Wei Guofei, Liu Huijun, Wu Qishu, et al. Multi-model consensus forecasting technology with optimal weight for precipitation intensity levels. J Appl Meteor Sci, 2020, 31(6): 668-680. DOI:  10.11898/1001-7313.20200603.
Citation: Wei Guofei, Liu Huijun, Wu Qishu, et al. Multi-model consensus forecasting technology with optimal weight for precipitation intensity levels. J Appl Meteor Sci, 2020, 31(6): 668-680. DOI:  10.11898/1001-7313.20200603.

Multi-model Consensus Forecasting Technology with Optimal Weight for Precipitation Intensity Levels

DOI: 10.11898/1001-7313.20200603
  • Received Date: 2020-06-27
  • Rev Recd Date: 2020-08-26
  • Publish Date: 2020-10-27
  • In the daily weather forecasting business, different model outputs are available for forecasters, but it's difficult to quickly and accurately make quantitative precipitation forecasts based on subjective analysis. Therefore, statistical post-processing techniques are required to scientifically and rationally integrate the multi-model forecast results, so as to obtain a forecast result that take advantages of each model, and the multi-model consensus forecasting technology is introduced. In the past, the research of multi-model consensus precipitation forecast is either based on global model or regional model, but they are rarely integrated. In addition, for a certain forecast, weights are constant, rarely considering variation of forecast ability among different models and precipitation intensity levels. It is found that the unrevised global and regional model present different advantages in forecasting precipitation of different intensities. Multi-model consensus forecasting for precipitation based on both global and regional models, integrating respective advantages of models at different precipitation levels would produce better objective forecasts.To synthesize both forecasting advantages in global and regional models, a consensus forecasting technology combining global and regional models with optimized weights for different precipitation intensity levels is designed. The consensus forecast combined revised ECMWF-IFS's(European Center for Medium-Range Weather Forecasts-Integrated Forecast System) and SMS-WARMS's(Shanghai Meteorological Service WRF ADAS Real-Time Modeling System) precipitation forecasts, which are revised by optimal threat score method(abbreviated as EC-OTS and SMS-OTS) in the Pan-Yangtze River region(23°-39°N, 101°-123°E). Take 2018 as the model training period of consensus weight and 2019 as the independent sample forecast test period. Comparing the consensus forecast with EC-OTS, SMS-OTS and subjective forecast of forecasters, results show that EC-OTS has a greater weight at low precipitation levels, with the increase of precipitation level, the weight of SMS-OTS gradually increases. The average absolute error of the consensus forecast is slightly smaller than EC-OTS and significantly smaller than SMS-OTS with all lead times. The consensus forecast has higher threat scores than EC-OTS and SMS-OTS with almost all lead times at all precipitation levels. The threat score of the 12 h accumulated precipitation of the consensus forecast is -0.009 to 0.041 higher than the subjective forecast of local forecasters, and the threat score of 24 h accumulated precipitation forecast is 0.009 to 0.023 higher than the subjective forecast of China National Meteorological Center forecasters.
  • Fig. 1  Target area

    Fig. 2  Threat score of 12 h accumulated precipitation forecasted by ECMWF-IFS, EC-OTS, SMS-WARMS and SMS-OTS with ranges of no less than 0.1 mm, no less than 10 mm, no less than 25 mm and no less than 50 mm in 2018

    Fig. 3  Threat score, false alarm ratio, missing ratio of 12 h accumulated precipitation from EC-OTS, SMS-OTS and consensus forecast with lead time of 24 h and mean absolute error of 12 h accumulative precipitation with lead time from 12 h to 72 h in 2018

    Fig. 4  Threat score, false alarm ratio, miss ratio of 12 h accumulated precipitation from EC-OTS, SMS-OTS and consensus forecast with lead time of 24 h and mean absolute error of 12 h accumulated precipitation with lead time from 12 h to 72 h in 2019

    Fig. 5  Quarterly skill score of 12 h accumulated precipitation with lead time of 24 h forecasted by consensus forecast relative to SMS-OTS and EC-OTS in 2018-2019

    Fig. 6  Twenty-four-hour accumulated precipitation forecasted by SMS-OTS, EC-OTS and consensus forecast initiated at 2000 BT 21 Jun 2019 with lead time of 36 h and observed precipition from 0800 BT 22 Jun to 0800 BT 23 Jun in 2019

    Table  1  Optimal weight coefficients of SMS-OTS with different lead times and precipitation levels (the weight coefficient of EC-OTS is 1 minus the weight coefficient of SMS-OTS)

    降水量级 预报时效/h
    12 24 36 48 60 72
    不低于0.1 mm 0.03 0.03 0.02 0.02 0.01 0.02
    不低于1 mm 0.10 0.10 0.06 0.06 0.06 0.07
    不低于5 mm 0.38 0.34 0.26 0.24 0.27 0.31
    不低于10 mm 0.30 0.40 0.34 0.32 0.32 0.37
    不低于25 mm 0.35 0.53 0.31 0.40 0.39 0.45
    不低于35 mm 0.40 0.47 0.51 0.47 0.45 0.40
    不低于50 mm 0.43 0.58 0.67 0.72 0.63 0.91
    不低于75 mm 0.56 0.88 0.97 0.80 0.90 0.55
    不低于100 mm 0.65 0.91 0.81 0.80 1.00 0.78
    DownLoad: Download CSV

    Table  2  Parameters of ten integrated schemes

    集成方案 初始集成降水量预报Y0生成方法 迭代次数 备注
    1 方法1 1
    2 方法1 2
    3 方法1 3
    4 方法2 1
    5 方法2 2
    6 方法2 3
    7 方法3 1
    8 方法3 2
    9 方法3 3
    10 等权重集成
    DownLoad: Download CSV

    Table  3  Ordinary rainfall threat score of 12 h accumulated precipitation forecasted by different integration schemes with different lead times in 2018

    集成方案 预报时效/h
    12 24 36 48 60 72
    1 0.589 0.564 0.546 0.529 0.515 0.497
    2 0.588 0.563 0.545 0.529 0.514 0.497
    3 0.588 0.564 0.546 0.529 0.515 0.497
    4 0.588 0.562 0.542 0.525 0.509 0.489
    5 0.588 0.563 0.545 0.528 0.513 0.495
    6 0.588 0.563 0.545 0.528 0.514 0.495
    7 0.588 0.564 0.544 0.527 0.511 0.492
    8 0.588 0.563 0.545 0.529 0.514 0.496
    9 0.588 0.564 0.546 0.529 0.514 0.497
    10 0.574 0.540 0.506 0.486 0.475 0.456
    DownLoad: Download CSV

    Table  4  Rainstorm threat score of 12 h accumulated precipitation forecasted by different integration schemes with different lead times in 2018

    集成方案 预报时效/h
    12 24 36 48 60 72
    1 0.184 0.143 0.117 0.082 0.063 0.045
    2 0.189 0.150 0.128 0.090 0.066 0.048
    3 0.188 0.151 0.126 0.084 0.066 0.047
    4 0.186 0.153 0.134 0.099 0.076 0.064
    5 0.190 0.156 0.135 0.097 0.081 0.058
    6 0.190 0.155 0.136 0.099 0.080 0.064
    7 0.190 0.151 0.127 0.088 0.071 0.051
    8 0.190 0.151 0.131 0.091 0.071 0.056
    9 0.190 0.151 0.126 0.088 0.071 0.051
    10 0.189 0.151 0.130 0.091 0.071 0.056
    DownLoad: Download CSV

    Table  5  Comparison of threat score of 12 h accumulated precipitation in the Pan-Yangtze River region between local forecaster and consensus forecast in 2019

    降水量级 预报方法 预报时效/h
    12 24 36 48 60
    不低于0.1 mm 主观预报 0.543 0.522 0.502 0.489 0.472
    集成预报 0.550 0.536 0.519 0.507 0.494
    不低于10 mm 主观预报 0.310 0.292 0.272 0.256 0.236
    集成预报 0.345 0.323 0.302 0.283 0.261
    不低于25 mm 主观预报 0.218 0.195 0.176 0.155 0.135
    集成预报 0.259 0.231 0.206 0.193 0.159
    不低于50 mm 主观预报 0.133 0.114 0.096 0.078 0.064
    集成预报 0.165 0.144 0.118 0.096 0.075
    不低于100 mm 主观预报 0.079 0.074 0.047 0.016 0.010
    集成预报 0.089 0.065 0.055 0.049 0.030
    DownLoad: Download CSV

    Table  6  Comparison of threat score of 24 h accumulated precipitation in the Pan-Yangtze River region between China National Meteorological Center forecaster and consensus forecast in 2019

    降水量级 预报方法 预报时效/h
    24 48
    不低于0.1 mm 主观预报 0.608 0.580
    集成预报 0.620 0.592
    不低于10 mm 主观预报 0.405 0.374
    集成预报 0.421 0.383
    不低于25 mm 主观预报 0.311 0.277
    集成预报 0.332 0.286
    不低于50 mm 主观预报 0.226 0.182
    集成预报 0.243 0.198
    不低于100 mm 主观预报 0.142 0.092
    集成预报 0.157 0.115
    DownLoad: Download CSV
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    • Received : 2020-06-27
    • Accepted : 2020-08-26
    • Published : 2020-10-27

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