Vol.27, NO.5, 2016
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Bi Baogui, Dai Kan, Wang Yi, et al. Advances in techniques of quantitative precipitation forecast. J Appl Meteor Sci, 2016, 27(5): 534-549. DOI:  10.11898/1001-7313.20160503.
Citation: Bi Baogui, Dai Kan, Wang Yi, et al. Advances in techniques of quantitative precipitation forecast. J Appl Meteor Sci, 2016, 27(5): 534-549. DOI:  10.11898/1001-7313.20160503.
shu

Advances in Techniques of Quantitative Precipitation Forecast

DOI: 10.11898/1001-7313.20160503

  • National Meteorological Center, Beijing 100081

  • Received Date: 2016-06-16
  • Rev Recd Date: 2016-08-01
  • Publish Date: 2016-09-30
    Abstract
  • The quantitative precipitation forecast (QPF) is a core operation of weather forecast. Modern technological processes of the QPF include numerical weather forecast, verification and evaluation, objective calibration and integration, forecaster's subjective modification and gridding post-processing. Domestic and international research work covering these five aspects are investigated and summarized, to provide reference for development of the quantitative precipitation forecast.In the aspect of numerical weather forecast, the forecast skill of the operational global model for precipitation has been improving continuously (a gain of about 1 forecast day per decade), and developments of the high resolution model (especially the convection-permitting model) contribute to describing characteristics of the convective precipitation, while the ensemble models provide uncertainty information and the most possible outcome of the forecast. These two techniques are the main way to improve the fine level and accuracy of QPF, and improvement of short-term precipitation forecast by developing operational high-resolution model ensembles is the international tendency. Objective calibration and integration as well as gridding post-processing make up the statistical post-processing technique of the QPF, which now reach a level that applies multiple approaches of data mining to extract and integrate more useful information from massive data, and the emergence of reforecast dataset will further promote the development of statistical post-processing. In terms of verification and evaluation, to solve new problems in assessing the fine level and accuracy of the QPF, a variety of new verification approaches are developed and applied, such as the new score for verifying the precipitation forecast of different climate backgrounds, extremes and multiple types, spatial verification methods for avoiding dual punishments of traditional methods, as well as the probability verification methods for verifying the stability, sharpness, and resolution of the probability forecast. In the aspect of forecaster's subjective modification, although the value added to model and post-processing methods become more and more limited, forecasters still play a core role, gradually changing to help users make decision. The development of QPF techniques still face challenges of solving scientific problems such as the observation of atmosphere moisture and data assimilation methods, as well as heavy rain forecast in warm section and complex topography.
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    References(161)

  • Fig. 1  Modern technique flow of quantitative precipitation forecast

    DownLoad: Full-Size Img PowerPoint

    Table  1  A brief summary of QPF calibration and integration

    名称 主要工作 技术特点
    模式输出统计方法
    (MOS)    		 Antolik[80]2000年回顾了美
    国国家天气局的统计定量降
    水预报    		 ·利用预报因子和降水建立回归方程;
    ·可以针对站点或是格点的降水进行建模，而精
      细化的格点MOS方法依赖于高分辨率的定量
      降水估测产品;
    ·对有降水量的简单的偏差订正不适用于无降
      水的情况
    基于配料法的
    强降水等级预报    		 Doswell[81]1996年引入气象研
    究，并用于暴洪预报；
    张小玲等[82]2010年应用于我国
    的暴雨预报    		 ·依据预报量和指示量之间的物理联系建立相
      应的预报方程，而不是简单依赖于回归分析;
    ·具有清晰的物理意义
    概率匹配集合
    平均方法    		 Elbert[48]2001年首先提出并应
    用于多模式“Poor Man”的集成；
    Fang等[52]2013年针对我国台湾
    登陆台风的强降水提出了改进的
    频率匹配方法    		 ·相对于单一模式能够提高降水预报技巧，消减
      系统性的误差；
    ·既能保留降水集合平均的空间分布，也能保留
      集合成员的概率分布，好于集合平均方法；
    ·过滤掉了不可预报的小尺度信息[83]
    频率订正技术 Zhu等[84]提出并应用于NCEP的
    GFS和GEFS模式的QPF预报    		 ·利用观测降水量的频率分布校正预报降水量
      的频率分布；
    ·有效减小了降水预报误差，降水的分布型也更
      加准确
    DownLoad: Download CSV

    Table  2  A brief summary of new QPF verification scores

    检验目的 方法概要
    检验不同气候背景的降水预报 ·Rodwell等[119-120]发展了概率空间稳定相当误差 (SEEPS) 评分方法，
      该方法通过降水长期气候特征分布确定的降水等级阈值，便于比较不
      同区域和不同季节的模式QPF性能；
    ·目前成为ECMWF、芬兰等气象部门主要检验评分手段，陈法敬等[121]
      也将该方法用于我国降水的预报检验
    检验极端降水的预报能力 ·Stephenson等[122]提出用极端依赖评分 (EDS) 检验小概率事件;
    ·EDS不断得到改进，发展了稳定EDS (SEDS) 评分方法[123]，以及基于
      极端依赖指数和稳定极端依赖指数多变量的评分方法[124]，可较好地
      分辨不同模式对极端事件的预报能力[114, 125]
    对比不同类型的预报评估 ·Mason等[126]和Weigel等[127]提出通用辨别评分 (GDS) 用于度量两种
      不同实况能被相应的预报正确分辨的可能性大小，适用于不同预报类
      型的对比
    DownLoad: Download CSV

    Table  3  A brief summary of QPF spatial verification methods

    方法名称 方法概要
    场变形技术 ·将误差分解为位置、量级和残余误差，给出较为清晰的物理意义;
    ·由Hoffman等[129]在检验方法中首先引入，标志着空间检验技术开端
    特征检验技术 ·识别降水目标，然后进行配对和对比，评估目标个体之间的属性差异;
    ·采用不同方法识别降水目标，如简单阈值[130]、圆柱卷积滤波[131]等
    尺度分离技术 ·采用空间滤波分解尺度，评估模式对不同尺度上降水结构的预报能力;
    ·因采用的滤波方法不同而有所差异，如Casati等[132]基于二维小波发展
      了强度-尺度检验技术，该方法已应用于QPF临近预报检验中[133]
    邻域检验技术 ·对比检验考虑了邻近空间或时间范围内的预报和观测降水事件[134];
    ·包含了降水固有的时空分布不确定性，尤其适合高分辨率模式的检验
    DownLoad: Download CSV
  • [1]
    Arnaud P, Bouvier C, Cisneros L, et al.Influence of rainfall spatial variability on flood prediction.J Hydrol, 2002, 260:216-230. doi:  10.1016/S0022-1694(01)00611-4
    [2]
    Novak D R, Bailey C, Brill K F, et al.Precipitation and temperature forecast performance at the Weather Prediction Center.Wea Forecasting, 2014, 29:489-504, doi: 10.1175/WAF-D-13-00066.1.
    [3]
    Golding B W.Quantitative precipitation forecasting in the UK.J Hydrol, 2000, 239:286-305. doi:  10.1016/S0022-1694(00)00354-1
    [4]
    Ebert E E, Damrath U, Wergen W, et al.The WGNE assessment of short-term quantitative precipitation forecasts.Bull Amer Meteor Soc, 2003, 84:481-492. doi:  10.1175/BAMS-84-4-481
    [5]
    Sanders F.Trends in skill of Boston forecasts made at MIT, 1966-84.Bull Amer Meteor Soc, 1986, 67:170-176.
    [6]
    Applequist S, Gahrs G E, Pfeffer R L, et al.Comparison of methodologies for probabilistic quantitative precipitation forecasting.Wea Forecasting, 2002, 17:783-799. doi:  10.1175/1520-0434(2002)017<0783:COMFPQ>2.0.CO;2
    [7]
    Roebber P, Shultz D M, Colle B A, et al.Toward improved prediction:High-resolution and ensemble modeling systems in operations.Wea Forecasting, 2004, 19:936-949. doi:  10.1175/1520-0434(2004)019<0936:TIPHAE>2.0.CO;2
    [8]
    Roads J O, Maisel T N.Evaluation of the National Meteorological Center's medium range forecast model precipitation forecasts.Wea Forecasting, 1991, 6:123-132. doi:  10.1175/1520-0434(1991)006<0123:EOTNMC>2.0.CO;2
    [9]
    Olson D A, Junker N W, Korty B.Evaluation of 33 years of quantitative precipitation forecasting at the NMC.Wea Forecasting, 1995, 10:498-511. doi:  10.1175/1520-0434(1995)010<0498:EOYOQP>2.0.CO;2
    [10]
    McBride J L, Ebert E E.Verification of quantitative precipitation forecasts from operational numerical weather prediction models over Australia.Wea Forecasting, 2000, 15:103-121. doi:  10.1175/1520-0434(2000)015<0103:VOQPFF>2.0.CO;2
    [11]
    Damrath U, Doms G, Fruehwald D, et al.Operational quantitative precipitation forecasting at the German Weather Service.J Hydrol, 2000, 239:260-285. doi:  10.1016/S0022-1694(00)00353-X
    [12]
    王雨.2004年主汛期各数值预报模式定量降水预报评估.应用气象学报, 2006, 17(3):316-324. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20060357&flag=1
    [13]
    Forbes R, Haiden T, Magnusson L.Improvements in IFS Forecasts of Heavy Precipitation, ECMWF Newsletter No.144, 2015:21-26. https://www.researchgate.net/publication/292989224_Improvements_in_IFS_forecasts_of_heavy_precipitation
    [14]
    Forbes R, Tompkins A.An Improved Representation of Cloud and Precipitation.ECMWF Newsletter No.129, 2011:13-18.
    [15]
    Malardel S, Wedi N, Deconinck W, et al.A New Grid for the IFS.ECMWF Newsletter No.146, 2016:23-28. https://www.researchgate.net/profile/Christian_Kuehnlein/publication/297695132_A_new_grid_for_the_IFS/links/56e03f5108aee77a15fe8fca.pdf?origin=publication_detail
    [16]
    刘艳, 薛纪善, 张林, 等.GRAPES全球三维变分同化系统的检验与诊断.应用气象学报, 2016, 27(1):1-15. doi:  10.11898/1001-7313.20160101
    [17]
    Kaufmann P, Schubiger F, Binder P.Precipitation forecasting by a mesoscale numerical weather prediction (NWP) model:Eight years of experience.Hydrol Earth Syst Sci, 2003, 7:812-832. doi:  10.5194/hess-7-812-2003
    [18]
    Richard E, Cosma S, Benoit R, et al.Intercomparison of mesoscale meteorological models for precipitation forecasting.Hydrol Earth Syst Sci, 2003, 7:799-811. doi:  10.5194/hess-7-799-2003
    [19]
    Uccellini L W, Kocin P J, Sienkiewicz J M.Advances in Forecasting Extratropical Cyclogenesis at the National Meteorological Center//The Life Cycles of Extratropical Cyclones.Amer Meteor Soc, 1999:317-336.
    [20]
    Mass C F, Ovens D, Westrick K, et al.Does increasing horizontal resolution produce more skillful forecasts?The results of two years of real-time numerical weather prediction over the Pacific Northwest.Bull Amer Meteor Soc, 2002, 83:407-430. doi:  10.1175/1520-0477(2002)083<0407:DIHRPM>2.3.CO;2
    [21]
    Roebber P J, Gehring M G.Real-time prediction of the lake breeze on the western shore of Lake Michigan.Wea Forecasting, 2000, 15:298-312. doi:  10.1175/1520-0434(2000)015<0298:RTPOTL>2.0.CO;2
    [22]
    Nielsen-Gammon J W, Strack J.Model Resolution Dependence of Simulations of Extreme Rainfall Rates//Preprints, 10th PSU/NCAR Mesoscale Model Users Workshop.Boulder, CO, PSU/NCAR, 2000:110-111.
    [23]
    Zhang F, Snyder C, Rotunno R.Mesoscale predictability of the "surprise" snowstorm of 24-25 January 2000.Mon Wea Rev, 2002, 130:1617-1632. doi:  10.1175/1520-0493(2002)130<1617:MPOTSS>2.0.CO;2
    [24]
    Weygandt S S, Shapiro A, Droegemeier K K.Retrieval of model initial fields from single-Doppler observations of a supercell thunderstorm.Part Ⅱ:Thermodynamic retrieval and numerical prediction.Mon Wea Rev, 2002, 130:454-476. doi:  10.1175/1520-0493(2002)130<0454:ROMIFF>2.0.CO;2
    [25]
    Gallus W A, Segal M.Impact of improved initialization of mesoscale features on convective system rainfall in 10-km Eta simulations.Wea Forecasting, 2001, 16:680-696. doi:  10.1175/1520-0434(2001)016<0680:IOIIOM>2.0.CO;2
    [26]
    Larson V E, Wood R, Field P R, et al.Systematic biases in the microphysics and thermodynamics of numerical models that ignore subgridscale variability.J Atmos Sci, 2001, 58:1117-1128. doi:  10.1175/1520-0469(2001)058<1117:SBITMA>2.0.CO;2
    [27]
    Lynn B H, Khain A P, Dudhia J, et al.Spectral (bin) microphysics coupled with a Mesoscale Model (MM5).PartⅠ:Model description and first results.Mon Wea Rev, 2005, 133:44-58. doi:  10.1175/MWR-2840.1
    [28]
    Lynn B H, Khain A P, Dudhia J, et al.Spectral (bin) microphysics coupled with a mesoscale model (MM5).PartⅡ:Simulation of a CaPe rain event with squall line.Mon Wea Rev, 2005, 133:59-71. doi:  10.1175/MWR-2841.1
    [29]
    Marshall C H, Crawford K C, Mitchell E, et al.The impact of land surface physics in the operational NCEP Eta Model on simulating the diurnal cycle:Evaluation and testing using Oklahoma Mesonet data.Wea Forecasting, 2003, 18:748-768. doi:  10.1175/1520-0434(2003)018<0748:TIOTLS>2.0.CO;2
    [30]
    Colle B A, Mass C F.The 5-9 February 1996 flooding event over the Pacific Northwest:Sensitivity studies and evaluation of the MM5 precipitation forecasts.Mon Wea Rev, 2000, 128:593-617. doi:  10.1175/1520-0493(2000)128<0593:TFFEOT>2.0.CO;2
    [31]
    Colle B A.Numerical simulations of the extratropical transition of Floyd (1999):Structural evolution and responsible mechanisms for the heavy rainfall over the northeast United States.Mon Wea Rev, 2003, 131:2905-2926. doi:  10.1175/1520-0493(2003)131<2905:NSOTET>2.0.CO;2
    [32]
    Clark P, Roberts N, Lean H, et al.Convection-permitting models: a step-change in rainfall forecasting.Meteorol Appl, 2016, 23(2):165-181. doi:  10.1002/met.2016.23.issue-2
    [33]
    Lilly D K.Numerical prediction of thunderstorms-has its time come?Q J R Meteorol Soc, 1990, 116:779-798. https://www.researchgate.net/publication/229746982_Numerical_prediction_of_thunderstorms_-_has_its_time_come
    [34]
    Xue M, Wang D H, Gao J D, et al.The advanced regional prediction system (ARPS), storm-scale numerical weather prediction and data assimilation.Meteorol Atmos Phys, 2003, 82:139-170. doi:  10.1007/s00703-001-0595-6
    [35]
    Michalakes J, Chen S, Dudhia J, et al."Development of a Next Generation Regional Weather Research and Forecast Model" in Developments in Teracomputing//Proceedings of the Ninth ECMWF Workshop on the Use of High Performance Computing in Meteorology.Singapore:World Scientific, 2001.
    [36]
    Tang Y, Lean H, Bornemann J.The benefits of the Met Office variable resolution NWP model for forecasting convection.Meteorol Appl, 2013, 20:417-426. doi:  10.1002/met.2013.20.issue-4
    [37]
    Baldauf M, Seifert A, Förstner J, et al.Operational convective-scale numerical weather prediction with the COSMO model:Description and sensitivities.Mon Wea Rev, 2011, 139:3887-3905. doi:  10.1175/MWR-D-10-05013.1
    [38]
    Seity Y, Brosseau P, Malardel S, et al.The AROME-France convective-scale operational model.Mon Wea Rev, 2011, 139:976-991. doi:  10.1175/2010MWR3425.1
    [39]
    Saito K, Fujita T, Yamada Y, et al.The operational JMA nonhydrostatic mesoscale model.Mon Wea Rev, 2006, 134:1266-1298. doi:  10.1175/MWR3120.1
    [40]
    Weisman M L, Davis C, Wang W, et al.Experiences with 0-36-h explicit convective forecasts with the WRF-ARW Model.Wea Forecasting, 2008, 23:407-437. doi:  10.1175/2007WAF2007005.1
    [41]
    Roberts N M.Assessing the spatial and temporal variation in the skill of precipitation forecasts from an NWP model.Meteorol Appl, 2008, 15:163-169. doi:  10.1002/(ISSN)1469-8080
    [42]
    Grimit E P, Mass C F.Initial results of a mesoscale short-range ensemble forecasting system over the Pacific Northwest.Wea Forecasting, 2002, 17:192-205. doi:  10.1175/1520-0434(2002)017<0192:IROAMS>2.0.CO;2
    [43]
    Tracton M S, Kalnay E.Operational ensemble prediction at the National Meteorological Center:Practical aspects.Wea Forecasting, 1993, 8:379-400. doi:  10.1175/1520-0434(1993)008<0379:OEPATN>2.0.CO;2
    [44]
    赵琳娜, 刘莹, 党皓飞, 等.集合数值预报在洪水预报中的应用进展.应用气象学报, 2014, 25(6):641-653. doi:  10.11898/1001-7313.20140601
    [45]
    Palmer T N.The economic value of ensemble forecasts as a tool for risk assessment:From days to decades.Q J R Meteorol Soc, 2002, 128:747-774. doi:  10.1256/0035900021643593
    [46]
    Buizza R.The value of probabilistic prediction.Atmos Sci Lett, 2008, 9:36-42. doi:  10.1002/(ISSN)1530-261X
    [47]
    Hoffman R N, Kalney E.Lagged average forecasting, an alternative to Monte Carlo forecasting.Tellus A, 1983, 35A (2):100-118. doi:  10.1111/tela.1983.35A.issue-2
    [48]
    Ebert E E.Ability of a poor man's ensemble to predict the probability and distribution of precipitation.Mon Wea Rev, 2001, 129:2461-2479. doi:  10.1175/1520-0493(2001)129<2461:AOAPMS>2.0.CO;2
    [49]
    Toth Z, Kalnay E.Ensemble forecasting at NMC:The generation of perturbations.Bull Amer Meteor Soc, 1993, 74:2317-2330. doi:  10.1175/1520-0477(1993)074<2317:EFANTG>2.0.CO;2
    [50]
    杜钧, 李俊.集合预报方法在暴雨研究和预报中的应用.气象科技进展, 2014, 4(5):6-20. http://www.cnki.com.cn/Article/CJFDTOTAL-QXKZ201405005.htm
    [51]
    Du J.Hybrid Ensemble Prediction System:A New Ensembling Approach.Symposium on the 50th Anniversary of Operational Numerical Weather Prediction, University of Maryland, College Park, Maryland, 2004.
    [52]
    Fang X Q, Kuo Y H.Improving ensemble-based quantitative precipitation forecast for topography-enhanced typhoon heavy rainfall over Taiwan with a modified probability-matching technique.Mon Wea Rev, 2013, 141:3908-3932. doi:  10.1175/MWR-D-13-00012.1
    [53]
    Palmer T N, Molteni F, Mureau R, et al.Ensemble Prediction//Proc of the ECMWF Seminar on Validation of Models over Europe, Vol.1.1992:21-66.
    [54]
    Houtekamer P L, Lefaivre L, Derome J, et al.A system simulation approach to ensemble prediction.Mon Wea Rev, 1996, 124:1225-1242. doi:  10.1175/1520-0493(1996)124<1225:ASSATE>2.0.CO;2
    [55]
    Bougeault P, and Coauthors.The THORPEX Interactive Grand Global Ensemble (TIGGE).Bull Amer Meteor Soc, 2010, 91:1059-1072. doi:  10.1175/2010BAMS2853.1
    [56]
    Su X, Yuan H, Zhu Y, et al.Evaluation of TIGGE ensemble predictions of Northern Hemisphere summer precipitation during 2008-2012.J Geophys Res Atmos, 2014, 119:7292-7310. doi:  10.1002/2014JD021733
    [57]
    Hamill T M.Verification of TIGGE multimodel and ECMWF reforecast-calibrated probabilistic precipitation forecasts over the contiguous United States.Mon Wea Rev, 2012, 140:2232-2252. doi:  10.1175/MWR-D-11-00220.1
    [58]
    Stensrud D J, Bao J W, Warner T T.Using initial condition and model physics perturbations in short-range ensemble simulations of mesoscale convective systems.Mon Wea Rev, 2000, 128:2077-2107. doi:  10.1175/1520-0493(2000)128<2077:UICAMP>2.0.CO;2
    [59]
    Du J, and Coauthors.The NOAA/NWS/NCEP Short Range Ensemble Forecast (SREF) System: Evaluation of an Initial Condition vs Multiple Model Physics Ensemble Approach//Preprints, 16th Conf on Numerical Weather Prediction, Seattle.Amer Meteor Soc, 2004.
    [60]
    Jones M S, Colle B A, Tongue J S.Evaluation of a mesoscale short-range ensemble forecast system over the northeast United States.Wea Forecasting, 2007, 22:36-55. doi:  10.1175/WAF973.1
    [61]
    Wandishin M S, Mullen S L, Stensrud D J, et al.Evaluation of a short-range multimodel ensemble system.Mon Wea Rev, 2001, 129:729-747. doi:  10.1175/1520-0493(2001)129<0729:EOASRM>2.0.CO;2
    [62]
    Eckel F A, Mass C F.Aspects of effective mesoscale, short-range ensemble forecasting.Wea Forecasting, 2005, 20:328-350. doi:  10.1175/WAF843.1
    [63]
    Jankov I, Gallus W A, Segal M, et al.The impact of different WRF model physical parameterizations and their interactions on warm season MCS rainfall.Wea Forecasting, 2005, 20:1048-1060. doi:  10.1175/WAF888.1
    [64]
    Clark A J, Gallus W A, Chen T C.Comparison of the diurnal precipitation cycle in convection-resolving and non-convection-resolving mesoscale models.Mon Wea Rev, 2007, 135:3456-3473. doi:  10.1175/MWR3467.1
    [65]
    Fritsch J M, Carbone R E.Improving quantitative precipitation forecasts in the warm season:A USWRP research and development strategy.Bull Amer Meteor Soc, 2004, 85:955-965. doi:  10.1175/BAMS-85-7-955
    [66]
    Clark A J, Gallus W A, Xue M, et al.A comparison of precipitation forecast skill between small convection-allowing and large convection-parameterizing ensembles.Wea Forecasting, 2009, 24:1121-1140. doi:  10.1175/2009WAF2222222.1
    [67]
    Gebhardt C, Theis S, Krahe P, et al.Experimental ensemble forecasts of precipitation based on a convection-resolving model.Atmos Sci Lett, 2008, 9:67-72. doi:  10.1002/(ISSN)1530-261X
    [68]
    Clark A J, Kain J S, Stenstrud D J, et al.Probabilistic precipitation forecast skill as a function of ensemble size and spatial scale in a convection-allowing ensemble.Mon Wea Rev, 2011, 139:1052-1081. https://www.researchgate.net/profile/Keith_Brewster/publication/229012395_Probabilistic_Precipitation_Forecast_Skill_as_a_Function_of_Ensemble_Size_and_Spatial_Scale_in_a_Convection-Allowing_Ensemble/links/0c960516c4d933a481000000.pdf
    [69]
    王晨稀, 姚建群, 梁旭东.上海区域降水集合预报系统的建立与运行结果的检验.应用气象学报, 2007, 18(2):173-180. doi:  10.11898/1001-7313.20070230
    [70]
    邓国, 龚建东, 邓莲堂, 等.国家级区域集合预报系统研发和性能检验.应用气象学报, 2010, 21(5):513-523. doi:  10.11898/1001-7313.20100501
    [71]
    Bouttier F, Vie B, Nuissier O, et al.Impact of stochastic physics in a convection-permitting ensemble.Mon Wea Rev, 2012, 140:3706-3721. doi:  10.1175/MWR-D-12-00031.1
    [72]
    Duc L, Saito K, Seko H.Spatial-temporal fractions verification for high-resolution ensemble forecasts.Tellus A, 2013, 65:18171-18193. doi:  10.3402/tellusa.v65i0.18171
    [73]
    Golding B W, Ballard S P, Mylne K, et al.Forecasting Capabilities for the London 2012 Olympics.Bull Amer Meteor Soc, 2014, 95:883-896. doi:  10.1175/BAMS-D-13-00102.1
    [74]
    Caron J F.Mismatching perturbations at the lateral boundaries in limited-area ensemble forecasting:A case study.Mon Wea Rev, 2013, 141:356-374. doi:  10.1175/MWR-D-12-00051.1
    [75]
    Gebhardt C, Theis S E, Paulat M E, et al.Uncertainties in COSMO-DE precipitation forecasts introduced by model perturbations and variation of lateral boundaries.Atmos Res, 2011, 100:168-177. doi:  10.1016/j.atmosres.2010.12.008
    [76]
    Leoncini G, Plant R S, Gray S L, et al.Ensemble forecasts of a flood-producing storm:Comparison of the influence of model-state perturbations and parameter modifications.Q J R Meteorol Soc, 2013, 139:198-211. doi:  10.1002/qj.1951
    [77]
    Schwartz C S, Kain J S, Weiss S J, et al.Toward improved convection-allowing ensembles:Model physics sensitivities and optimizing probabilistic guidance with small ensemble membership.Wea Forecasting, 2010, 25:263-280. doi:  10.1175/2009WAF2222267.1
    [78]
    Ben Bouallègue Z, Theis S E.Spatial techniques applied to precipitation ensemble forecasts:From verification results to probabilistic products.Meteorol Appl, 2014, 21:922-929. doi:  10.1002/met.2014.21.issue-4
    [79]
    Scheuerer M, Hamill T M.Statistical postprocessing of ensemble precipitation forecasts by fitting Censored, shifted gamma distributions.Mon Wea Rev, 2015, 143(11):4578-4596. doi:  10.1175/MWR-D-15-0061.1
    [80]
    Antolik M S.An overview of the National Weather Service's centralized statistical quantitative precipitation forecast.J Hydrol, 2000, 239:306-337. doi:  10.1016/S0022-1694(00)00361-9
    [81]
    Doswell C A.Flash flood forecasting:An ingredient-based methodology.Wea Forecasting, 1996, 11:560-581. doi:  10.1175/1520-0434(1996)011<0560:FFFAIB>2.0.CO;2
    [82]
    张小玲, 陶诗言, 孙建华.基于"配料"的暴雨预报.大气科学, 2010, 34(4):754-764. http://www.cnki.com.cn/Article/CJFDTOTAL-DQXK201004009.htm
    [83]
    Surcel M, Zawadzki I, Yau M K.On the filtering properties of ensemble averaging for storm-scale precipitation forecasts.Mon Wea Rev, 2014, 142:1093-1105. doi:  10.1175/MWR-D-13-00134.1
    [84]
    Zhu Y J, Luo Y.Precipitation calibration based on the frequency-matching method.Wea Forecasting, 2015, 30(5):1109-1124. doi:  10.1175/WAF-D-13-00049.1
    [85]
    Bentzien S, Friederichs P.Generating and calibrating probabilistic quantitative precipitation forecasts from the high-resolution NWP model COSMO-DE.Wea Forecasting, 2012, 27(4):988-1002. doi:  10.1175/WAF-D-11-00101.1
    [86]
    Stensrud D J, Yussouf N.Reliable probabilistic quantitative precipitation forecasts from a short-range ensemble forecasting system.Wea Forecasting, 2007, 22(1):3-17. doi:  10.1175/WAF968.1
    [87]
    Gahrs G E, Applequist S, Pfeffer R L, et al.Improved results for probabilistic quantitative precipitation forecasting.Wea Forecasting, 2003, 18(5):879-890. doi:  10.1175/1520-0434(2003)018<0879:IRFPQP>2.0.CO;2
    [88]
    Friederichs P, Hense A.A probabilistic forecast approach for daily precipitation totals.Wea Forecasting, 2008, 23(4):659-673. doi:  10.1175/2007WAF2007051.1
    [89]
    Roulin E, Vannitsem S.Postprocessing of ensemble precipitation predictions with extended logistic regression based on hindcasts.Mon Wea Rev, 2012, 140(3):874-888. doi:  10.1175/MWR-D-11-00062.1
    [90]
    Ben Bouallègue Z.Calibrated short-range ensemble precipitation forecasts using extended logistic regression with interaction terms.Wea Forecasting, 2013, 28(2):515-524. doi:  10.1175/WAF-D-12-00062.1
    [91]
    Sloughter J M, Raftery A E, Gneiting T, et al.Probabilistic quantitative precipitation forecasting using Bayesian model averaging.Mon Wea Rev, 2007, 135:3209-3220. doi:  10.1175/MWR3441.1
    [92]
    Liu J, Xie Z.BMA probabilistic quantitative precipitation forecasting over the Huaihe basin using TIGGE multimodel ensemble forecasts.Mon Wea Rev, 2014, 142(4):1542-1555. doi:  10.1175/MWR-D-13-00031.1
    [93]
    Zhu J, Kong F, Ran L, et al.Bayesian model averaging with stratified sampling for probabilistic quantitative precipitation forecasting in Northern China during summer 2010.Mon Wea Rev, 2015, 143(9):3628-3641. doi:  10.1175/MWR-D-14-00301.1
    [94]
    Gneiting T, Raftery A E.Weather forecasting with ensemble methods.Science, 2005, 310:248-249. doi:  10.1126/science.1115255
    [95]
    Peel S, Wilson L J.Modeling the distribution of precipitation forecasts from the Canadian ensemble prediction system using kernel density estimation.Wea Forecasting, 2008, 23(4):575-595. doi:  10.1175/2007WAF2007023.1
    [96]
    Yuan H, Gao X, Mullen S L, et al.Calibration of probabilistic quantitative precipitation forecasts with an artificial neural network.Wea Forecasting, 2007, 22(6):1287-1303. doi:  10.1175/2007WAF2006114.1
    [97]
    Voisin N, Schaake J C, Lettenmaier D P.Calibration and downscaling methods for quantitative ensemble precipitation forecasts.Wea Forecasting, 2010, 25(6):1603-1627. doi:  10.1175/2010WAF2222367.1
    [98]
    Fernández-Ferrero A, Sáenz J, Ibarra-Berastegi G.Comparison of the performance of different analog-based Bayesian probabilistic precipitation forecasts over Bilbao, Spain.Mon Wea Rev, 2010, 138(8):3107-3119. doi:  10.1175/2010MWR3284.1
    [99]
    Gagne D J, McGovern A, Xue M.Machine learning enhancement of storm-scale ensemble probabilistic quantitative precipitation forecasts.Wea Forecasting, 2014, 29(4):1024-1043. doi:  10.1175/WAF-D-13-00108.1
    [100]
    Schaffer C J, Gallus W A, Segal M.Improving probabilistic ensemble forecasts of convection through the application of QPF-POP relationships.Wea Forecasting, 2011, 26:319-336. doi:  10.1175/2010WAF2222447.1
    [101]
    Im J S, Brill K, Danaher E.Confidence interval estimation for quantitative precipitation forecasts (QPF) using Short-Range Ensemble Forecasts (SREF).Wea Forecasting, 2006, 21(1):24-41. doi:  10.1175/WAF902.1
    [102]
    Hamill T M, Whitaker J S.Probabilistic quantitative precipitation forecasts based on reforecast analogs:Theory and application.Mon Wea Rev, 2006, 134(11):3209-3229. doi:  10.1175/MWR3237.1
    [103]
    Hamill T M, Whitaker J S, Mullen S L.Reforecasts:An important dataset for improving weather predictions.Bull Amer Meteor Soc, 2006, 87(1):33. doi:  10.1175/BAMS-87-1-33
    [104]
    Hamill T M, Hagedorn R, Whitaker J S.Probabilistic forecast calibration using ECMWF and GFS ensemble reforecasts.PartⅡ:Precipitation.Mon Wea Rev, 2008, 136:2620-2632. doi:  10.1175/2007MWR2411.1
    [105]
    Fundel F, Walser A, Liniger M A, et al.Calibrated precipitation forecasts for a limited-area ensemble forecast system using reforecasts.Mon Wea Rev, 2010, 138(1):176-189. doi:  10.1175/2009MWR2977.1
    [106]
    宗志平, 代刊, 蒋星.定量降水预报技术研究进展.气象科技进展, 2012, 2(5):29-35. http://www.cnki.com.cn/Article/CJFDTOTAL-YYQX201605003.htm
    [107]
    Pavlik D, Söhl D, Pluntke T, et al.Dynamic downscaling of global climate projections for Eastern Europe with a horizontal resolution of 7 km.Environmental Earth Sciences, 2012, 65(5):1475-1482. doi:  10.1007/s12665-011-1081-1
    [108]
    Clark M, Gangopadhyay S, Hay L, et al.The Schaake shuffle:A method for reconstructing space-time variability in forecasted precipitation and temperature fields.J Hydrol, 2004, 5(1):243-262. https://www.researchgate.net/publication/255649739_The_Schaake_Shuffle_A_Method_for_Reconstructing_Space_Time_Variability_in_Forecasted_Precipitation_and_Temperature_Fields
    [109]
    Schaake J, Henkel A, Cong S.Application of PRISM Climatologies for Hydrologic Modeling and Forecasting in the Western US//Preprints, 18th Conf on Hydrology.2004.
    [110]
    Zawadzki I.Statistical properties of precipitation patterns.J Appl Meteorol, 1973, 12:459-472. doi:  10.1175/1520-0450(1973)012<0459:SPOPP>2.0.CO;2
    [111]
    Stanski H R, Wilson L J, Burrows W R.Survey of Common Verification Methods in Meteorology.World Weather Watch Tech Rept No.8, WMO/TD No.358, Geneva:WMO, 1989.
    [112]
    Wilks D S.Statistical Methods in the Atmospheric Sciences.An Introduction.San Diego:Academic Press, 2006. http://www.citeulike.org/user/eimaj42jdp/article/244693
    [113]
    Jolliffe I T, Stephenson D B.Forecast Verification//A Practitioner's Guide in Atmospheric Science.Wiley and Sons Ltd, 2003.
    [114]
    Nurmi P.Recommendations on the Verification of Local Weather Forecasts.ECMWF Tech Memo, 2003. https://www.researchgate.net/publication/238107438_Recommendations_on_the_verification_of_local_weather_forecasts
    [115]
    Bougeault P.WGNE Survey of Verification Methods for Numerical Prediction of Weather Elements and Severe Weather Events.CAS/JSC WGNE Report No.18, 2002.
    [116]
    Wilson C.Review of Current Methods and Tools for Verification of Numerical foRecasts of Precipitation.COST717 Working Group Report on Approaches to Verification.2001.
    [117]
    Rossa A, Nurmi P, Ebert E.Overview of Methods for the Verification of Quantitative Precipitation Forecasts//Michaelides S.Precipitation:Advances in Measurement, Estimation and Prediction.Springer, Dordrecht, 2008.
    [118]
    Ebert E E, Gallus W A.Toward better understanding of the contiguous rain area (CRA) method for spatial forecast verification.Wea Forecasting, 2009, 24(5):1401-1415. doi:  10.1175/2009WAF2222252.1
    [119]
    Rodwell M J, Haiden T, Richardson D S.Developments in precipitation verification.ECMWF Newsl, 2011, 128:12-16.
    [120]
    Rodwell M J, Richardson D S, Hewson T D, et al.A new equitable score suitable for verifying precipitation in numerical weather prediction.Q J R Meteorol Soc, 2010, 136:1344-1363. https://www.researchgate.net/publication/227733303_A_new_equitable_score_for_verifying_precipitation_in_numerical_weather_prediction
    [121]
    陈法敬, 陈静."SEEPS"降水预报检验评分方法在我国降水预报中的应用试验.气象科技进展, 2015, 5(5):6-13. http://www.cnki.com.cn/Article/CJFDTOTAL-QXKZ201505006.htm
    [122]
    Stephenson D B, Casati B, Ferro C A T, et al.The extreme dependency score:A non-vanishing measure for forecasts of rare events.Meteorol Appl, 2008, 15:41-50. doi:  10.1002/(ISSN)1469-8080
    [123]
    Hogan R, O'Connor E J, Illingworth A J.Verification of cloudfraction forecasts.Q J R Meteorol Soc, 2009, 135:1494-1511. doi:  10.1002/qj.v135:643
    [124]
    Ferro C A T, Stephenson D B.Extremal dependence indice:Improved verification measures for deterministic forecasts of rare binary events.Wea Forecasting, 2011, 26:699-713. doi:  10.1175/WAF-D-10-05030.1
    [125]
    North R, Trueman M, Mittermaier M, et al.An assessment of the SEEPS and SEDI metrics for the verification of 6 h forecast precipitation accumulations.Meteorol Appl, 2013, 20:164-175. doi:  10.1002/met.2013.20.issue-2
    [126]
    Mason S J, Weigel A P.A generic forecast verification framework for administrative purposes.Mon Wea Rev, 2009, 137:331-349. doi:  10.1175/2008MWR2553.1
    [127]
    Weigel A P, Mason S J.The generalized discrimination score for ensemble forecasts.Mon Wea Rev, 2011, 139:3069-3074. doi:  10.1175/MWR-D-10-05069.1
    [128]
    Gilleland E, Ahijevych D, Brown B G, et al.Intercomparison of spatial forecast verification methods.Wea Forecasting, 2009, 24:1416-1430. doi:  10.1175/2009WAF2222269.1
    [129]
    Hoffman R N, Liu Z, Louis J F, et al.Distortion representation of forecast errors.Mon Wea Rev, 1995, 123:2758-2770. doi:  10.1175/1520-0493(1995)123<2758:DROFE>2.0.CO;2
    [130]
    Ebert E E, McBride J L.Verification of precipitation in weather systems:Determination of systematic errors.J Hydrol, 2000, 239:179-202. doi:  10.1016/S0022-1694(00)00343-7
    [131]
    Davis C, Brown B, Bullock R.Object-based verification of precipitation forecasts.Part Ⅰ:Methodology and application to mesoscale rain areas.Mon Wea Rev, 2006, 134:1772-1784. doi:  10.1175/MWR3145.1
    [132]
    Casati B, Ross G, Stephenson D B.A new intensity-scale approach for the verification of spatial precipitation forecasts.Meteorol Appl, 2004, 11:141-154. doi:  10.1017/S1350482704001239
    [133]
    孔荣, 王建捷, 梁丰, 等.尺度分解技术在定量降水临近预报检验中的应用.应用气象学报, 2010, 21(5):535-544. doi:  10.11898/1001-7313.20100503
    [134]
    Ebert E E.Fuzzy verification of high resolution gridded forecasts:A review and proposed framework.Meteorol Appl, 2008, 15:53-66. https://www.researchgate.net/publication/227657507_Fuzzy_verification_of_high-resolution_gridded_forecasts_A_review_and_proposed_framework
    [135]
    Zhu Y, Toth Z.Ensemble Based Probabilistic Forecast Verification.19th Conference on Probability and Statistics, 2008. http://www.emc.ncep.noaa.gov/gmb/yzhu/gif/pub/AMS_Zhu_2008.pdf
    [136]
    Hamill T M.Interpretation of rank histograms for verifying ensemble forecasts.Mon Wea Rev, 2001, 129:550-560. doi:  10.1175/1520-0493(2001)129<0550:IORHFV>2.0.CO;2
    [137]
    Hersbach H.Decomposition of the continuous rank probability score for ensemble prediction systems.Wea Forecasting, 2000, 15:559-570. doi:  10.1175/1520-0434(2000)015<0559:DOTCRP>2.0.CO;2
    [138]
    Wilks D S.The minimum spanning tree (MST) histogram as a verification tool for multidimensional ensemble forecasts.Mon Wea Rev, 2004, 132:1329-1340. doi:  10.1175/1520-0493(2004)132<1329:TMSTHA>2.0.CO;2
    [139]
    Weisheimer A, Smith L A, Judd K.A new view of forecast skill:Bounding boxes from the DEMETER ensemble seasonal forecasts.Tellus, 2004, 57(3):265-279. https://www.researchgate.net/profile/Leonard_Smith5/publication/227602808_A_new_view_of_seasonal_forecast_skill_Bounding_boxes_from_the_DEMETER_ensemble_forecasts/links/02e7e537f84aa8119d000000/A-new-view-of-seasonal-forecast-skill-Bounding-boxes-from-the-DEMETER-ensemble-forecasts.pdf
    [140]
    Roulston M S, Smith L A.Evaluating probabilistic forecasts using information theory.Mon Wea Rev, 2002, 130:1653-1660. doi:  10.1175/1520-0493(2002)130<1653:EPFUIT>2.0.CO;2
    [141]
    Wilson L J, Burrows W R, Lanzinger A.A strategy for verification of weather element forecasts from an ensemble prediction system.Mon Wea Rev, 1999, 127:956-970. doi:  10.1175/1520-0493(1999)127<0956:ASFVOW>2.0.CO;2
    [142]
    Brier G W.Verification of forecasts expressed in terms of probability.Mon Wea Rev, 1950, 78:1-3. doi:  10.1175/1520-0493(1950)078<0001:VOFEIT>2.0.CO;2
    [143]
    Brocker J, Smith L A.Increasing the reliability of reliability diagrams.Wea Forecasting, 2007, 22(3):651-661. doi:  10.1175/WAF993.1
    [144]
    Swets J A, Pickett R M.Evaluation of Diagnostic Systems:Methods from Signal Detection Theory.New York:Academic Press, 1982.
    [145]
    Epstein E S.A scoring system for probability forecasts of ranked categories.J Applied Meteorology, 1969, 8:985-987. doi:  10.1175/1520-0450(1969)008<0985:ASSFPF>2.0.CO;2
    [146]
    Murphy A H.A new vector partition of the probability score.J Applied Meteorology, 1973, 12:595-600. doi:  10.1175/1520-0450(1973)012<0595:ANVPOT>2.0.CO;2
    [147]
    Stephenson D B.Definition, Diagnosis, and Origin of Extreme Weather and Climate Events//Climate Extremes and Society.New York:Cambridge University Press, 2008.
    [148]
    Ferro C A T, Richardson D S, Weigel A P.On the effect of ensemble size on the discrete and continuous ranked probability scores.Meteorol Appl, 2008, 15:19-24. doi:  10.1002/(ISSN)1469-8080
    [149]
    Funk T W.Forecasting techniques utilized by the forecast branch of the national meteorological center during a major convective rainfall event.Wea Forecasting, 1991, 6(4):548-564. doi:  10.1175/1520-0434(1991)006<0548:FTUBTF>2.0.CO;2
    [150]
    Reynolds D.Value-added quantitative precipitation forecasts:How valuable is the forecaster?Bull Amer Meteor Soc, 2003, 84:876-878. doi:  10.1175/BAMS-84-7-876
    [151]
    Doswell C A Ⅲ, Brooks H E.Budgetcutting and the value of weather services.Wea Forecasting, 1998, 13:206-212. doi:  10.1175/1520-0434(1998)013<0206:BCATVO>2.0.CO;2
    [152]
    Novak D R, Bright D R, Brennan M J.Operational forecaster uncertainty needs and future roles.Wea Forecasting, 2008, 23:1069-1084. doi:  10.1175/2008WAF2222142.1
    [153]
    Stuart N A, and Coauthors.The future role of the human in an increasingly automated forecast process.Bull Amer Meteor Soc, 2006, 87:1497-1502. doi:  10.1175/BAMS-87-11-1497
    [154]
    Mass C F. IFPS and the future of the National Weather Service.Wea Forecasting, 2003, 18:75-79. doi:  10.1175/1520-0434(2003)018<0075:IATFOT>2.0.CO;2
    [155]
    Homar V, Stensrud D J, Levit J J, et al.Value of human-generated perturbations in short-range ensemble forecasts of severe weather.Wea Forecasting, 2006, 21:347-363. doi:  10.1175/WAF920.1
    [156]
    Sills D M L.On the MSC forecasters forums and the future role of the human forecaster.Bull Amer Meteor Soc, 2009, 90:619-627. doi:  10.1175/2008BAMS2657.1
    [157]
    Stuart N A, Schultz D M, Klein G.Maintaining the role of humans in the forecast process.Bull Amer Meteor Soc, 2007, 88:1893-1898. doi:  10.1175/BAMS-88-12-1893
    [158]
    Fujita T, Stensrud D J, Dowell D C.Using precipitation observations in a mesoscale short-range ensemble analysis and forecasting system.Wea Forecasting, 2008, 23:357-372. doi:  10.1175/2007WAF2006108.1
    [159]
    Marcus S, Kim J, Chin T, et al.Influence of GPS precipitable water vapor retrievals on quantitative precipitation forecasting in Southern California.J Appl Meteor Climatol, 2007, 46:1828-1839. doi:  10.1175/2007JAMC1502.1
    [160]
    Fritsch J M, Carbone R.Improving quantitative precipitation forecasts in the warm season:A USWRP research and development strategy.Bull Amer Meteor Soc, 2004, 85:955-965. doi:  10.1175/BAMS-85-7-955
    [161]
    Richard E, Buzzi A, Zängl G.Quantitative precipitation forecasting in the Alps:The advances achieved by the Mesoscale Alpine Programme.Q J R Meteorol Soc, 2007, 133:831-846. doi:  10.1002/(ISSN)1477-870X
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