Vol.27, NO.6, 2016
Turn off MathJax
Article Contents
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.
shu

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.
    • FullText(HTML)
    References(25)

  • Fig. 1  bserved daily mean precipitation (the shaded) and its standard deviation (the isoline, unit: mm)

    DownLoad: Full-Size Img PowerPoint

    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

    DownLoad: Full-Size Img PowerPoint

    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

    DownLoad: Full-Size Img PowerPoint

    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

    DownLoad: Full-Size Img PowerPoint

    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

    DownLoad: Full-Size Img PowerPoint

    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

    DownLoad: Full-Size Img PowerPoint

    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

    DownLoad: Full-Size Img PowerPoint

    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
    DownLoad: Download CSV
  • [1]
    杜钧, 钱维宏.天气预报的三次跃进.气象科技进展, 2014, 4 (6):13-26. http://www.cnki.com.cn/Article/CJFDTOTAL-QXKZ201406008.htm
    [2]
    段明铿, 王盘兴, 李驰钦.NCEP集合预报系统在亚欧和北美区域的预报效果对比.大气科学学报, 2012, 35 (6):641-651. http://www.cnki.com.cn/Article/CJFDTOTAL-NJQX201206002.htm
    [3]
    Molteni F, Buizza R, Palmer N, et al.The ECMWF ensemble prediction system:Methodology and validation.Quart J Roy Meteor Soc, 1996, 122 (529):73-119. doi:  10.1002/(ISSN)1477-870X
    [4]
    Toth Z, Kalnay E.Ensemble forecasting at NMC:The generation of perturbations.Bull Amer Meteor Soc, 1993, 74 (12):2317-2330. doi:  10.1175/1520-0477(1993)074<2317:EFANTG>2.0.CO;2
    [5]
    Houterkamer L, Lefaiver L, Derome J, et al.A system simulation approach to ensemble prediction.Mon Wea Rev, 1996, 124 (6):1225-1242. doi:  10.1175/1520-0493(1996)124<1225:ASSATE>2.0.CO;2
    [6]
    皇甫雪官.国家气象中心集合数值预报检验评价.应用气象学报, 2002, 13 (1):29-36. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20020103&flag=1
    [7]
    夏凡, 陈静.基于T213集合预报的极端天气预报指数及温度预报应用试验.气象, 2012, 38 (12):1492-1501. http://www.cnki.com.cn/Article/CJFDTOTAL-QXXX201212007.htm
    [8]
    郭换换, 段明铿, 智协飞, 等.基于TIGGE资料的预报跳跃性特征.应用气象学报, 2016, 27 (2):220-229. doi:  10.11898/1001-7313.20160210
    [9]
    唐圣钧, 王东海, 杜钧, 等.混合集合预报法在华南暴雨短期预报中的试验.应用气象学报, 2015, 26 (6):669-679. doi:  10.11898/1001-7313.20150603
    [10]
    于连庆, 李月安, 高嵩, 等.集合预报产品综合分析显示平台关键技术与实现.应用气象学报, 2015, 26 (3):369-377. doi:  10.11898/1001-7313.20150313
    [11]
    马旭林, 薛纪善, 陆维松.GRAPES全球集合预报的集合卡尔曼变换初始扰动方案初步研究.气象学报, 2008, 66 (4):526-536. doi:  10.11676/qxxb2008.050
    [12]
    潘留杰, 张宏芳, 王建鹏.数值天气预报检验方法研究进展.地球科学进展, 2014, 29 (3):327-335. doi:  10.11867/j.issn.1001-8166.2014.03.0327
    [13]
    刘永和, 严中伟, 冯锦明, 等.基于TIGGE资料的沂沭河流域6小时降水集合预报能力分析.大气科学, 2013, 37 (3):539-551. doi:  10.3878/j.issn.1006-9895.2012.11078
    [14]
    段明铿, 王盘兴, 吴洪宝, 等.夏季亚欧中高纬度环流的集合预报效果检验.应用气象学报, 2009, 20 (1):56-61. doi:  10.11898/1001-7313.20090107
    [15]
    林春泽, 智协飞, 韩艳, 等.基于TIGGE资料的地面气温多模式超级集合预报.应用气象学报, 2009, 20 (6):706-712. doi:  10.11898/1001-7313.20090608
    [16]
    赵琳娜, 刘琳, 刘莹, 等.观测降水概率不确定性对集合预报概率Brier技巧评分结果的分析.气象, 2015, 41 (6):684-694. doi:  10.3969/2014jms.0053
    [17]
    王敏, 李晓莉, 范广洲.区域集合预报系统2 m温度预报的校准技术.应用气象学报, 2012, 23 (4):395-401. doi:  10.11898/1001-7313.20120402
    [18]
    韩焱红, 矫梅燕, 陈静, 等.基于贝叶斯理论的集合降水概率预报方法研究.气象, 2013, 39 (1):1-10. doi:  10.7522/j.issn.1000-0534.2012.00001
    [19]
    潘旸, 沈艳, 宇婧婧, 等.基于最优插值方法分析的中国区域地面观测与卫星反演逐时降水融合试验.气象学报, 2012, 70 (6):1381-1389. doi:  10.11676/qxxb2012.116
    [20]
    沈艳, 潘旸, 宇婧婧, 等.中国区域小时降水量融合产品的质量评估.大气科学学报, 2012, 36 (1):37-46. http://www.cnki.com.cn/Article/CJFDTOTAL-NJQX201301007.htm
    [21]
    潘留杰, 张宏芳, 王建鹏, 等.日本高分辨率模式对中国降水预报能力的客观检验.高原气象, 2014, 33 (2):483-494. doi:  10.7522/j.issn.1000-0534.2012.00188
    [22]
    潘留杰, 张宏芳, 朱伟军, 等.ECMWF模式对东北半球气象要素场预报能力的检验.气候与环境研究, 2013, 18 (1):112-123. http://www.cnki.com.cn/Article/CJFDTOTAL-QHYH201301013.htm
    [23]
    Schaefer J T.The critical success index as an indicator of warning skill.Wea Forecasting, 1990, 5 (4):570-575. doi:  10.1175/1520-0434(1990)005<0570:TCSIAA>2.0.CO;2
    [24]
    Brier G.Verification of Forecasts Expressed in Terms of Probability.Mon Wea Rev, 1950, 71 (1):1-3. doi:  10.1175/1520-0493%281950%29078<0001%3AVOFEIT>2.0.CO%3B2
    [25]
    Kharin K, Zwiers F W.Improved seasonal probability forecasts.J Climate, 2007, 16 (11):1684-1701. https://www.deepdyve.com/lp/american-meteorological-society/improved-seasonal-probability-forecasts-YCUxSXjWAQ
    • Relative Articles
    • Supplements(0)
    • Cited By
  • 加载中
  • -->

Catalog

    Figures(7)  / Tables(1)

    Get Citation
    PDF
    XML
    Article views (2762) PDF downloads(515) Cited by()
    • Received : 2016-03-24
    • Accepted : 2016-06-30
    • Published : 2016-11-30
    Journal Online
    Contact

    © 2020 Journal of Applied Meteorological Science   京ICP备15006967号-1

    Supported by Beijing Renhe Information Technology Co. Ltd

    /

    DownLoad:  Full-Size Img  PowerPoint
    Return