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Recognition algorithm for deep convective clouds based on FY4A

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Abstract

The short-term heavy rainfalls, thunderstorm gales, hail, squall lines, tornadoes, thunderstorms and other disastrous weather caused by deep convective clouds greatly threaten social and economic activities and the safety of people’s lives and property. Therefore, it is of great value to study the recognition methods of deep convective clouds in the field of the weather forecast. Since deep convective clouds are characterized by a short life cycle, small spatial scale and complex structure, it is difficult to accurately monitor and identify deep convective clouds by traditional ground monitoring methods. In this paper, the semantic segmentation network SCNET based on attention mechanism was proposed and a deep learning network for the recognition of deep convective clouds was established with infrared and brightness temperature channels of FY4A stationary meteorological satellites as input features. The results showed that SCNET has a better recognition effect than meteorological and machine learning methods, such as single-band threshold method, SVM, NN, UNET and RESNET, and can effectively improve the recognition accuracy of deep convective clouds

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Funding

Partial financial support was received from National Natural Science Foundation of China Under Grant Nos. 61772280 and 62072249

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Authors and Affiliations

  1. School of Artificial Intelligence/School of Future Technology, Nanjing University of Information Science and Technology, Pancheng, Nanjing, 210044, Jiangsu, China

    Tao Li, Di Wu & Lina Wang

  2. School of Business, Nanjing University, Gulou, Nanjing, 210008, Jiangsu, China

    Xiaofeng Yu

Authors
  1. Tao Li
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  2. Di Wu
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  3. Lina Wang
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  4. Xiaofeng Yu
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Correspondence to Xiaofeng Yu.

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Appendix A: Case results identified by other models

Appendix A: Case results identified by other models

See Figs. 15, 16, 17, 18, 19, 20, 21, 22, 23, 24, 25, 26, 27 and 28.

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Recognition results of deep convective clouds over Henan Province, China from July 18 to 20 by SCNET without SAM

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Recognition results of deep convective clouds over Henan Province, China from July 18 to 20 by SCNET without CAM

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Recognition results of deep convective clouds over Henan Province, China from July 18 to 20 by RESNET

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Recognition results of deep convective clouds over Henan Province, China from July 18 to 20 by UNET

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Recognition results of deep convective clouds over Henan Province, China from July 18 to 20 by NN

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Recognition results of deep convective clouds over Henan Province, China from July 18 to 20 by SVM

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Recognition results of deep convective clouds over Henan Province, China from July 18 to 20 by single-band threshold method

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Recognition results of deep convective clouds over Super Typhoon Chanthu by SCNET without SAM

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Recognition results of deep convective clouds over Super Typhoon Chanthu by SCNET without CAM

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Recognition results of deep convective clouds over Super Typhoon Chanthu by RESNET

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Recognition results of deep convective clouds over Super Typhoon Chanthu by UNET

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Recognition results of deep convective clouds over Super Typhoon Chanthu by NN

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Recognition results of deep convective clouds over Super Typhoon Chanthu by SVM

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Recognition results of deep convective clouds over Super Typhoon Chanthu by single-band threshold method

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Li, T., Wu, D., Wang, L. et al. Recognition algorithm for deep convective clouds based on FY4A. Neural Comput & Applic 34, 21067–21088 (2022). https://doi.org/10.1007/s00521-022-07590-x

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  • DOI: https://doi.org/10.1007/s00521-022-07590-x

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