Improving the heavy rainfall forecasting using a weighted deep learning model

被引:9
|
作者
Chen, Yutong [1 ,2 ,3 ]
Huang, Gang [1 ,2 ,3 ]
Wang, Ya [1 ]
Tao, Weichen [1 ]
Tian, Qun [4 ]
Yang, Kai [1 ]
Zheng, Jiangshan [5 ]
He, Hubin [6 ]
机构
[1] Chinese Acad Sci, Inst Atmospher Phys, State Key Lab Numer Modeling Atmospher Sci & Geop, Beijing, Peoples R China
[2] Qingdao Natl Lab Marine Sci & Technol, Lab Reg Oceanog & Numer Modeling, Qingdao, Peoples R China
[3] Univ Chinese Acad Sci, Coll Earth & Planetary Sci, Beijing, Peoples R China
[4] Guangzhou Inst Trop & Marine Meteorol, Guangdong Prov Key Lab Reg Numer Weather Predict, CMA, Guangzhou, Peoples R China
[5] Design & Res Inst Co Ltd, Shanghai Invest, Shanghai, Peoples R China
[6] Zhejiang Inst Communicat Co Ltd, Hangzhou, Peoples R China
基金
中国国家自然科学基金;
关键词
bias correction; deep learning; extremely heavy rainfall; imbalanced data; ECMWF; Henan; WEATHER PREDICTION MODELS; PRECIPITATION FORECASTS; UNCERTAINTY; MOS;
D O I
10.3389/fenvs.2023.1116672
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Weather forecasting has been playing an important role in socio-economics. However, operational numerical weather prediction (NWP) is insufficiently accurate in terms of precipitation forecasting, especially for heavy rainfalls. Previous works on NWP bias correction utilizing deep learning (DL) methods mostly focused on a local region, and the China-wide precipitation forecast correction had not been attempted. Meanwhile, earlier studies imposed no particular focus on strong rainfalls despite their severe catastrophic impacts. In this study, we propose a DL model called weighted U-Net (WU-Net) that incorporates sample weights for various precipitation events to improve the forecasts of intensive precipitation in China. It is found that WU-Net can further improve the forecasting skill of heaviest rainfall comparing with the ordinary U-Net and ECMWF-IFS. Further analysis shows that this improvement increases with growing lead time, and distributes mainly in the eastern parts of China. This study suggests that a DL model considering the imbalance of the meteorological data could further improve the precipitation forecasting generated by numerical weather prediction.
引用
收藏
页数:11
相关论文
共 50 条
  • [31] Rainfall forecasting model using machine learning methods: Case study Terengganu, Malaysia
    Ridwan, Wanie M.
    Sapitang, Michelle
    Aziz, Awatif
    Kushiar, Khairul Faizal
    Ahmed, Ali Najah
    El-Shafie, Ahmed
    AIN SHAMS ENGINEERING JOURNAL, 2021, 12 (02) : 1651 - 1663
  • [32] Load demand forecasting of residential buildings using a deep learning model
    Wen, Lulu
    Zhou, Kaile
    Yang, Shanlin
    ELECTRIC POWER SYSTEMS RESEARCH, 2020, 179
  • [33] Analysis of Financial Time Series Forecasting using Deep Learning Model
    Kumar, Raghavendra
    Kumar, Pardeep
    Kumar, Yugal
    2021 11TH INTERNATIONAL CONFERENCE ON CLOUD COMPUTING, DATA SCIENCE & ENGINEERING (CONFLUENCE 2021), 2021, : 877 - 881
  • [34] Electric vehicle charging demand forecasting using deep learning model
    Yi, Zhiyan
    Liu, Xiaoyue Cathy
    Wei, Ran
    Chen, Xi
    Dai, Jiangpeng
    JOURNAL OF INTELLIGENT TRANSPORTATION SYSTEMS, 2022, 26 (06) : 690 - 703
  • [35] Rice Yield Forecasting Using Hybrid Quantum Deep Learning Model
    Setiadi, De Rosal Ignatius Moses
    Susanto, Ajib
    Nugroho, Kristiawan
    Muslikh, Ahmad Rofiqul
    Ojugo, Arnold Adimabua
    Gan, Hong-Seng
    COMPUTERS, 2024, 13 (08)
  • [36] Electric vehicle charging demand forecasting using deep learning model
    Yi, Zhiyan
    Liu, Xiaoyue Cathy
    Wei, Ran
    Chen, Xi
    Dai, Jiangpeng
    Journal of Intelligent Transportation Systems: Technology, Planning, and Operations, 2022, 26 (06): : 690 - 703
  • [37] Improving sentiment analysis using hybrid deep learning model
    Pandey A.C.
    Rajpoot D.S.
    Recent Advances in Computer Science and Communications, 2020, 13 (04) : 627 - 640
  • [38] Improving the forecasting accuracy of monthly runoff time series of the Brahmani River in India using a hybrid deep learning model
    Swagatika, Sonali
    Paul, Jagadish Chandra
    Sahoo, Bibhuti Bhusan
    Gupta, Sushindra Kumar
    Singh, P. K.
    JOURNAL OF WATER AND CLIMATE CHANGE, 2024, 15 (01) : 139 - 156
  • [39] Short-term forecasting of typhoon rainfall with a deep-learning-based disaster monitoring model
    Kim, Doyi
    Choi, Yeji
    Seo, Minseok
    Shin, Seungheon
    Jeong, Hyun-Jin
    ENVIRONMENTAL DATA SCIENCE, 2023, 2
  • [40] Improving Explainability of Deep Learning for Polarimetric Radar Rainfall Estimation
    Li, Wenyuan
    Chen, Haonan
    Han, Lei
    GEOPHYSICAL RESEARCH LETTERS, 2024, 51 (11)