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Article

Reconstruction of the Radar Reflectivity of Convective Storms Based on Deep Learning and Himawari-8 Observations

1
College of Oceanic and Atmospheric Sciences, Ocean University of China, Qingdao 266100, China
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Institute of Atmospheric Physics, Chinese Academy of Sciences, Beijing 100029, China
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College of Earth and Planetary Sciences, University of Chinese Academy of Sciences, Beijing 100049, China
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College of Information Science and Engineering, Ocean University of China, Qingdao 266100, China
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Institute of Urban Meteorology, CMA, Beijing 100089, China
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Meteorological Observation Center, China Meteorological Administration, Beijing 100081, China
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State Key Laboratory of Earth Surface Processes and Resource Ecology, College of Global Change and Earth System Science, Beijing Normal University, Beijing 100875, China
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Institut Pierre-Simon Laplace, 4 Place Jussieu, 75005 Paris, France
*
Author to whom correspondence should be addressed.
Academic Editor: Andreas Reigber
Remote Sens. 2021, 13(16), 3330; https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163330
Received: 22 July 2021 / Revised: 16 August 2021 / Accepted: 18 August 2021 / Published: 23 August 2021
Radar reflectivity (RR) greater than 35 dBZ usually indicates the presence of severe convective weather, which affects a variety of human activities, including aviation. However, RR data are scarce, especially in regions with poor radar coverage or substantial terrain obstructions. Fortunately, the radiance data of space-based satellites with universal coverage can be converted into a proxy field of RR. In this study, a convolutional neural network-based data-driven model is developed to convert the radiance data (infrared bands 07, 09, 13, 16, and 16–13) of Himawari-8 into the radar combined reflectivity factor (CREF). A weighted loss function is designed to solve the data imbalance problem due to the sparse convective pixels in the sample. The developed model demonstrates an overall reconstruction capability and performs well in terms of classification scores with 35 dBZ as the threshold. A five-channel input is more efficient in reconstructing the CREF than the commonly used one-channel input. In a case study of a convective event over North China in the summer using the test dataset, U-Net reproduces the location, shape and strength of the convective storm well. The present RR reconstruction technology based on deep learning and Himawari-8 radiance data is shown to be an efficient tool for producing high-resolution RR products, which are especially needed for regions without or with poor radar coverage. View Full-Text
Keywords: aviation; deep learning; convective storms; weather radar reflectivity; Himawari-8 aviation; deep learning; convective storms; weather radar reflectivity; Himawari-8
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MDPI and ACS Style

Duan, M.; Xia, J.; Yan, Z.; Han, L.; Zhang, L.; Xia, H.; Yu, S. Reconstruction of the Radar Reflectivity of Convective Storms Based on Deep Learning and Himawari-8 Observations. Remote Sens. 2021, 13, 3330. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163330

AMA Style

Duan M, Xia J, Yan Z, Han L, Zhang L, Xia H, Yu S. Reconstruction of the Radar Reflectivity of Convective Storms Based on Deep Learning and Himawari-8 Observations. Remote Sensing. 2021; 13(16):3330. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163330

Chicago/Turabian Style

Duan, Mingshan, Jiangjiang Xia, Zhongwei Yan, Lei Han, Lejian Zhang, Hanmeng Xia, and Shuang Yu. 2021. "Reconstruction of the Radar Reflectivity of Convective Storms Based on Deep Learning and Himawari-8 Observations" Remote Sensing 13, no. 16: 3330. https://0-doi-org.brum.beds.ac.uk/10.3390/rs13163330

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