black box denotes boundary layer convergence line region identified by algorithm )

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    Fig. 4  Comparative evaluation of gust front identification performance between R2CNN and MIGFA methods

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    Fig. 5  Comparative evaluation of gust front identification by weather radar of Suqian at 1049 UTC 30 Apr 2021

    (the black box denotes R2CNN algorithm, and the orange box denotes MIGFA algorithm)

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    Fig. 6  Comparative evaluation of gust front identification by weather radar of Nanjing at 0727 UTC 28 Jul 2022

    (the black box denotes R2CNN algorithm, and the orange box denotes MIGFA algorithm)

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    • Received : 2024-07-26
    • Accepted : 2024-09-25
    • Published : 2024-11-30
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