Zhi Xiefei, Zhao Chen. Heavy precipitation forecasts based on multi-model ensemble members. J Appl Meteor Sci, 2020, 31(3): 303-314. DOI:  10.11898/1001-7313.20200305.
Citation: Zhi Xiefei, Zhao Chen. Heavy precipitation forecasts based on multi-model ensemble members. J Appl Meteor Sci, 2020, 31(3): 303-314. DOI:  10.11898/1001-7313.20200305.

Heavy Precipitation Forecasts Based on Multi-model Ensemble Members

DOI: 10.11898/1001-7313.20200305
  • Received Date: 2019-10-28
  • Rev Recd Date: 2020-01-08
  • Publish Date: 2020-05-31
  • Based on the daily 24-168 h ensemble precipitation forecasts over China from 1 May to 31 August in 2016 from the global ensemble models of ECMWF, JMA, NCEP, CMA and UKMO extracted from the TIGGE archives, the frequency matching method is tested to calibrate the precipitation frequency of each ensemble member. Then results of multi-model ensemble forecasts before and after calibration, including Kalman filter(KF), multi-model super-ensemble (SUP) and bias-removed ensemble mean(BREM), are analyzed in order to improve the prediction of precipitation based on numerical weather forecast data. Results show that precipitation forecasts calibrated by the frequency matching method, which uses the moderate precipitation to correct light and heavy precipitation, can effectively improve the problem of the underestimation of heavy precipitation caused by ensemble mean forecast and improve the positive deviation of the ensemble forecasting system, so that the precipitation forecast category is closer to the observation. However, the frequency matching method can barely improve the prediction of precipitation area. Different from frequency matching method, multi-model ensemble forecasts can extract and consider features of each model, therefore the prediction of precipitation area is more accurate than each single model, but the result is not as good as the frequency matching method in terms of the prediction of precipitation category. Among different multi-model methods, because of the updated weights over time, the result of Kalman filter forecast is superior to SUP and BREM in terms of threat scores, root mean square error (RMSE) and anomaly correlation coefficient (ACC). Furthermore, combining advantages of the above two methods, the multi-model ensemble precipitation after calibration based on ensemble members is more effective in the prediction of heavy precipitation category and area, which is closer to the observation. Results improve the threat score (TS) of the precipitation in all forecast lead times, especially in the heavy precipitation with the TS of 24 h forecast reaching 0.26, indicating a lower false alarm rate and missing rate compared with single model. Results also improve ACC and RMSE of the heavy precipitation and this method produces the best results among all the other methods, especially in the coastal areas in the south of China. In terms of the prediction of precipitation area, results effectively optimize the area of heavy and light precipitation, making the multi-model ensemble precipitation after calibration best in predicting heavy precipitation processes.
  • Fig. 1  Distributions of 24 h accumulated precipitation in observation and forecast with lead time of 24 h for KF, SUP, BREM, ECMWF, UKMO and JMA over South China (18°-30°N, 102°-120°E) on 12 Aug 2016

    Fig. 2  Regional mean absolute error of 24 h accumulated precipitation(a) and threat score(TS) of heavy rain(b) for KF, SUP, BREM, ECMWF, UKMO and JMA during forecasting period

    Fig. 3  Taragrand distribution of precipitation with 168 h lead time in ECMWF, JMA and UKMO before and after calibrated by FMM

    Fig. 4  TS of light rain, moderate rain, heavy rain and rainstorm for 24 h accumulated precipitation with lead time from 24 h to 168 h for ECMWF, JMA, NCEP, UKMO and CMA over China from May to Aug in 2016 before(the dashed line) and after(the solid line) FMM calibration

    Fig. 5  TS of light rain, moderate rain, heavy rain, rainstorm and heavy rainstorm for 24 h accumulated precipitation with different lead time from 24 h to 168 h for FMM_KF, KF, BREM, FMM_UK, UKMO and ECMWF over China during forecasting period

    Fig. 6  The FAR and MR of heavy rain, rainstorm and heavy rainstorm for 24 h accumulated precipitation with different lead time from 24 h to 168 h for FMM_KF, KF, BREM, FMM_UK, UKMO and ECMWF over China during forecasting period

    Fig. 7  Regional averaged root mean square error(a) and anomaly correlation coefficient(b) of 24 h accumulated precipitation with lead time from 24 h to 168 h for FMM_KF, KF, BREM, FMM_UK, UKMO and ECMWF during forecasting period

    Fig. 8  The distribution of 24 h accumulated precipitation in observation and forecast with 24 h lead time for FMM_KF, KF, BREM, FMM_UK, UKMO over South China and South China Sea(15°-25°N, 105°-122°E) on 17 Aug 2016

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    • Received : 2019-10-28
    • Accepted : 2020-01-08
    • Published : 2020-05-31

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