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Volume 18 Issue 5

Sep.  2001

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Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2001 >  18(5): 950-958

Forecasting Monsoon Precipitation Using Artificial Neural Networks

  • 1.

    School of Business Systems, Faculty of Information Technology, Monash University, Australia,Chinese Academy of Meteorological Sciences, Beijing 100081,School of Business Systems, Faculty of Information Technology, Monash University, Australia,Chinese Academy of Meteorological Sciences, Beijing 100081,Chinese Academy of Meteorological Sciences, Beijing 100081


doi: 10.1007/s00376-997-0014-0

  • This paper explores the application of Artificial Intelligent (AI) techniques for climate forecast. It pres ents a study on modelling the monsoon precipitation forecast by means of Artificial Neural Networks (ANNs). Using the historical data of the total amount of summer rainfall over the Delta Area of Yangtze River in China, three ANNs models have been developed to forecast the monsoon precipitation in the corre sponding area one year, five-year, and ten-year forward respectively. Performances of the models have been validated using a ＇new＇ data set that has not been exposed to the models during the processes of model development and test. The experiment results are promising, indicating that the proposed ANNs models have good quality in terms of the accuracy, stability and generalisation ability.
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    [2] LI Hongmei, ZHOU Tianjun, LI Chao, 2010: Decreasing Trend in Global Land Monsoon Precipitation over the Past 50 Years Simulated by a Coupled Climate Model, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 285-292.  doi: 10.1007/s00376-009-8173-9
    [3] LIN Zhenshan, SHI Xiangsheng, 2003: The Decade-Scale Climatic Forecasting in China, ADVANCES IN ATMOSPHERIC SCIENCES, 20, 604-611.  doi: 10.1007/BF02915503
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    [6] Abebe Kebede, Kirsten Warrach-sagi, Thomas Schwitalla, Volker Wulfmeyer, Tesfaye Amdie, Markos Ware, 2024: Assessment of Seasonal Rainfall Prediction in Ethiopia: Evaluating a Dynamic Recurrent Neural Network to Downscale ECMWF-SEAS5 Rainfall, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-024-3345-1
    [7] XIAO Cunying, HU Xiong, 2010: Applying Artificial Neural Networks to Modeling the Middle Atmosphere, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 883-890.  doi: 10.1007/s00376-009-9019-1
    [8] Su Jeong LEE, Myoung-Hwan AHN, Yeonjin LEE, 2016: Application of an Artificial Neural Network for a Direct Estimation of Atmospheric Instability from a Next-Generation Imager, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 221-232.  doi: 10.1007/s00376-015-5084-9
    [9] WANG Xin, Lü Daren, 2005: Retrieval of Water Vapor Profiles with Radio Occultation Measurements Using an Artificial Neural Network, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 759-764.  doi: 10.1007/BF02918719
    [10] Temesgen Gebremariam ASFAW, Jing-Jia LUO, 2024: Downscaling Seasonal Precipitation Forecasts over East Africa with Deep Convolutional Neural Networks, ADVANCES IN ATMOSPHERIC SCIENCES, 41, 449-464.  doi: 10.1007/s00376-023-3029-2
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    [12] Yang Hongwei, Wang Bin, Ji Zhongzhen, 2002: Application of the Artificial Compression Method to the Simulation of Two-Dimensional Frontogenesis, ADVANCES IN ATMOSPHERIC SCIENCES, 19, 863-869.  doi: 10.1007/s00376-002-0051-7
    [13] YAO Zhigang, CHEN Hongbin, LIN Longfu, 2005: Retrieving Atmospheric Temperature Profiles from AMSU-A Data with Neural Networks, ADVANCES IN ATMOSPHERIC SCIENCES, 22, 606-616.  doi: 10.1007/BF02918492
    [14] HUANG Yanyan, XUE Jishan, WAN Qilin, CHEN Zitong, DING Weiyu, ZHANG Chengzhong, 2013: Improvement of the Surface Pressure Operator in GRAPES and Its Application in Precipitation Forecasting in South China, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 354-366.  doi: 10.1007/s00376-012-1270-1
    [15] Tang Maocang, Li Tianshi, Zhang Jian, Li Cunqiang, 1989: The Operational Forecasting of Total Precipitation in Flood Seasons (April to September) of 5 Years (1983-1987), ADVANCES IN ATMOSPHERIC SCIENCES, 6, 289-300.  doi: 10.1007/BF02661535
    [16] LIU Xiangwen, WU Tongwen, YANG Song, JIE Weihua, NIE Suping, LI Qiaoping, CHENG Yanjie, LIANG Xiaoyun, 2015: Performance of the Seasonal Forecasting of the Asian Summer Monsoon by BCC_CSM1.1(m), ADVANCES IN ATMOSPHERIC SCIENCES, 32, 1156-1172.  doi: 10.1007/s00376-015-4194-8
    [17] Congwen ZHU, Boqi LIU, Kang XU, Ning JIANG, Kai LIU, 2021: Diversity of the Coupling Wheels in the East Asian Summer Monsoon on the Interannual Time Scale: Challenge of Summer Rainfall Forecasting in China, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 546-554.  doi: 10.1007/s00376-020-0199-z
    [18] H. Kurtulus OZCAN, Erdem BILGILI, Ulku SAHIN, O. Nuri UCAN, Cuma BAYAT, 2007: Modeling of Trophospheric Ozone Concentrations Using Genetically Trained Multi-Level Cellular Neural Networks, ADVANCES IN ATMOSPHERIC SCIENCES, 24, 907-914.  doi: 10.1007/s00376-007-0907-y
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    [20] Liu Liping, Feng Jinming, Chu Rongzhong, Zhou Yunjun, K. Ueno, 2002: The Diurnal Variation of Precipitation in Monsoon Season in the Tibetan Plateau, ADVANCES IN ATMOSPHERIC SCIENCES, 19, 365-378.  doi: 10.1007/s00376-002-0028-6
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    • Manuscript History

    Manuscript received: 10 September 2001
    Manuscript revised: 10 September 2001
    通讯作者: 陈斌, bchen63@163.com
    • 1. 

      沈阳化工大学材料科学与工程学院 沈阳 110142

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    Forecasting Monsoon Precipitation Using Artificial Neural Networks

    • 1. School of Business Systems, Faculty of Information Technology, Monash University, Australia,Chinese Academy of Meteorological Sciences, Beijing 100081,School of Business Systems, Faculty of Information Technology, Monash University, Australia,Chinese Academy of Meteorological Sciences, Beijing 100081,Chinese Academy of Meteorological Sciences, Beijing 100081
    • Manuscript received: 2001-09-10
    • Manuscript revised: 2001-09-10

    Abstract: This paper explores the application of Artificial Intelligent (AI) techniques for climate forecast. It pres ents a study on modelling the monsoon precipitation forecast by means of Artificial Neural Networks (ANNs). Using the historical data of the total amount of summer rainfall over the Delta Area of Yangtze River in China, three ANNs models have been developed to forecast the monsoon precipitation in the corre sponding area one year, five-year, and ten-year forward respectively. Performances of the models have been validated using a ＇new＇ data set that has not been exposed to the models during the processes of model development and test. The experiment results are promising, indicating that the proposed ANNs models have good quality in terms of the accuracy, stability and generalisation ability.

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