Preprints
https://doi.org/10.5194/gmd-2023-158
https://doi.org/10.5194/gmd-2023-158
Submitted as: development and technical paper
 | 
15 Aug 2023
Submitted as: development and technical paper |  | 15 Aug 2023
Status: this preprint has been withdrawn by the authors.

Deep-learning statistical downscaling of precipitation in the middle reaches of the Yellow River: A Residual in Residual Dense Block based network

He Fu, Jianing Guo, Chenguang Deng, Heng Liu, Jie Wu, Zhengguo Shi, Cailing Wang, and Xiaoning Xie

Abstract. The middle reaches of the Yellow River (MRYR), located in northern China, are the transition zone between semi-arid and semi-humid climates. As one of the climate-sensitive regions in China, MRYR has fragile ecological environment and serious soil loss, which leads to geological disasters such as landslides, collapses and mudslides caused by extreme precipitation to occur. However, scarceness of the high-resolution precipitation data over MRYR limits the assessment of the environmental impacts caused by climate change, especially for extreme precipitation. In this paper, we design a Residual in Residual Dense Block based network (RRDBNet) model for the statistical downscaling of precipitation in MRYR, and compare the proposed RRDBNet with the generalized linear regression model and two popular deep learning-based models. The results show that the proposed RRDBNet model has a good performance on precipitation simulations, which can well reproduce the spatial-temporal characteristics of high-resolution precipitation. Especially, RRDBNet has substantial improvements in extreme precipitation compared with other models. On the probability density function of daily precipitation, it is further demonstrated that RRDBNet performs better on extreme precipitation frequency. Our results suggest that the statistical downscaling based on RRDBNet may be an effective tool for historical and future climate simulations from global climate models.

This preprint has been withdrawn.

He Fu, Jianing Guo, Chenguang Deng, Heng Liu, Jie Wu, Zhengguo Shi, Cailing Wang, and Xiaoning Xie

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-158', Anonymous Referee #1, 24 Aug 2023
    • AC2: 'Reply on RC1', Xiaoning Xie, 07 Nov 2023
  • CEC1: 'Comment on gmd-2023-158', Juan Antonio Añel, 05 Sep 2023
    • AC1: 'Reply on CEC1', Xiaoning Xie, 07 Nov 2023
  • RC2: 'Comment on gmd-2023-158', Anonymous Referee #2, 26 Sep 2023
    • AC3: 'Reply on RC2', Xiaoning Xie, 07 Nov 2023
  • EC1: 'Comment on gmd-2023-158', Di Tian, 07 Nov 2023

Interactive discussion

Status: closed

Comment types: AC – author | RC – referee | CC – community | EC – editor | CEC – chief editor | : Report abuse
  • RC1: 'Comment on gmd-2023-158', Anonymous Referee #1, 24 Aug 2023
    • AC2: 'Reply on RC1', Xiaoning Xie, 07 Nov 2023
  • CEC1: 'Comment on gmd-2023-158', Juan Antonio Añel, 05 Sep 2023
    • AC1: 'Reply on CEC1', Xiaoning Xie, 07 Nov 2023
  • RC2: 'Comment on gmd-2023-158', Anonymous Referee #2, 26 Sep 2023
    • AC3: 'Reply on RC2', Xiaoning Xie, 07 Nov 2023
  • EC1: 'Comment on gmd-2023-158', Di Tian, 07 Nov 2023
He Fu, Jianing Guo, Chenguang Deng, Heng Liu, Jie Wu, Zhengguo Shi, Cailing Wang, and Xiaoning Xie
He Fu, Jianing Guo, Chenguang Deng, Heng Liu, Jie Wu, Zhengguo Shi, Cailing Wang, and Xiaoning Xie

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This preprint has been withdrawn.

Short summary
A Residual in Residual Dense Block based network model (RRDBNet) is designed for statistical downscaling of precipitation in the middle reaches of the Yellow River. RRDBNet has a good performance on precipitation simulations, well reproducing the spatial-temporal characteristics of high-resolution precipitation. RRDBNet has substantial improvements in extreme precipitation compared with generalized linear regression model and two deep learning-based models.