Assessment of Deep Learning-Based Nowcasting Using Weather Radar in South Korea

Author:

Yoon Seong-Sim1ORCID,Shin Hongjoon2ORCID,Heo Jae-Yeong3,Choi Kwang-Bae2

Affiliation:

1. Korea Institute of Civil Engineering and Building Technology, 283, Goyangdae-ro, Ilsanseo-gu, Goyang-si 10223, Gyeonggi-do, Republic of Korea

2. Hydro-Power Research and Training Center, Korea Hydro & Nuclear Power Co., Ltd., Gyeongju-si 38120, Gyeongsangbuk-do, Republic of Korea

3. Department of Civil and Environmental Engineering, Sejong University, 209, Neungdong-ro, Gunja-dong, Gwangjin-gu, Seoul 05006, Republic of Korea

Abstract

This study examines the effectiveness of various deep learning algorithms in nowcasting using weather radar data from South Korea. Herein, the algorithms examined include RainNet, ConvLSTM2D U-Net, a U-Net-based recursive model, and a generative adversarial network. Moreover, this study used S-band radar data from the Ministry of Environment to assess the predictive performance of these models. Results show the efficacy of these algorithms in short-term rainfall prediction. Specifically, for a threshold of 0.1 mm/h, the recursive RainNet model achieved a critical success index (CSI) of 0.826, an F1 score of 0.781, and a mean absolute error (MAE) of 0.378. However, for a higher threshold of 5 mm/h, the model achieved an average CSI of 0.498, an F1 score of 0.577, and a MAE of 0.307. Furthermore, some models exhibited spatial smoothing issues with increasing rainfall-prediction times. The findings of this research hold promise for applications of societal importance, especially for preventing disasters due to extreme weather events.

Funder

KOREA HYDRO & NUCLEAR POWER CO., LTD

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Reference20 articles.

1. Kim, M.O., Lee, J.W., Cho, K.H., and Kim, S.H. (2021). Korean Climate Change Assessment Report 2020—The Physical Science Basis 40, Korea Meteorological Administration.

2. Shi, X., Chen, Z., Wang, H., Yeung, D., Wong, W., and Woo, W. (2015, January 7–12). Convolutional LSTM network: A machine learning approach for precipitation nowcasting. Proceedings of the 28th International Conference on Neural Information Processing Systems, Montreal, QC, Canada.

3. Shi, X., Gao, Z., Lausen, L., Wang, H., Yeung, D., Wong, W., and Woo, W. (2017, January 4–9). Deep learning for precipitation nowcasting: A benchmark and a new model. In Proceeding of the 31st Conference on Neural Information Processing Systems (NIPS), Long Beach, CA, USA.

4. Deep learning and process understanding for data-driven Earth system science;Reichstein;Nature,2019

5. Tran, Q.K., and Song, S.K. (2019). Computer vision in precipitation nowcasting: Applying image quality assessment metrics for training deep neural networks. Atmosphere, 10.

Cited by 1 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Theoretical Assessment for Weather Nowcasting Using Deep Learning Methods;Archives of Computational Methods in Engineering;2024-03-19

同舟云学术

1.学者识别学者识别

2.学术分析学术分析

3.人才评估人才评估

"同舟云学术"是以全球学者为主线,采集、加工和组织学术论文而形成的新型学术文献查询和分析系统,可以对全球学者进行文献检索和人才价值评估。用户可以通过关注某些学科领域的顶尖人物而持续追踪该领域的学科进展和研究前沿。经过近期的数据扩容,当前同舟云学术共收录了国内外主流学术期刊6万余种,收集的期刊论文及会议论文总量共计约1.5亿篇,并以每天添加12000余篇中外论文的速度递增。我们也可以为用户提供个性化、定制化的学者数据。欢迎来电咨询!咨询电话:010-8811{复制后删除}0370

www.globalauthorid.com

TOP

Copyright © 2019-2024 北京同舟云网络信息技术有限公司
京公网安备11010802033243号  京ICP备18003416号-3