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Modelling the ZR Relationship of Precipitation Nowcasting Based on Deep Learning

Jianbing Ma1,*, Xianghao Cui1, Nan Jiang2

1 Chengdu University of Information Technology, Chengdu, 610225, China
2 Bournemouth University, Bournemouth, BH12 5BB, UK

* Corresponding Author: Jianbing Ma. Email: email

Computers, Materials & Continua 2022, 72(1), 1939-1949. https://doi.org/10.32604/cmc.2022.025206

Abstract

Sudden precipitations may bring troubles or even huge harm to people's daily lives. Hence a timely and accurate precipitation nowcasting is expected to be an indispensable part of our modern life. Traditionally, the rainfall intensity estimation from weather radar is based on the relationship between radar reflectivity factor (Z) and rainfall rate (R), which is typically estimated by location-dependent experiential formula and arguably uncertain. Therefore, in this paper, we propose a deep learning-based method to model the ZR relation. To evaluate, we conducted our experiment with the Shenzhen precipitation dataset. We proposed a combined method of deep learning and the ZR relationship, and compared it with a traditional ZR equation, a ZR equation with its parameters estimated by the least square method, and a pure deep learning model. The experimental results show that our combined model performs much better than the equation-based ZR formula and has the similar performance with a pure deep learning nowcasting model, both for all level precipitation and heavy ones only.

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Cite This Article

APA Style
Ma, J., Cui, X., Jiang, N. (2022). Modelling the ZR relationship of precipitation nowcasting based on deep learning. Computers, Materials & Continua, 72(1), 1939-1949. https://doi.org/10.32604/cmc.2022.025206
Vancouver Style
Ma J, Cui X, Jiang N. Modelling the ZR relationship of precipitation nowcasting based on deep learning. Comput Mater Contin. 2022;72(1):1939-1949 https://doi.org/10.32604/cmc.2022.025206
IEEE Style
J. Ma, X. Cui, and N. Jiang "Modelling the ZR Relationship of Precipitation Nowcasting Based on Deep Learning," Comput. Mater. Contin., vol. 72, no. 1, pp. 1939-1949. 2022. https://doi.org/10.32604/cmc.2022.025206



cc This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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