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Volume 23 Issue 3

May  2006

Article Contents
Article >  ADVANCES IN ATMOSPHERIC SCIENCES  > 2006 >  23(3): 405-414

A Short-Range Quantitative Precipitation Forecast Algorithm Using Back-Propagation Neural Network Approach

  • 1.

    Department of Atmospheric Science, Zhongshan University, Guangzhou 510275, Guangdong Provincial Meteorological Observatory, Guangzhou 510080,Hydrology Laboratory, Office of Hydrologic Development, National Weather Service, NOAA, USA


doi: 10.1007/s00376-006-0405-7

  • A back-propagation neural network (BPNN) was used to establish relationships between the shortrange (0-3-h) rainfall and the predictors ranging from extrapolative forecasts of radar reflectivity, satelliteestimated cloud-top temperature, lightning strike rates, and Nested Grid Model (NGM) outputs. Quantitative precipitation forecasts (QPF) and the probabilities of categorical precipitation were obtained. Results of the BPNN algorithm were compared to the results obtained from the multiple linear regression algorithm for an independent dataset from the 1999 warm season over the continental United States. A sample forecast was made over the southeastern United States. Results showed that the BPNN categorical rainfall forecasts agreed well with Stage III observations in terms of the size and shape of the area of rainfall. The BPNN tended to over-forecast the spatial extent of heavier rainfall amounts, but the positioning of the areas with rainfall >25.4 mm was still generally accurate. It appeared that the BPNN and linear regression approaches produce forecasts of very similar quality, although in some respects BPNN slightly outperformed the regression.
  • [1] Kanghui ZHOU, Jisong SUN, Yongguang ZHENG, Yutao ZHANG, 2022: Quantitative Precipitation Forecast Experiment Based on Basic NWP Variables Using Deep Learning, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 1472-1486.  doi: 10.1007/s00376-021-1207-7
    [2] Haibo ZOU, Shanshan WU, Miaoxia TIAN, 2023: Radar Quantitative Precipitation Estimation Based on the Gated Recurrent Unit Neural Network and Echo-Top Data, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 1043-1057.  doi: 10.1007/s00376-022-2127-x
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    [6] Lu ZHOU, Rong-Hua ZHANG, 2022: A Hybrid Neural Network Model for ENSO Prediction in Combination with Principal Oscillation Pattern Analyses, ADVANCES IN ATMOSPHERIC SCIENCES, 39, 889-902.  doi: 10.1007/s00376-021-1368-4
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    [8] Tingyu WANG, Ping HUANG, 2024: Superiority of a Convolutional Neural Network Model over Dynamical Models in Predicting Central Pacific ENSO, ADVANCES IN ATMOSPHERIC SCIENCES, 41, 141-154.  doi: 10.1007/s00376-023-3001-1
    [9] Jiang HUANGFU, Zhiqun HU, Jiafeng ZHENG, Lirong WANG, Yongjie ZHU, 2024: Study on Quantitative Precipitation Estimation by Polarimetric Radar Using Deep Learning, ADVANCES IN ATMOSPHERIC SCIENCES.  doi: 10.1007/s00376-023-3039-0
    [10] Xingchao CHEN, Kun ZHAO, Juanzhen SUN, Bowen ZHOU, Wen-Chau LEE, 2016: Assimilating Surface Observations in a Four-Dimensional Variational Doppler Radar Data Assimilation System to Improve the Analysis and Forecast of a Squall Line Case, ADVANCES IN ATMOSPHERIC SCIENCES, 33, 1106-1119.  doi: 10.1007/s00376-016-5290-0
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    [14] WANG Gaili, WONG Waikin, LIU Liping, WANG Hongyan, 2013: Application of Multi-Scale Tracking Radar Echoes Scheme in Quantitative Precipitation Nowcasting, ADVANCES IN ATMOSPHERIC SCIENCES, 30, 448-460.  doi: 10.1007/s00376-012-2026-7
    [15] Ling WANG, Xiuqing HU, Na XU, Lin CHEN, 2021: Water Vapor Retrievals from Near-infrared Channels of the Advanced Medium Resolution Spectral Imager Instrument onboard the Fengyun-3D Satellite, ADVANCES IN ATMOSPHERIC SCIENCES, 38, 1351-1366.  doi: 10.1007/s00376-020-0174-8
    [16] Yang LI, Yubao LIU, Rongfu SUN, Fengxia GUO, Xiaofeng XU, Haixiang XU, 2023: Convective Storm VIL and Lightning Nowcasting Using Satellite and Weather Radar Measurements Based on Multi-Task Learning Models, ADVANCES IN ATMOSPHERIC SCIENCES, 40, 887-899.  doi: 10.1007/s00376-022-2082-6
    [17] WANG Gaili, LIU Liping, DING Yuanyuan, 2012: Improvement of Radar Quantitative Precipitation Estimation Based on Real-Time Adjustments to Z--R Relationships and Inverse Distance Weighting Correction Schemes, ADVANCES IN ATMOSPHERIC SCIENCES, 29, 575-584.  doi: 10.1007/s00376-011-1139-8
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    • Manuscript History

    Manuscript received: 10 May 2006
    Manuscript revised: 10 May 2006
    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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    A Short-Range Quantitative Precipitation Forecast Algorithm Using Back-Propagation Neural Network Approach

    • 1. Department of Atmospheric Science, Zhongshan University, Guangzhou 510275, Guangdong Provincial Meteorological Observatory, Guangzhou 510080,Hydrology Laboratory, Office of Hydrologic Development, National Weather Service, NOAA, USA
    • Manuscript received: 2006-05-10
    • Manuscript revised: 2006-05-10

    Abstract: A back-propagation neural network (BPNN) was used to establish relationships between the shortrange (0-3-h) rainfall and the predictors ranging from extrapolative forecasts of radar reflectivity, satelliteestimated cloud-top temperature, lightning strike rates, and Nested Grid Model (NGM) outputs. Quantitative precipitation forecasts (QPF) and the probabilities of categorical precipitation were obtained. Results of the BPNN algorithm were compared to the results obtained from the multiple linear regression algorithm for an independent dataset from the 1999 warm season over the continental United States. A sample forecast was made over the southeastern United States. Results showed that the BPNN categorical rainfall forecasts agreed well with Stage III observations in terms of the size and shape of the area of rainfall. The BPNN tended to over-forecast the spatial extent of heavier rainfall amounts, but the positioning of the areas with rainfall >25.4 mm was still generally accurate. It appeared that the BPNN and linear regression approaches produce forecasts of very similar quality, although in some respects BPNN slightly outperformed the regression.

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