Vol.27, NO.6, 2016
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Article Navigation >  Journal of Applied Meteorological Science  > 2016  >  27(6): 676-687
Pan Liujie, Xue Chunfang, Zhang Hongfang, et al. Comparative analysis on precipitation forecasting capabilities of two ensemble prediction systems around Qinling Area. J Appl Meteor Sci, 2016, 27(6): 676-687. DOI:  10.11898/1001-7313.20160604.
Citation: Pan Liujie, Xue Chunfang, Zhang Hongfang, et al. Comparative analysis on precipitation forecasting capabilities of two ensemble prediction systems around Qinling Area. J Appl Meteor Sci, 2016, 27(6): 676-687. DOI:  10.11898/1001-7313.20160604.
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Comparative Analysis on Precipitation Forecasting Capabilities of Two Ensemble Prediction Systems Around Qinling Area

DOI: 10.11898/1001-7313.20160604
  • 1.

    Shaanxi Meteorological Observatory, Xi'an 710014

  • 2.

    Shaanxi Meteorological Bureau, Xi'an 710014

  • 3.

    Shaanxi Meteorological Service Center, Xi'an 710014

  • Received Date: 2016-03-24
  • Rev Recd Date: 2016-06-30
  • Publish Date: 2016-11-30
    Abstract
  • Using precipitation forecast data of ECMWF and NCEP ensemble prediction systems, hourly rainfall data fusion by CMORPH (NOAA Climate Prediction Center Morphing Method) satellites and 30000 automatic weather stations, the precipitation and precipitation probability forecasting capability of ECMWF and NCEP ensemble prediction systems around Qinling area are comparatively analyzed from June to October in 2013 and 2014, mainly based on classic skill score and ROC (relative operating characteristic) statistical method. Results show that the precipitation spatial distribution pattern can be better described by both ECMWF and NCEP ensemble prediction systems with the disadvantages that forecasted high value center is larger and the precipitation amplitude is small; the correlation coefficient of ECMWF control forecast and perturb forecast with observations is higher, the standard deviation ratio is close to 1.0 in previous 10 days, which is better than NCEP, but NCEP forecast skill score has better performance than ECMWF in 264-360 hours.The ensemble mean skill score for heavy rain of ECMWF ensemble prediction system is better than NCEP in 0-120 hours. The forecast skill score of ensemble mean is lower than control forecast and perturb forecast for both two systems. Ensemble mean significantly reduces the standard deviation of precipitation amplitude and this is not conducive to the accuracy of synoptic scale precipitation prediction. Ensemble mean significantly increases forecast bias of light rain and increases the false rate, while the forecast bias of heavy rain and the fail rate decrease. This phenomenon is more remarkable when ensemble mean contains more perturb members and forecast skill is roughly the same between different members, and this makes ECMWF ensemble mean skill scores for light rain lower than NCEP.Overall, ECMWF probability forecast effect is better than NCEP. When precipitation threshold increases, BS score of both two models increases sharply while the forecast capacity significantly reduces, for storms, the ROC area is smaller than climate probability sometimes. The ROC analysis show that as the forecast probability improves, ECMWF ensemble prediction system slightly decreases the hit rate and significantly reduces the false rate, however, NCEP have a high false rate and higher hit rate. So depending on user's requirements, different model can be chosen as reference.
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  • Fig. 1  bserved daily mean precipitation (the shaded) and its standard deviation (the isoline, unit: mm)

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    Fig. 2  Forecasted daily mean precipitation (the shaded) and its standard deviation (the isoline, unit: mm) during study period (a) ECMWF control forecast, (b) ECMWF ensemble mean, (c) NCEP control forecast, (d) NCEP ensemble mean

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    Fig. 3  Daily mean precipitation and its standard deviation of perturbation members, control forecast and observation (the shaded denotes amplitude of perturbation members of corresponding ensemble prediction system)

    (a) daily mean precipitation of ECMWF, (b) daily mean precipitation of NCEP, (c) standard deviation of ECMWF, (d) standard deviation of NCEP

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    Fig. 4  Skill scores of perturbation members, control forecast and ensemble mean for daily precipitation forecasting of ECMWF and NCEP (the shaded denotes amplitude of perturbation members of corresponding ensemble prediction system)

    (a) ECMWF forecast bias of light rain, (b) NCEP forecast bias of light rain, (c) ECMWF TS score of light rain, (d) NCEP TS score of light rain, (e) ECMWF forecast bias of heavy rain, (f) NCEP forecast bias of heavy rain, (g) ECMWF TS score of heavy rain, (h) NCEP TS score of heavy rain

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    Fig. 5  he spatial distribution of precipitation forecast skill scores for ECMWF and NCEP (the shaded denotes the control forecast, the isoline denotes the ensemble mean)

    (a) ECMWF forecast bias of light rain, (b) NCEP forecast bias of light rain, (c) ECMWF TS score of light rain, (d)NCEP TS score of light rain

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    Fig. 6  Brier score and ROC area for different magnitude rainfall

    (a) ECMWF BS score, (b) NCEP BS score, (c) ECMWF ROC area, (d) NCEP ROC area

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    Fig. 7  ROC charts of light rain and heavy rain for ECMWF and NCEP

    (a) ROC chart of light rain for ECMWF, (b) ROC chart of light rain for NCEP, (c) ROC chart of heavy rain for ECMWF, (d) ROC chart of heavy rain for NCEP

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    Table  1  The standard deviation ratio and abnormal correlation coefficient of 24-hour accumulated precipitation between the forecast and observation at different forecast period

    预报时效/h ECMWF系统控制预报 ECMWF系统集合平均 NCEP系统控制预报 NCEP系统集合平均
    ACC σ ACC σ ACC σ ACC σ
    24 0.728 0.892 0.681 0.812 0.718 0.815 0.707 0.777
    48 0.712 1.003 0.649 0.829 0.703 0.938 0.684 0.877
    72 0.693 1.030 0.605 0.769 0.682 1.047 0.652 0.929
    96 0.683 0.954 0.598 0.693 0.684 1.093 0.644 0.919
    120 0.691 0.902 0.589 0.600 0.683 1.067 0.623 0.807
    144 0.678 0.891 0.570 0.562 0.662 1.041 0.592 0.711
    168 0.652 0.924 0.546 0.536 0.644 0.994 0.565 0.654
    192 0.653 0.879 0.533 0.461 0.645 1.023 0.557 0.619
    216 0.641 1.003 0.497 0.472 0.637 1.019 0.536 0.648
    240 0.620 0.829 0.484 0.407 0.619 0.956 0.530 0.551
    264 0.528 1.177 0.486 0.473 0.612 0.923 0.501 0.479
    288 0.601 1.040 0.447 0.416 0.611 1.029 0.469 0.517
    312 0.591 1.135 0.432 0.427 0.600 1.081 0.449 0.526
    336 0.588 1.098 0.426 0.402 0.582 0.994 0.436 0.499
    360 0.572 1.110 0.417 0.429 0.588 1.194 0.426 0.601
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    • Received : 2016-03-24
    • Accepted : 2016-06-30
    • Published : 2016-11-30
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