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Volume 12 Issue 3

Jul.  1995

Article Contents
Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 1995 >  12(3): 335-342

Neuroid BP-type Model Applied to the Study of Monthly Rainfall Forecasting

  • 1.

    Nanjing Institute of Meteorology, Nanjing 210044,Nanjing Institute of Meteorology, Nanjing 210044,Nanjing Institute of Meteorology, Nanjing 210044


doi: 10.1007/BF02656982

  • A neuroid BP-type three-layer mapping model is used for monthly rainfall forecasting in terms of 1946-1985 Nanjing monthly precipitation records as basic sequences and the model has the form i × j ＝ 8 × 3, K = 1; by steadily modifying the weighing coefficient, long-range monthly forecasts for January to December, 1986 are constructed and 1986 month-to-month predictions are made based on, say, the January measurement for February rainfall and so on, with mean absolute error reaching 6,07 and 5,73 mm, respectively. Also, with a different monthly initial value for June through September, 1994, neuroid forecasting is done, indicating the same result of the drought in Nanjing dur-ing the summer, an outcome that is in sharp agreement with the observation.
  • [1] Yan Shaojin, Peng Yongqing, Quo Guang, 1995: Monthly Mean Temperature Prediction Based on a Multi-level Mapping Model of Neural Network BP Type, ADVANCES IN ATMOSPHERIC SCIENCES, 12, 225-232.  doi: 10.1007/BF02656835
    [2] Li Yang, 2000: Baroclinic Instability in the Generalized Phillips’ Model Part II: Three-layer Model, ADVANCES IN ATMOSPHERIC SCIENCES, 17, 413-432.  doi: 10.1007/s00376-000-0033-6
    [3] Rui WANG, Yiting ZHU, Fengxue QIAO, Xin-Zhong LIANG, Han ZHANG, Yang DING, 2021: High-resolution Simulation of an Extreme Heavy Rainfall Event in Shanghai Using the Weather Research and Forecasting Model: Sensitivity to Planetary Boundary Layer Parameterization, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 98-115.  doi: 10.1007/s00376-020-9255-y
    [4] Zhou Jiabin, 1985: A NEW TYPE OF TIME-SERIES-FORECASTING METHOD, ADVANCES IN ATMOSPHERIC SCIENCES, 2, 385-401.  doi: 10.1007/BF02677255
    [5] Zhang Jijia, Chen Xingfang, 1987: THE OPERATIONAL SEASONAL FORECASTING OF THE SUMMER RAINFALL IN CHINA, ADVANCES IN ATMOSPHERIC SCIENCES, 4, 349-362.  doi: 10.1007/BF02663605
    [6] John ABBOT, Jennifer MAROHASY, 2012: Application of Artificial Neural Networks to Rainfall Forecasting in Queensland, Australia, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 717-730.  doi: 10.1007/s00376-012-1259-9
    [7] CHEN Lianshou, LI Ying, CHENG Zhengquan, 2010: An Overview of Research and Forecasting on Rainfall Associated with Landfalling Tropical Cyclones, ADVANCES IN ATMOSPHERIC SCIENCES, 27, 967-976.  doi: 10.1007/s00376-010-8171-y
    [8] Cai Zeyi, Wang Zuoshu, Pan Zaitao, 1992: A Numerical Study on Forecasting the Henan Extraordinarily Heavy Rainfall Event in August 1975, ADVANCES IN ATMOSPHERIC SCIENCES, 9, 53-62.  doi: 10.1007/BF02656930
    [9] C. V. Singh, R. S. Adhikari, H. P. Garg, 2002: Analysis of the Statistical Behaviour of Daily Maximum and Monthly Rainfall Data at New Delhi During Monsoon Period, ADVANCES IN ATMOSPHERIC SCIENCES, 19, 425-432.  doi: 10.1007/s00376-002-0076-y
    [10] SU Qin, LU Riyu, LI Chaofan, 2014: Large-scale Circulation Anomalies Associated with Interannual Variation in Monthly Rainfall over South China from May to August, ADVANCES IN ATMOSPHERIC SCIENCES, 31, 273-282.  doi: 10.1007/s00376-013-3051-x
    [11] Peng Yongqing, Yan Shaojin, Wang Tongmei, 1995: A Nonlinear Time-lag Differential Equation Model for Predicting Monthly Precipitation, ADVANCES IN ATMOSPHERIC SCIENCES, 12, 319-324.  doi: 10.1007/BF02656980
    [12] Liu Hui, Jin Xiangze, Zhang Xuehong, Wu Guoxiong, 1996: A Coupling Experiment of an Atmosphere and an Ocean Model with a Monthly Anomaly Exchange Schem, ADVANCES IN ATMOSPHERIC SCIENCES, 13, 133-146.  doi: 10.1007/BF02656857
    [13] Zhina JIANG, Da-Lin ZHANG, Hongbo LIU, 2020: Roles of Synoptic to Quasi-Monthly Disturbances in Generating Two Pre-Summer Heavy Rainfall Episodes over South China, ADVANCES IN ATMOSPHERIC SCIENCES, 37, 211-228.  doi: 10.1007/s00376-019-8156-4
    [14] Xu Youping, Xia Daqing, Qian Yueying, 1998: The Water-Bearing Numerical Model and Its Operational Forecasting Experiments Part II: The Operational Forecasting Experiments, ADVANCES IN ATMOSPHERIC SCIENCES, 15, 321-336.  doi: 10.1007/s00376-998-0004-x
    [15] Linye SONG, Wansuo DUAN, Yun LI, Jiangyu MAO, 2016: A Timescale Decomposed Threshold Regression Downscaling Approach to Forecasting South China Early Summer Rainfall, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 1071-7084.  doi: 10.1007/s00376-016-5251-7
    [16] 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
    [17] Shi Neng, Chen Jiyi, 1986: QUADRATIC DISCRIMINATION THROUGH ORTHOGONAL TRANSFORMATION AND ITS APPLICATION TO LONG-RANGE FORECASTING OF DROUGHT AND EXCESSIVE RAINFALL, ADVANCES IN ATMOSPHERIC SCIENCES, 3, 125-133.  doi: 10.1007/BF02680051
    [18] Jin Long, LuoYing, Lin Zhenshan, 1997: Comparison of Long-Term Forecasting of June-August Rainfall over Changjiang-Huaihe Valley, ADVANCES IN ATMOSPHERIC SCIENCES, 14, 87-92.  doi: 10.1007/s00376-997-0047-4
    [19] SUN Jianqi, Joong Bae AHN, 2011: A GCM-Based Forecasting Model for the Landfall of Tropical Cyclones in China, ADVANCES IN ATMOSPHERIC SCIENCES, 28, 1049-1055.  doi: 10.1007/s00376-011-0122-8
    [20] Xiaqiong ZHOU, Johnny C. L. CHEN, 2006: Ensemble Forecasting of Tropical Cyclone Motion Using a Baroclinic Model, ADVANCES IN ATMOSPHERIC SCIENCES, 23, 342-354.  doi: 10.1007/s00376-006-0342-5
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    • Manuscript History

    Manuscript received: 10 July 1995
    Manuscript revised: 10 July 1995
    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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    Neuroid BP-type Model Applied to the Study of Monthly Rainfall Forecasting

    • 1. Nanjing Institute of Meteorology, Nanjing 210044,Nanjing Institute of Meteorology, Nanjing 210044,Nanjing Institute of Meteorology, Nanjing 210044
    • Manuscript received: 1995-07-10
    • Manuscript revised: 1995-07-10

    Abstract: A neuroid BP-type three-layer mapping model is used for monthly rainfall forecasting in terms of 1946-1985 Nanjing monthly precipitation records as basic sequences and the model has the form i × j ＝ 8 × 3, K = 1; by steadily modifying the weighing coefficient, long-range monthly forecasts for January to December, 1986 are constructed and 1986 month-to-month predictions are made based on, say, the January measurement for February rainfall and so on, with mean absolute error reaching 6,07 and 5,73 mm, respectively. Also, with a different monthly initial value for June through September, 1994, neuroid forecasting is done, indicating the same result of the drought in Nanjing dur-ing the summer, an outcome that is in sharp agreement with the observation.

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