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Modelling the ZR Relationship of Precipitation Nowcasting Based on Deep Learning.

Ma, J., Cui, X. and Jiang, N., 2022. Modelling the ZR Relationship of Precipitation Nowcasting Based on Deep Learning. Computers, Materials and Continua, 72 (1), 1939 - 1949.

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TSP_CMC_25206.pdf - Published Version
Available under License Creative Commons Attribution.


DOI: 10.32604/cmc.2022.025206


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 performsmuch better than the equation-based ZRformula and has the similar performance with a pure deep learning nowcasting model, both for all level precipitation and heavy ones only.

Item Type:Article
Uncontrolled Keywords:Keywords Deep learning; meteorology; precipitation nowcasting; weather forecasting; ZR formula
Group:Faculty of Science & Technology
ID Code:36722
Deposited By: Symplectic RT2
Deposited On:07 Mar 2022 16:01
Last Modified:14 Mar 2022 14:33


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