基于动力相似方法的台风极端降水概率预报研究
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摘要
采用动力与统计相结合的方法,对台风引起的极端降水事件进行概率预报方法的研究,回算并评估了25个在2012—2015年间登陆中国的台风。此外,还对该预报方法的相似成员数和有效概率阈值进行了敏感性试验。试验结果显示,该台风极端降水概率预报方法具有一定的预报技巧,能准确预报多数产生极端降水的站点,但预报范围略偏大,空报率较高。相似成员数的敏感性试验表明,100%的相似成员数比率具有更优的预报效果;在相似成员数比率为100%的基础上,24 h预报的有效概率阈值在0%～2%之间的预报效果最好,48 h的有效概率阈值在0%～1.5%之间的预报效果最好。最后对"苏迪罗"和"海鸥"两个台风进行个例预报试验,结果表明对不同个例,预报效果存在较大差异。
This paper uses a combination of dynamic similitude methods to study the method of probabilistic prediction of extreme precipitation events caused by tropical cyclones, and evaluates 25 tropical cyclones which landed in China from 2012 to 2015. In addition, we conduct two sensitivity experiments on optimal similar number of members and effective probability threshold. Experiment results show that the typhoon extreme precipitation probability forecasting method has certain forecasting skills, and can accurately predict many sites occurring extreme precipitation, but the forecast range is larger than the actual situation, and the FAR is higher. The sensitivity experiment of the optimal similar number of members shows that the ratio of similar members with 100% has a better forecasting effect. Based on the optimal similarity of 100%, the effective probability threshold has a better forecasting effect between 0% and 2% of24 h and between 0% and 1.5% of 48 h. Finally, a case prediction experiment is carried out for two typhoons, i.e., Soudelor and Kalmaegi. The results show that there are significant differences in the forecast results of different cases.
This paper uses a combination of dynamic similitude methods to study the method of probabilistic prediction of extreme precipitation events caused by tropical cyclones, and evaluates 25 tropical cyclones which landed in China from 2012 to 2015. In addition, we conduct two sensitivity experiments on optimal similar number of members and effective probability threshold. Experiment results show that the typhoon extreme precipitation probability forecasting method has certain forecasting skills, and can accurately predict many sites occurring extreme precipitation, but the forecast range is larger than the actual situation, and the FAR is higher. The sensitivity experiment of the optimal similar number of members shows that the ratio of similar members with 100% has a better forecasting effect. Based on the optimal similarity of 100%, the effective probability threshold has a better forecasting effect between 0% and 2% of24 h and between 0% and 1.5% of 48 h. Finally, a case prediction experiment is carried out for two typhoons, i.e., Soudelor and Kalmaegi. The results show that there are significant differences in the forecast results of different cases.
引文
[1]钱奇峰,张长安,高拴柱,等.台风路径集合预报的实时订正技术研究[J].热带气象学报, 2014, 30(5):905-910.
[2]雷小途,余晖. WMO登陆台风预报示范项目研究进展[J].气象科技进展, 2015(2):18-23.
[3] LALAURETTE F. Early detection of abnormal weather conditions using a probabilistic extreme forecast index[J]. Quart J Royal Meteor Soc, 2003, 129(594):3 037-3 057.
[4]江志红,陈威霖,宋洁,等. 7个IPCC AR4模式对中国地区极端降水指数模拟能力的评估及其未来情景预估[J].大气科学, 2009,33(1):109-120.
[5]刘琳,陈静,程龙,等.基于集合预报的中国极端强降水预报方法研究[J].气象学报, 2013, 71(5):853-866.
[6]陈朝平,冯汉中,陈静.基于贝叶斯方法的四川暴雨集合概率预报产品释用[C]//全国重大天气过程总结和预报经验交流会. 2010.
[7]张宇彤,矫梅燕,陈静,等.基于贝叶斯方法的极端降水概率预报试验[J].气象, 2016, 42(7):799-808.
[8]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[J].气象, 2013, 39(1):1-10.
[9]赵琳娜,梁莉,王成鑫,等.基于贝叶斯模型平均的集合降水预报偏差订正[C].中国气象学会.第28届中国气象学会年会———S3天气预报灾害天气研究与预报.中国气象学会, 2011:14.
[10]郯俊岭,江志红,马婷婷.基于贝叶斯模型的中国未来气温变化预估及不确定性分析[J].气象学报, 2016, 14(4):583-597.
[11]段小刚,曹伟华.贝叶斯模型平均及其在北京降雨预报中的一个应用[J].北京师范大学学报(自然科学版), 2016, 52(2):134-138.
[12] RAFTERY A E, GNEITING T, BALABDAOUI F, et al. Using Bayesian Model averaging to calibrate forecast ensembles[J]. Mon Wea Rev, 2005, 133(5):1 155-1 174.
[13]董全,金荣花,代刊,等. ECMWF集合预报和确定性预报对淮河流域暴雨预报的对比分析[J].气象, 2016, 42(9):1 146-1 153.
[14]丑纪范.为什么要动力-统计相结合?——兼论如何结合[J].高原气象, 1986, 5(4):367-372.
[15]任宏利,丑纪范.统计-动力相结合的相似误差订正法[J].气象学报, 2005, 63(6):988-993.
[16]曹晓岗,王田民,戴熙敏,等.区域暴雨预报诊断模式的建立[J].江西气象科技, 1995, 17(2):15-19.
[17]钟元,余晖,滕卫平,等.热带气旋定量降水预报的动力相似方案[J].应用气象学报, 2009, 20(1):17-27.
[18] YING M, ZHANG W, YU H, et al. An overview of the China Meteorological Administration Tropical Cyclone Database[J]. Journal of Atmospheric&Oceanic Technology, 2014, 31(2):287-301.
[19] HOUGHTON J E T, DING Y H, GRIGGS J, et al. IPCC 2001. Climate change 2001:the scientific basis[M]. Climate Change 2001:The Scientific Basis, 2001:227-239.
[20]王晓.“苏迪罗”台风极端降水的数值模拟及初始场不确定性的影响研究[D].中国气象科学研究院, 2016.
[21]翟盘茂,潘晓华.中国北方近50年温度和降水极端事件变化[J].地理学报, 2003, 58(z1):1-10.
[22]夏侯杰,朱伟军,任福民.两广地区热带气旋极端降水特征及成因初探[J].热带气象学报, 2017, 33(4):510-518.
[23]孙兴池,陈金敏,刁秀产,等.一次远距离台风暴雨过程分析[J].气象, 2009, 35(5):34-41.
[24]李媛,张泽铭,高广强,等.台风环流对远距离暴雨水汽输送影响研究[J].海洋气象学报, 2017,37(1):111-117.
[25] KITADE T. Numerical experiments of tropical cyclones on a plane with variable Coriolis parameter[J]. J Meteor Soc Jap, 1980, 58(4):471-488.
[26] ANTHES R A, HOKE J E. The effect of horizontal divegence and latitudinal variation of the Coriolis parameter on the drift of a model hurricane[J]. Mon Wea Rev, 1975, 103(5):757-763.
[27]董美莹,陈联寿,李英,等.登陆热带气旋降水增幅的合成诊断分析[J].气象学报, 2011, 69(6):964-977.
[28]赖珍权,翟丽萍,古文保. 1415号台风“海鸥”的卫星云图及雷达资料分析[J].气象研究与应用, 2017,38(2):10-13.
[29]狄靖月,赵琳娜,张国平,等.降水集合预报集成方法研究[J].气象, 2013, 39(6):691-698.
[30]叶龙彬,谌芸,李晟祺,等. 1513号台风“苏迪罗”不同阶段降水的中尺度特征分析[J].热带气象学报,2018,34(3):371-382.
[2]雷小途,余晖. WMO登陆台风预报示范项目研究进展[J].气象科技进展, 2015(2):18-23.
[3] LALAURETTE F. Early detection of abnormal weather conditions using a probabilistic extreme forecast index[J]. Quart J Royal Meteor Soc, 2003, 129(594):3 037-3 057.
[4]江志红,陈威霖,宋洁,等. 7个IPCC AR4模式对中国地区极端降水指数模拟能力的评估及其未来情景预估[J].大气科学, 2009,33(1):109-120.
[5]刘琳,陈静,程龙,等.基于集合预报的中国极端强降水预报方法研究[J].气象学报, 2013, 71(5):853-866.
[6]陈朝平,冯汉中,陈静.基于贝叶斯方法的四川暴雨集合概率预报产品释用[C]//全国重大天气过程总结和预报经验交流会. 2010.
[7]张宇彤,矫梅燕,陈静,等.基于贝叶斯方法的极端降水概率预报试验[J].气象, 2016, 42(7):799-808.
[8]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[J].气象, 2013, 39(1):1-10.
[9]赵琳娜,梁莉,王成鑫,等.基于贝叶斯模型平均的集合降水预报偏差订正[C].中国气象学会.第28届中国气象学会年会———S3天气预报灾害天气研究与预报.中国气象学会, 2011:14.
[10]郯俊岭,江志红,马婷婷.基于贝叶斯模型的中国未来气温变化预估及不确定性分析[J].气象学报, 2016, 14(4):583-597.
[11]段小刚,曹伟华.贝叶斯模型平均及其在北京降雨预报中的一个应用[J].北京师范大学学报(自然科学版), 2016, 52(2):134-138.
[12] RAFTERY A E, GNEITING T, BALABDAOUI F, et al. Using Bayesian Model averaging to calibrate forecast ensembles[J]. Mon Wea Rev, 2005, 133(5):1 155-1 174.
[13]董全,金荣花,代刊,等. ECMWF集合预报和确定性预报对淮河流域暴雨预报的对比分析[J].气象, 2016, 42(9):1 146-1 153.
[14]丑纪范.为什么要动力-统计相结合?——兼论如何结合[J].高原气象, 1986, 5(4):367-372.
[15]任宏利,丑纪范.统计-动力相结合的相似误差订正法[J].气象学报, 2005, 63(6):988-993.
[16]曹晓岗,王田民,戴熙敏,等.区域暴雨预报诊断模式的建立[J].江西气象科技, 1995, 17(2):15-19.
[17]钟元,余晖,滕卫平,等.热带气旋定量降水预报的动力相似方案[J].应用气象学报, 2009, 20(1):17-27.
[18] YING M, ZHANG W, YU H, et al. An overview of the China Meteorological Administration Tropical Cyclone Database[J]. Journal of Atmospheric&Oceanic Technology, 2014, 31(2):287-301.
[19] HOUGHTON J E T, DING Y H, GRIGGS J, et al. IPCC 2001. Climate change 2001:the scientific basis[M]. Climate Change 2001:The Scientific Basis, 2001:227-239.
[20]王晓.“苏迪罗”台风极端降水的数值模拟及初始场不确定性的影响研究[D].中国气象科学研究院, 2016.
[21]翟盘茂,潘晓华.中国北方近50年温度和降水极端事件变化[J].地理学报, 2003, 58(z1):1-10.
[22]夏侯杰,朱伟军,任福民.两广地区热带气旋极端降水特征及成因初探[J].热带气象学报, 2017, 33(4):510-518.
[23]孙兴池,陈金敏,刁秀产,等.一次远距离台风暴雨过程分析[J].气象, 2009, 35(5):34-41.
[24]李媛,张泽铭,高广强,等.台风环流对远距离暴雨水汽输送影响研究[J].海洋气象学报, 2017,37(1):111-117.
[25] KITADE T. Numerical experiments of tropical cyclones on a plane with variable Coriolis parameter[J]. J Meteor Soc Jap, 1980, 58(4):471-488.
[26] ANTHES R A, HOKE J E. The effect of horizontal divegence and latitudinal variation of the Coriolis parameter on the drift of a model hurricane[J]. Mon Wea Rev, 1975, 103(5):757-763.
[27]董美莹,陈联寿,李英,等.登陆热带气旋降水增幅的合成诊断分析[J].气象学报, 2011, 69(6):964-977.
[28]赖珍权,翟丽萍,古文保. 1415号台风“海鸥”的卫星云图及雷达资料分析[J].气象研究与应用, 2017,38(2):10-13.
[29]狄靖月,赵琳娜,张国平,等.降水集合预报集成方法研究[J].气象, 2013, 39(6):691-698.
[30]叶龙彬,谌芸,李晟祺,等. 1513号台风“苏迪罗”不同阶段降水的中尺度特征分析[J].热带气象学报,2018,34(3):371-382.