Modelling the ZR Relationship of Precipitation Nowcasting Based on Deep Learning

Authors: Ma, J., Cui, X. and Jiang, N.

Journal: Computers, Materials and Continua

Volume: 72

Issue: 1

Pages: 1939-1949

eISSN: 1546-2226

ISSN: 1546-2218

DOI: 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 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.

http://eprints.bournemouth.ac.uk/36722/

Source: Scopus

Modelling the ZR Relationship of Precipitation Nowcasting Based on Deep Learning

Authors: Ma, J., Cui, X. and Jiang, N.

Journal: CMC-COMPUTERS MATERIALS & CONTINUA

Volume: 72

Issue: 1

Pages: 1939-1949

eISSN: 1546-2226

ISSN: 1546-2218

DOI: 10.32604/cmc.2022.025206

http://eprints.bournemouth.ac.uk/36722/

Source: Web of Science (Lite)

Modelling the ZR Relationship of Precipitation Nowcasting Based on Deep Learning

Authors: Ma, J., Cui, X. and Jiang, N.

Journal: Computers, Materials and Continua

Volume: 72

Issue: 1

Pages: 1939-1949

eISSN: 1546-2226

ISSN: 1546-2218

DOI: 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 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.

http://eprints.bournemouth.ac.uk/36722/

Source: Manual

Preferred by: Nan Jiang

Modelling the ZR Relationship of Precipitation Nowcasting Based on Deep Learning

Authors: Ma, J., Cui, X. and Jiang, N.

Journal: Computers, Materials and Continua

Volume: 72

Issue: 1

Pages: 1939-1949

ISSN: 1546-2218

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 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.

http://eprints.bournemouth.ac.uk/36722/

Source: BURO EPrints