地球科学进展 ›› 2019, Vol. 34 ›› Issue (7): 731 -746. doi: 10.11867/j.issn.1001-8166.2019.07.0731

研究论文 上一篇    下一篇

联合概率方法在安徽强对流潜势预报中的应用和检验
朱月佳 1( ),邢蕊 2,朱明佳 3,王东勇 1,邱学兴 1   
  1. 1. 安徽省气象台,安徽 合肥 230031
    2. 天津市滨海新区气象局,天津 300457
    3. 安徽省人工影响天气办公室,安徽 合肥 230031
  • 收稿日期:2019-03-08 修回日期:2019-05-18 出版日期:2019-07-10
  • 基金资助:
    安徽省气象局预报员专项项目“基于联合概率的安徽省强对流潜势预报”(kY201507);中国气象局基建项目“全国集合预报业务系统建设(一期)

Application and Verification of Joint Probability Method in Potential Forecast for Severe Convective Weather in Anhui Province

Yuejia Zhu 1( ),Rui Xing 2,Mingjia Zhu 3,Dongyong Wang 1,Xuexing Qiu 1   

  1. 1. Anhui Meteorological Observatory, Hefei 230031, China
    2. Tianjin Binhai New District Meteorological Bureau, Tianjin 300457, China
    3. Anhui Weather Modification Office, Hefei 230031, China
  • Received:2019-03-08 Revised:2019-05-18 Online:2019-07-10 Published:2019-07-29
  • About author:Zhu Yuejia (1986-), female, Suzhou City, Jiangsu Province, Engineer. Research areas include ensemble post-processing. E-mail: zhuyuejia_124@163.com
  • Supported by:
    ect supported by the Anhui Meteorological Bureau Special Project for Forecasters “Potential forecast for severe convective weather in Anhui Province based on joint probability method”(kY201507);The China Meteorological Administration Infrastructure Project "National ensemble forecasting operational system construction(Phase I)

强对流天气预报的不确定性很大,集合预报是定量估计预报不确定性的动力学方法。联合概率方法是基于集合预报模式输出,综合考虑多个重要物理参数的不确定性,进而预报天气事件可能发生的关键区。通过时空匹配2009—2015年4~9月(暖季)安徽省80个国家站逐日灾害性天气观测资料、降水观测资料和美国国家环境预报中心(NCEP)的FNL资料,借鉴“配料法”思路统计分析得到弱降水对流、强降水对流天气各4个具有较好表征意义的对流参数及各对流参数在暖季逐月的阈值,据此建立弱降水对流、强降水对流联合概率预报方程。利用2016—2017年4~9月欧洲中期天气预报中心(ECMWF)集合预报产品和同期观测资料开展联合概率预报的系统性ROC检验和不同天气过程检验。结果表明,联合概率方法对于是否发生弱降水对流或强降水对流具有良好的分辨能力,对判别强降水对流发生与否更优,不同时效的预报表现相当。弱降水对流联合概率对于区域性或范围较小的对流天气均具有较好的指示性,但也存在一定程度的空报。强降水对流联合概率对于区域性集中短时强降水具有良好的指示作用,对较小范围的强降水也具有一定指示意义。此方法也存在个别漏报,可以大致将10%作为联合概率预报阈值以获得较高的TS评分。基于集合预报的联合概率方法可以为强对流天气提供具有实用价值的短期概率预报指导。

In consideration of large uncertainties in severe convective weather forecast, ensemble forecasting is a dynamic method developed to quantitatively estimate forecast uncertainty. Based on ensemble output, joint probability is a post-processing method to delineate key areas where weather event may actually occur by taking account of the uncertainty of several important physical parameters. An investigation of the environments of little rainfall convection and strong rainfall convection from April to September (warm season) during 2009-2015 was presented using daily disastrous weather data, precipitation data of 80 stations in Anhui province and NCEP Final Analysis (FNL) data. Through ingredients-based forecasting methodology and statistical analysis,four convective parameters characterizing two types of convection were obtained, respectively, which were used to establish joint probability forecasting together with their corresponding thresholds. Using the ECMWF ensemble forecast and observations from April to September during 2016-2017, systematic verification mainly based on ROC and case study of different weather processes were conducted. The results demonstrate that joint probability method is capable of discriminating little rainfall convection and non-convection with comparable performance for different lead times, which is more favorable to identifying the occurrence of strong rainfall convection. The joint probability of little rainfall convection is a good indication for the occurrence of regional or local convection, but may produce some false alarms. The joint probability of strong rainfall convection is good at indicating regional concentrated short-term heavy precipitation as well as local heavy rainfall. There are also individual missing reports in this method, and in practice, 10% can be roughly used as joint probability threshold to achieve relative high TS score. Overall, ensemble-based joint probability method can provide practical short-term probabilistic guidance for severe convective weather.

中图分类号: 

图1 20092015年暖季对流的基本特征
Fig.1 Basic features of convection in warm season during the period from 2009 to 2015
图2 20092015年平均暖季弱降水对流(a)和强降水对流(b)发生日数空间分布
Fig.2 Spatial distribution of annual mean (a) little rainfall convective days and (b) strong rainfall convective days in warm season during the period of 2009-2015
图3 20092015年暖季逐月不同类型对流天气的水汽条件箱线图
Fig.3 Box-and-whisker plots for environments of moisture by month and type of convection in warm season during the period of 2009-2015
图4 20092015年暖季逐月不同类型对流天气的不稳定条件箱线图
Fig.4 Box-and-whisker plots for environments of instability by month and type of convection in warm season during the period of 2009-2015
图5 20092015年暖季逐月不同类型对流天气的不稳定条件箱线图
Fig.5 Box-and-whisker plots for environments of instability by month and type of convection in warm season during the period of 2009-2015
图6 20092015年暖季逐月不同类型对流天气的动力条件箱线图
Fig.6 Box-and-whisker plots for dynamic condition by month and type of convection in warm season during the period of 2009-2015
图7 20092015年暖季对流参数联合分布
Fig. 7 Joint distribution of convective parameters in warm season during the period of 2009-2015
图8 暖季逐月不同类型对流天气的整层大气可降水量箱线图
Fig.8 Box-and-whisker plots for precipitable water for entire atmospheric column by month and type of convection in warm season
图9 2010年、2011年和2015年暖季逐月利用不同分析资料得到的对流参数箱线图
Fig.9 Box-and-whisker plots for convective parameters by month using different analysis data in warm season for years of 2010, 2011 and 2015
表1 弱降水对流中选取的各对流参数的阈值
Table 1 Thresholds of convective parameters selected for little rainfall convection
表2 强降水对流中选取的各对流参数的阈值
Table 2 Thresholds of convective parameters selected for strong rainfall convection
图10 20162017年暖季2类对流的ROC检验
Fig.10 Verification of two types of convection in warm season during the period of 2016-2017 based on ROC metrics
图11 20162017年暖季2类对流的TS评分分析
Fig. 11 Analysis of TS for two types of convection in warm season during the period of 2016-2017
图12 联合概率预报(单位:%)与对流发生情况的空间分布
Fig. 12 Spatial distribution of joint probability forecasts (unit: %) and corresponding convective weather
1 DuJun, ChenJing. The corner stone in facilitating the transition from deterministic to probabilistic forecasts-ensemble forecasting and its impact on numerical weather prediction[J]. Meteorological Monthly,2010,36(11):1-11.
杜钧,陈静.单一值预报向概率预报转变的基础:谈谈集合预报及其带来的变革[J].气象,2010,36(11):1-11.
2 DuJun, LiJun. Application of ensemble methodology to heavy-rain research and prediction[J]. Advances in Meteorological Science and Technology, 2014,4(5):6-20.
杜钧,李俊.集合预报方法在暴雨研究和预报中的应用[J].气象科技进展,2014,4(5):6-20.
3 MaJuhui, ZhuYuejian, WangPanxing, et al. A review on the developments of NCEP, ECMWF and CMC global ensemble forecast system[J]. Transactions of Atmospheric Sciences, 2011,34(3):370-380.
麻巨慧,朱跃建,王盘兴,等. NCEP、ECMWF及CMC全球集合预报业务系统发展综述[J].大气科学学报,2011,34(3):370-380.
4 DuJ, YuR C, CuiC G, et al. Using a mesoscale ensemble to predict forecast error and perform targeted observation[J]. Acta Oceanologica Sinica, 2014,33(1): 83-91.
5 BowlerN E, ArribasA, MylneK R, et al. The MOGREPS short-range ensemble prediction system[J]. Quarterly Journal of the Royal Meteorological Society, 2008,134(632):703-722.
6 HagelinS, SonJ, SwinbankR, et al. The Met Office convective-scale ensemble, MOGREPS-UK[J]. Quarterly Journal of the Royal Meteorological Society, 2017,143(708):2 846-2 861.
7 LiuLin, ChenJing, WangJiaoyang. A study on medium-range objective weather forecast technology for persistent heavy rainfall events based on T639 ensemble forecast[J]. Acta Meteorologica Sinica,2018,76(2):228-240.
刘琳,陈静,汪娇阳.基于T639集合预报的持续性强降水中期客观预报技术研究[J].气象学报,2018,76(2):228-240.
8 ZhangH B, ChenJ, ZhiX F, et al. Study on multi-scale blending initial condition perturbations for a regional ensemble prediction system[J]. Advances in Atmospheric Sciences,2015,32(8):1 143-1 155.
9 LiJiangfeng, CaiXiaojun, WangWen, et al. Application of partial least squares regression in multimodal integrated forecasting of water vapor and surface air temperature[J]. Advances in Earth Science, 2018,33(4):404-415.
李江峰,蔡晓军,王文,等.偏最小二乘回归在水汽和地面气温多模式集成预报中的应用研究[J].地球科学进展,2018,33(4):404-415.
10 ZhangLixia, ZhangWenxia, ZhouTianjun, et al. Assessment of the decadal prediction skill on global land summer monsoon precipitation in the coupled models of ENSEMBLES[J]. Advances in Earth Science,2017,32(4):409-419.
张丽霞,张文霞,周天军,等.ENSEMBLES耦合模式对全球陆地季风区夏季降水的年代际预测能力评估[J].地球科学进展,2017,32(4):409-419.
11 DaiKan, ZhuYuejian, BiBaogui. The review of statistical post-process technologies for quantitative precipitation forecast of ensemble prediction system[J]. Acta Meteorologica Sinica, 2018,76(4):493-510.
代刊,朱跃建,毕宝贵.集合模式定量降水预报的统计后处理技术研究综述[J].气象学报,2018,76(4):493-510.
12 DoswellIII C A, BrooksH E ,MaddoxR A. Flash flood forecasting: An ingredients-based methodology[J]. Weather and Forecasting,1996,11:560-581.
13 YuXiaoding. Ingredients based forecasting methodology[J]. Meteorological Monthly,2011,37(8):913-918.
俞小鼎.基于构成要素的预报方法——配料法[J].气象,2011,37(8):913-918.
14 LaiXiaofang. An Ingredients-Based Methodology for Forecasting Rainstorm in the Low Reach of Yangtze River[D]. Nanjing: Nanjing University of Information Science and Technology,2006.
来小芳.“配料法”用于长江下游暴雨预报[D].南京:南京信息工程大学,2006.
15 TangXiaowen, TangJianping, ZhangXiaoling. An ingredient-based operational heavy rain quantitative forecast system[J]. Journal of Nanjing University (Natural Sciences), 2010, 46(3):277-283.
唐晓文,汤剑平,张小玲.基于业务中尺度模式的配料法强降水定量预报[J].南京大学学报:自然科学版,2010,46(3):277-283.
16 ZhengYongguang, ZhouKanghui, ShengJie, et al. Advances in techniques of monitoring, forecasting and warning of severe convective weather[J]. Journal of Applied Meteorological Science,2015,26(6):641-657.
郑永光,周康辉,盛杰,等.强对流天气监测预报预警技术进展[J].应用气象学报,2015,26(6):641-657.
17 ZhangXiaoling, YangBo, ShengJie, et al. Development of operations on forecasting severe convective weather in China[J]. Advances in Meteorological Science and Technology, 2018,8(3):8-18.
张小玲,杨波,盛杰,等.中国强对流天气预报业务发展[J].气象科技进展,2018,8(3):8-18.
18 TianFuyou, ZhengYongguang, ZhangTao, et al. Sensitivity analysis of short-duration heavy rainfall related diagnostic parameters with point-area verification[J]. Journal of Applied Meteorological Science,2015,26(4):385-396.
田付友,郑永光,张涛,等.短时强降水诊断物理量敏感性的点对面检验[J].应用气象学报,2015,26(4):385-396.
19 WangTingting, WangYingchun, ChenMingxuan, et al. The contrastive analysis of dry and moist thunderstorms in Beijing[J]. Meteorological Monthly, 2011,37(2):142-155.
王婷婷,王迎春,陈明轩,等.北京地区干湿雷暴形成机制的对比分析[J].气象,2011,37(2):142-155.
20 ZhengLinlin, SunJianhua. Characteristics of synoptic and surface circulation of mesoscale convective systems in dry and moist environmental conditions[J]. Chinese Journal of Atmospheric Sciences, 2013,37(4):891-904.
郑淋淋,孙建华.干、湿环境下中尺度对流系统发生的环流背景和地面特征分析[J].大气科学,2013,37(4):891-904.
21 FlanaganP X, MelickC J, JirakI L, et al. Definition of dry thunderstorms for use in verifying SPC fire weather products[C]//95th American Meteorological Society Annual Meeting,2015.
22 WaiteM L, SnyderC. Mesoscale energy spectra of moist baroclinic waves[J]. Journal of the Atmospheric Sciences,2013,70(4):1 242-1 256.
23 SunY Q, ZhangF Q. Intrinsic versus practical limits of atmospheric predictability and the significance of the butterfly effect[J]. Journal of the Atmospheric Sciences,2016,73(3):1 419-1 438.
24 WangXiuming,YuXiaoding,ZhuHe. The applicability of NCEP reanalysis data to severe convection environment analysis[J]. Journal of Applied Meteorological Science, 2012,23(2):139-146.
王秀明,俞小鼎,朱禾.NCEP再分析资料在强对流环境分析中的应用[J].应用气象学报,2012,23(2):139-146.
25 BoltonD. The computation of equivalent potential temperature[J].Monthly Weather Review,1980,108(7):1 046-1 053.
26 DingYihui. Diagnostic Analysis Methods in Synoptic Dynamics[M]. Beijing: Science Press,1989:45-47.
丁一汇.天气动力学中的诊断分析方法[M].北京:科学出版社,1989:45-47.
27 LiuJianwen, GuoHu, LiYaodong, et al.The Calculation of Meteorological Variables Used in Analysis and Forecast[M].Beijing: China Meteorological Press,2007:77-82.
刘健文,郭虎,李耀东,等.天气分析预报物理计算基础[M].北京:气象出版社,2007:77-82.
28 FawcettTom. An introduction to ROC analysis[J]. Pattern Recognition Letters,2006, 27(8): 861-874.
29 PanLiujie, ZhangHongfang, WangJianpeng. Progress on verification methods of numerical weather prediction[J]. Advances in Earth Science, 2014,29(3):327-335.
潘留杰,张宏芳,王建鹏.数值天气预报检验方法研究进展[J].地球科学进展,2014,29(3):327-335.
30 FanLimiao, YuXiaoding. Characteristic analyses on environmental parameters in short-term severe convective weather in China[J]. Plateau Meteorology, 2013,32(1):156-165.
樊李苗,俞小鼎.中国短时强对流天气的若干环境参数特征分析[J].高原气象,2013,32(1):156-165.
31 BunkersM J. Vertical wind shear associated with left-moving supercells[J]. Weather and Forecasting,2002,17(4):845-855.
32 ZhengLinlin, SunJianhua. The impact of vertical wind shear on the intensity and organizational mode of mesoscale convective systems using numerical experiments [J]. Chinese Journal of Atmospheric Sciences,2016,40(2):324-340.
郑淋淋,孙建华.风切变对中尺度对流系统强度和组织结构影响的数值试验[J].大气科学, 2016,40(2):324-340.
33 DennisE J, KumjianM R. The impact of vertical wind shear on hail growth in simulated supercells[J]. Journal of Atmospheric Sciences,2017,74(3):641-663.
34 LeiLei,SunJisong, WangGuorong, et al.An experimental study of the summer convective weather categorical probability forecast based on the rapid updated cycle system for the Beijing area (BJ-RUC)[J]. Acta Meteorologica Sinica, 2012,70(4):752-765.
雷蕾,孙继松,王国荣,等.基于中尺度数值模式快速循环系统的强对流分类概率预报试验[J].气象学报,2012,70(4):752-765.
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