基于快速更新同化数值预报的小时降水量时间滞后集合订正技术
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摘要
由中小尺度对流系统造成短时强降水天气的发生发展十分迅速,对其落区和时效的预报预警一直都是预报业务中的难点。本文基于快速更新同化的中尺度数值预报系统GRAPES-RAFS 0. 1°×0. 1°分辨率逐小时降水预报,首先通过时间滞后集合预报方法构建了多个集合成员,使用平均TS评分值计算相应预报成员权重系数建立预报方程,然后采用频率匹配订正法进行降水量级订正,从而得到集合订正的逐小时降水量预报。2017年8-9月的逐日试验和典型个例预报结果评估表明效果良好。(1)GRAPES-RAFS最新时次的预报场并不完全代表最好的预报效果,通过时间滞后集合订正方法自动识别优选预报成员,显著提高了预报能力;(2)GRAPES-RAFS预报存在降水量级偏弱的系统性误差,经过频率匹配方法订正后,在量级预报上更接近实况;(3)时间滞后集合预报对我国中东部(包括黄淮、江淮、江南地区)的小时降水量订正效果最好;(4)进一步使用的频率匹配订正方法显著提升了逐时降水量的预报效果,其在降水频率更高、强度更大的江南南部、华南、西南地区效果更为显著;(5)对于中小尺度的强降水过程,经过上述方法订正后,显著提高了模式对强降水系统位置、形态及降水量级的预报水平。
The formation and development of short-term heavy precipitation caused by small and mesoscale convective systems are very rapid, and the prediction and warning of the location and period of precipitation are always difficult in forecasting operation. In recent years, the accuracy and resolution of mesoscale model have been improved, and it plays more and more important role in forecasting and warning of severe convective weather. In this paper, based on hourly precipitation forecast from Rapid Analysis and Forecasting System GRAPES-RAFS(0. 1° X 0. 1°) during August-September 2017, ensemble members are formed by the time-lagged ensemble forecast method. The average TS score is used to calculate weight coefficients of corresponding ensemble members, and then frequency matching method is adopted to correct precipitation forecast bias. The conclusions drawn from this study are as follows.(1) For GRAPESRAFS, the most accurate precipitation forecast does not always come from the most recent ensemble member. Time-lagged ensemble method can significantly improve the prediction ability of the model by automatically identifying the optimal forecast members.(2) The GRAPES-RAFS hourly precipitation forecast presents systematic weak biases. After corrected by the frequency matching method, the hourly precipitation forecast gets more close to the actual situation in magnitude.(3) The time-lagged ensemble method works better for central and eastern parts of China.(4) The frequency matching method works better for south of the Yangtze River, South China and Southwest China, where precipitation occurs more frequently with greater intensities.(5) The method can significantly improve the prediction capacity of the model for the location, amount and patterns of rainfalls in severe precipitation process of small and medium scales.
The formation and development of short-term heavy precipitation caused by small and mesoscale convective systems are very rapid, and the prediction and warning of the location and period of precipitation are always difficult in forecasting operation. In recent years, the accuracy and resolution of mesoscale model have been improved, and it plays more and more important role in forecasting and warning of severe convective weather. In this paper, based on hourly precipitation forecast from Rapid Analysis and Forecasting System GRAPES-RAFS(0. 1° X 0. 1°) during August-September 2017, ensemble members are formed by the time-lagged ensemble forecast method. The average TS score is used to calculate weight coefficients of corresponding ensemble members, and then frequency matching method is adopted to correct precipitation forecast bias. The conclusions drawn from this study are as follows.(1) For GRAPESRAFS, the most accurate precipitation forecast does not always come from the most recent ensemble member. Time-lagged ensemble method can significantly improve the prediction ability of the model by automatically identifying the optimal forecast members.(2) The GRAPES-RAFS hourly precipitation forecast presents systematic weak biases. After corrected by the frequency matching method, the hourly precipitation forecast gets more close to the actual situation in magnitude.(3) The time-lagged ensemble method works better for central and eastern parts of China.(4) The frequency matching method works better for south of the Yangtze River, South China and Southwest China, where precipitation occurs more frequently with greater intensities.(5) The method can significantly improve the prediction capacity of the model for the location, amount and patterns of rainfalls in severe precipitation process of small and medium scales.
引文
陈葆德,王晓峰,李泓,等,2013.快速更新同化预报的关键技术综述[J].气象科技进展,3(2):29-35. Chen B D,Wang X F,Li H,et al,2013. An overview of the key techniques in rapid refresh assimilation and forecast[J]. Adv Meteor Sci Tech,3(2):29-35(in Chinese).
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黄丽萍,陈德辉,邓莲堂,等,2017. GRAPES_Meso V4. 0主要技术改进和预报效果检验[J].应用气象学报,28(1):25-37. Huang L P,Chen D H,Deng L T, et al.2017. Main technical improvements of GRAPES Meso V4. 0 and verification[J]. J Appl Meteor Sci,28(1):25-37(in Chinese).
李佰平,智协飞,2012. ECMWF模式地面气温预报的四种误差订正方法的比较研究[J].气象,38(8):897-902.Li B P,Zhi X F,2012. Comparative study of four correction schemes of the ECMWF surface temperature forecasts[J]. Meteor Mon, 38(8):897-902(in Chinese).
李俊,杜钧,陈超君,2014.降水偏差订正的频率(或面积)匹配方法介绍和分析[J].气象,40(5):580-588. Li J,Du J,Chen C J,2014.Introduction and analysis to frequency or area matching method applied to precipitation forecast bias correction[J]. Meteor Mon,40(5):580-588(in Chinese).
漆梁波,2015.高分辨率数值模式在强对流天气预警中的业务应用进展[J].气象,41(6):661-673.Qi L B,2015. Operational progressof high-resolution numerical model on severe convective weather warning[J]. Meteor Mon,41(6):661-673(in Chinese).
唐文苑,周庆亮,刘鑫华,等,2017.国家级强对流天气分类预报检验分析[J].气象,43(1):67-76. Tang W Y,Zhou Q L,Liu X H,et al,2017. Anlyisis on verification of national severe convective weather categorical forecasts[J]. Meteor Mon,43(1):67-76(in Chinese).
唐文苑,郑永光,张小雯,2018.基于FSS的高分辨率模式华北对流预报能力评估[J].应用气象学报,29(5):513-523. Tang W Y,Zheng Y G,Zhang X W,2018. FSS-based evaluation of convective weather forecasts in North China from high resolution modes[J]. J Appl Meteor Sci,29(5):513-523(in Chinese).
徐枝芳,郝民,朱立娟,等,2013. GRAPES_RAFS系统研发[J].气象,39(4):466-477. Xu Z F,Hao M,Zhu L J,et al,2013. On the research and development of GRAPES_RAFS[J]. Meteor Mon,39(4):466-477(in Chinese).
俞小鼎,周小刚,王秀明,2012.雷暴与强对流临近天气预报技术进展[J].气象学报,70(3):311-337. Yu X D,Zhou X G,Wang X M,2012. The advances in the nowcasting techniques on thunderstorms and severe convection[J]. Acta Meteor Sin, 70(3):311-337(in Chinese).
郑永光,周康辉,盛杰,等,2015.强对流天气监测预报预警技术进展[J].应用气象学报,26(6):641-657. Zheng Y G,Zhou K H,Sheng J,et al,2015. Advances in techniques of monitoring,forecasting and warning of severe convective weather[J]. J Appl Meteor Sci,26(6):641-657(in Chinese).
Bernardet L R,Grasso L D,Nachamkin J E,et al,2000. Simulating convective events using a high-resolution mesoscale model[J]. J Geophys Res,105(D11):14963-14982.
Cartwright T J, Krishnamurti T N,2007. Warm season mesoscale superensemble precipitation forecasts in the southeastern United States[J]. Wea Forecasting,22(4):873-886.
Ebert E E, McBride J L,2000. Verification of precipitation in weather systems:determination of systematic errors[J]. J Hydrol, 239(1/2/3/4):179-202.
Hitchens N M,Brooks H E,2012. Evaluation of the storm prediction center's day 1 convective outlooks[J]. Wea Forecasting,27(6):1580-1585.
Hitchens N M,Brooks H E,2014. Evaluation of the storm prediction Center's convective outlooks from day 3 through day 1[J]. Wea Forecasting,29(5):1134-1142.
Hoffman R N,Kalnay E,1983. Lagged average forecasting,an alternative to Monte Carlo forecasting[J]. Tellus A,35(2):100-118.
Krishnamurti T N, Kishtawal C M, LaRow T E, et al, 1999. Improved weather and seasonal climate forecasts from multimodel superensemble[J]. Science,285(5433):1548-1550.
Lu C G,Yuan H L,Schwartz B E,et al,2007. Short-range numerical weather prediction using time-lagged ensembles[J]. Wea Forecasting, 22(3):580-595.
Mittermaier M P,2007. Improving short-range high-resolution model precipitation forecast skill using time-lagged ensembles[J].Quart J Roy Meteor Soc,133(627):1487-1500.
Roberts N M,Lean H W,2008. Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events[J]. Mon Wea Rev,136(1):78-97.
Sun J Z,Xue M,Wilson J W,et al,2014. Use of NWP for nowcasting convective precipitation:recent progress and challenges[J]. Bull Amer Meteor Soc,95(3):409-426.
Walser A,L(u|¨)thi D,Sch(a|¨)r C,2004. Predictability of precipitation in a cloud-resolving model[J]. Mon Wea Rev, 132(2):560-577.
Wilson J W,Crook N A,Mueller C K,et al,1998. Nowcasting thunderstorms:a status report[J].Bull Amer Meteor Soc, 79(10):2079-2100.
Wilson J W, Feng Y R,Chen M,et al,2010. Nowcasting challenges during the Beijing Olympics:successes,failures,and implications for future nowcasting systems[J]. Wea Forecasting, 25(6):1691-1714.
Yuan H L,Lu C G,McGinley J A,et al,2009. Evaluation of shortrange quantitative precipitation forecasts from a time-lagged multimodel ensemble[J]. Wea Forecasting,24(1):18-38.
Zhi X F,Qi H X,Bai Y Q,et al,2012. A comparison of three kinds of multimodel ensemble forecast techniques based on the TIGGE data[J]. Acta Meteor Sin,26(1):41-51.
Zhu Y J,Luo Y,2015. Precipitation calibration based on the frequency-matching method[J]. Wea Forecasting,30(5):1109-1124.
傅娜,陈葆德,谭燕,等,2013.基于快速更新同化的滞后短时集合预报试验及检验[J].气象,39(10):1247-1256.Fu N, Chen B D,Tan Y,et al, 2013. Time-lag ensemble forecasting experiment and evaluation based on SMB-WARR[J]. Meteor Mon,39(10):1247-1256(in Chinese).
黄丽萍,陈德辉,邓莲堂,等,2017. GRAPES_Meso V4. 0主要技术改进和预报效果检验[J].应用气象学报,28(1):25-37. Huang L P,Chen D H,Deng L T, et al.2017. Main technical improvements of GRAPES Meso V4. 0 and verification[J]. J Appl Meteor Sci,28(1):25-37(in Chinese).
李佰平,智协飞,2012. ECMWF模式地面气温预报的四种误差订正方法的比较研究[J].气象,38(8):897-902.Li B P,Zhi X F,2012. Comparative study of four correction schemes of the ECMWF surface temperature forecasts[J]. Meteor Mon, 38(8):897-902(in Chinese).
李俊,杜钧,陈超君,2014.降水偏差订正的频率(或面积)匹配方法介绍和分析[J].气象,40(5):580-588. Li J,Du J,Chen C J,2014.Introduction and analysis to frequency or area matching method applied to precipitation forecast bias correction[J]. Meteor Mon,40(5):580-588(in Chinese).
漆梁波,2015.高分辨率数值模式在强对流天气预警中的业务应用进展[J].气象,41(6):661-673.Qi L B,2015. Operational progressof high-resolution numerical model on severe convective weather warning[J]. Meteor Mon,41(6):661-673(in Chinese).
唐文苑,周庆亮,刘鑫华,等,2017.国家级强对流天气分类预报检验分析[J].气象,43(1):67-76. Tang W Y,Zhou Q L,Liu X H,et al,2017. Anlyisis on verification of national severe convective weather categorical forecasts[J]. Meteor Mon,43(1):67-76(in Chinese).
唐文苑,郑永光,张小雯,2018.基于FSS的高分辨率模式华北对流预报能力评估[J].应用气象学报,29(5):513-523. Tang W Y,Zheng Y G,Zhang X W,2018. FSS-based evaluation of convective weather forecasts in North China from high resolution modes[J]. J Appl Meteor Sci,29(5):513-523(in Chinese).
徐枝芳,郝民,朱立娟,等,2013. GRAPES_RAFS系统研发[J].气象,39(4):466-477. Xu Z F,Hao M,Zhu L J,et al,2013. On the research and development of GRAPES_RAFS[J]. Meteor Mon,39(4):466-477(in Chinese).
俞小鼎,周小刚,王秀明,2012.雷暴与强对流临近天气预报技术进展[J].气象学报,70(3):311-337. Yu X D,Zhou X G,Wang X M,2012. The advances in the nowcasting techniques on thunderstorms and severe convection[J]. Acta Meteor Sin, 70(3):311-337(in Chinese).
郑永光,周康辉,盛杰,等,2015.强对流天气监测预报预警技术进展[J].应用气象学报,26(6):641-657. Zheng Y G,Zhou K H,Sheng J,et al,2015. Advances in techniques of monitoring,forecasting and warning of severe convective weather[J]. J Appl Meteor Sci,26(6):641-657(in Chinese).
Bernardet L R,Grasso L D,Nachamkin J E,et al,2000. Simulating convective events using a high-resolution mesoscale model[J]. J Geophys Res,105(D11):14963-14982.
Cartwright T J, Krishnamurti T N,2007. Warm season mesoscale superensemble precipitation forecasts in the southeastern United States[J]. Wea Forecasting,22(4):873-886.
Ebert E E, McBride J L,2000. Verification of precipitation in weather systems:determination of systematic errors[J]. J Hydrol, 239(1/2/3/4):179-202.
Hitchens N M,Brooks H E,2012. Evaluation of the storm prediction center's day 1 convective outlooks[J]. Wea Forecasting,27(6):1580-1585.
Hitchens N M,Brooks H E,2014. Evaluation of the storm prediction Center's convective outlooks from day 3 through day 1[J]. Wea Forecasting,29(5):1134-1142.
Hoffman R N,Kalnay E,1983. Lagged average forecasting,an alternative to Monte Carlo forecasting[J]. Tellus A,35(2):100-118.
Krishnamurti T N, Kishtawal C M, LaRow T E, et al, 1999. Improved weather and seasonal climate forecasts from multimodel superensemble[J]. Science,285(5433):1548-1550.
Lu C G,Yuan H L,Schwartz B E,et al,2007. Short-range numerical weather prediction using time-lagged ensembles[J]. Wea Forecasting, 22(3):580-595.
Mittermaier M P,2007. Improving short-range high-resolution model precipitation forecast skill using time-lagged ensembles[J].Quart J Roy Meteor Soc,133(627):1487-1500.
Roberts N M,Lean H W,2008. Scale-selective verification of rainfall accumulations from high-resolution forecasts of convective events[J]. Mon Wea Rev,136(1):78-97.
Sun J Z,Xue M,Wilson J W,et al,2014. Use of NWP for nowcasting convective precipitation:recent progress and challenges[J]. Bull Amer Meteor Soc,95(3):409-426.
Walser A,L(u|¨)thi D,Sch(a|¨)r C,2004. Predictability of precipitation in a cloud-resolving model[J]. Mon Wea Rev, 132(2):560-577.
Wilson J W,Crook N A,Mueller C K,et al,1998. Nowcasting thunderstorms:a status report[J].Bull Amer Meteor Soc, 79(10):2079-2100.
Wilson J W, Feng Y R,Chen M,et al,2010. Nowcasting challenges during the Beijing Olympics:successes,failures,and implications for future nowcasting systems[J]. Wea Forecasting, 25(6):1691-1714.
Yuan H L,Lu C G,McGinley J A,et al,2009. Evaluation of shortrange quantitative precipitation forecasts from a time-lagged multimodel ensemble[J]. Wea Forecasting,24(1):18-38.
Zhi X F,Qi H X,Bai Y Q,et al,2012. A comparison of three kinds of multimodel ensemble forecast techniques based on the TIGGE data[J]. Acta Meteor Sin,26(1):41-51.
Zhu Y J,Luo Y,2015. Precipitation calibration based on the frequency-matching method[J]. Wea Forecasting,30(5):1109-1124.