设为首页
加入收藏
基于TIGGE资料识别适应性观测敏感区的应用研究
Applied Research on Adaptive Observation for Identifying Sensitive Regions Based upon TIGGE Data
-
摘要: 基于TIGGE (THORPEX Interactive Grand Global Ensemble) 资料，通过对比两类强降水过程，分析了集合变换卡尔曼滤波 (Ensemble Transform Kalman Filter，ETKF) 适应性观测敏感区识别方法在实际应用中的具体环节。试验中使用两种分辨率和不同范围的集合预报资料得到的信号方差空间分布和极大值区基本一致，而在实施计算中使用适当分辨率和范围的集合预报资料能够大大节省计算时间；使用可获得的最近时刻为初值的集合预报资料得到的敏感区识别结果更加可靠；使用不同中心集合预报资料得到的敏感区识别结果有一定差异，但对于比较典型的夏季主雨带降水过程，各中心资料得到的结果较一致，敏感区识别结果比较可靠；不同类型强降水过程的敏感区对选取的度量函数具有一定依赖性。Abstract: Accurate prediction of high impact weather is very important. Adaptive observation has an immediate significance to improve the quality of high impact weather forecast. THORPEX (THe Observing system Research and Predictability EXperiment) is a ten year international atmospheric research program with the primary objective to improve the accuracy of 1 day to 2 week high impact weather forecasts for the benefit of society and economy. THORPEX is developed under four sub programs, and two of them are related to adaptive observation, which are observing systems, data assimilation and observing strategies, so adaptive observation is the foundation and prerequisite to achieve the ultimate goal of THORPEX. Adaptive observation is a type of observation interactive between weather forecast (or weather service) and observation. The key to adaptive observation lies in identifying sensitive regions. Sensitive regions are defined as localized regions from where the analysis errors grow significantly and thereby the forecast skills are degraded. As a new method for identification of sensitive regions which is based on ensemble predictions, ETKF (Ensemble Transform Kalman Filter) has many advantages, such as the program simplicity, independency and so on. On the basis of an ensemble prediction solely, ETKF solves Kalman equation in the space of ensemble, and it can estimate the reductions of prediction error covariance caused by adding observation immediately. The research of ETKF has a proactive effect on both adaptive observation and ensemble predictions. On the basis of TIGGE (THORPEX Interactive Grand Global Ensemble) data, through identification and comparison of the sensitive regions of two different types of heavy precipitation events, specific aspects in the actual application of ETKF method to the adaptive observation are analyzed in detail. The results indicate that the appropriate horizontal resolution and coverage of ensemble data can be used for a reasonable result and shorter computational time, and the reasonable result shows that the geographical distribution of the signal variance maxima calculated from different resolutions and coverages are basically coincident and a relatively small coverage can even be used for the heavy precipitation event under regional weather circulation comparatively to that under macroscale weather circulation; identified sensitive regions are more credible by using ensemble predictions available which are initialized more recently; identified sensitive regions calculated from different meteorological centers' ensemble data are more consistent and reliable for the heavy precipitation event under clear macroscale weather circulation than under regional weather circulation; a calculation of signal variance using nine adjacent grids at one time makes the wide distribution of signal variance maxima, and makes it not conducive to locate sensitive regions more precisely. To the result of sensitive regions, the effects could negligible if the analysis errors of routine observation are changed moderately. Sensitive regions under different circulation are dependent on the selected metric to some extent. In conclusion, the sensitive regions identified by ETKF are understandable and reasonable.
-
图 1 2007年7月16-19日500hPa位势高度场（单位：dagpm）
Fig. 1 500 hPa geopotential height from 16 Jul 2007 to 19 Ju l2007 (unit:dagpm)
图 2 标准化的ETKF信号方差（阴影区）与集合平均的850hPa位势高度（等值线，单位：gpm）（方框内表示验证区）
（a）山东暴雨，集合预报资料分辨率为2.0°×2.0°，（b）山东暴雨，集合预报资料分辨率为1.0°×1.0°，（c）川渝暴雨，集合预报资料分辨率为2.0°×2.0°，（d）川渝暴雨，集合预报资料分辨率为1.0°×1.0°
Fig. 2 Standardized signal variance of ETKF (shadow region) and 850 hPa geopotential height of ensemble average (isoline, unit:gpm)(verification region is denoted by the rectangle)
(a) Shandong Rainstorm, resolution is 2.0°×2.0°, (b) Shandong Rainstorm, resolution is1.0°×1.0°, (c) Rainstorm of Sichuan Basins, resolution is 2.0°×2.0°, (d) Rainstorm of Sichuan Basins, resolution is 1.0°×1.0°
图 3 不同范围ECMWF集合预报资料的标准化ETKF信号方差（阴影区）与850hPa位势高度的分析场（等值线，单位：gpm）（a）山东暴雨，集合预报资料区域范围为北半球，（b）山东暴雨，集合预报资料区域范围为10°~90°N, 70°~150°E, （c）川渝暴雨，集合预报资料区域范围为北半球，（d）川渝暴雨，集合预报资料区域范围为10°~90°N, 70°~150°E
Fig. 3 Standardized signal variance of ETKF (shadow region) using ensemble data of different regions, and 850 hPa geopotential height of ensemble average (isoline, unit:gpm) (a) Shandong Rainstorm, the scope of ensemble data is Northern Hemisphere, (b) Shandong Rainstorm, thescope of ensembledatais10°-90°N, 70°-150°E, (c) Rainstormof Sichuan Basins, the scope of ensemble datais Northern Hemisphere, (d) Rainstorm of Sichuan Basins, the scope of ensemble data is 10°-90°N, 70°-150°E
图 4 不同起始时刻的标准化ETKF信号方差（阴影区）与集合平均的850hPa位势高度（等值线，单位：gpm）（a）山东暴雨，ta为2007年7月18日00:00, ti为2007年7月16日00:00（ta与ti间隔为48h）, （b）山东暴雨，但ta为验证时刻，（c）川渝暴雨，ta为2007年7月17日00:00, ti为2007年7月15日00:00（ta与ti间隔为48h）, （d）川渝暴雨，但ta为验证时刻
Fig. 4 Standardized signal variance of ETKF (shadow region) using ensemble data of different initial time, and 850 hPa geopotential height of ensemble average (isoline, unit:gpm) (a) Shandong Rainstorm, ta is 00:00 18 Jul 2007, ti is 00:00 16 Jul 2007 (time interval is48h), (b) Shandong Rainstorm, butta is verification time, (c) Rainstorm of Sichuan Basins, ta is 00:00 17 Jul 2007, ti is 00:00 15 Jul 2007 (time interval is48h), (d) Rainstorm of Sichuan Basins, butta is verification time
图 5 使用ECMWF, CMA和NCEP集合预报资料得到的山东暴雨标准化ETKF信号方差（阴影区）与各自集合平均的850hPa位势高度（等值线，单位：gpm）（a）ECMWF, ta为2007年7月18日00:00, （b）CMA, ta为2007年7月18日00:00, （c）NCEP, ta为2007年7月18日00:00, （d）ECMWF, ta为验证时刻，（e）CMA, ta为验证时刻，（f）NCEP, ta为验证时刻
Fig. 5 Standardized signal variance (shadow region) of Shandong Rainstorm using ECMWF, CMA and NCEP ensemble prediction with 850 hPa geopotential height of ensemble average (isoline, unit:gpm) (a) ECMWF, ta is 00:00 18 Jul 2007, (b) CMA, ta is 00:00 18 Jul 2007, (c) NCEP, ta is 00:00 18 Jul 2007, (d) ECMWF, ta is verification time, (e) CMA, ta is verification time, (f) NCEP, ta is verification time
图 6 使用ECMWF, CMA和NCEP集合预报资料得到的川渝暴雨标准化ETKF信号方差（阴影区）与各自集合平均的850hPa位势高度（等值线，单位：gpm）（a）ECMWF, ta为2007年7月17日00:00, （b）CMA, ta为2007年7月17日00:00, （c）NCEP, ta为2007年7月17日00:00, （d）ECMWF, ta为即验证时刻，（e）CMA, ta为验证时刻，（f）NCEP, ta为验证时刻
Fig. 6 Standardized signal variance (shadow region) of Rainstorm of Sichuan Basins using ECMWF, CMA and NCEP ensemble pridiction with 850 hPa geopotential height of ensemble average (isoline, unit:gpm) (a) ECMWF, ta is 00:00 17 Jul 2007, (b) CMA, ta is 00:00 17 Jul 2007, (c) NCEP, ta is 00:00 17 Jul 2007, (d) ECMWF, ta is verification time, (e) CMA, ta is verification time, (f) NCEP, ta is verification time
图 7 使用ECMWF（a）, CMA（b）, NCEP（c）集合预报资料得到的山东暴雨标准化ETKF信号方差（阴影区）与各自集合平均的850hPa位势高度（等值线，单位：gpm）（度量函数为扰动动能）
Fig. 7 Standardized signal variance (shadow region) of Shandong Rainstorm using ECMWF (a), CMA (b), NCEP (c) ensemble prediction and 850 hPa geopotential height of ensemble average (isoline, unit:gpm) (the metric of kinetic energy)
图 8 使用ECMWF（a）, CMA（b）, NCEP（c）集合预报资料得到的川渝暴雨标准化ETKF信号方差（阴影区）与各自集合平均的850hPa位势高度（等值线，单位：gpm）（度量函数为扰动动能）
Fig. 8 Standardized signal variance (shadow region) of Rainstorm of Sichuan Basins using ECMWF (a), CMA (b), NCEP (c) ensemble prediction and 850 hPa geopotential height of ensemble average (isoline, unit:gpm)(the metric of kinetic energy)
-
[1] THORPEX中国委员会秘书处.THORPEX国际科学计划.北京:气象出版社, 2005:1-55. [2] Bishop C H, Etherton B J, Majumdar S J.Adaptive sampling with the ensemble transform Kalman filter.Part I:Theoreti-cal aspects.Mon Wea Rev, 2001, 129:420-436. doi: 10.1175/1520-0493(2001)129<0420:ASWTET>2.0.CO;2 [3] Majumdar S J, Bishop C H, Etherton B J, et al.Cananen-semble trans form Kalman filter predict the reduction in fore-cast error variance produced by targeted observations? Q J R Meteorol Soc, 2001, 127:2803-2820. doi: 10.1002/(ISSN)1477-870X [4] Majumdar S J, Bishop C H, Etherton B J, et al.Adaptive sampling with the ensemble transform Kalman filter.Part II: Field program implementation.Mon Wea Rev, 2002, 130: 1357-1369. [5] Majumdar S J, Bishop C H, Buizza R, et al.Acomparison of ensemble transform Kalman filter targeting guidance with ECMWF and NRL total energy singular vector guidance.Q J R Meteorol Soc, 2002, 128:2527-2549. doi: 10.1256/qj.01.214 [6] 田伟红, 庄世宇.适应性观测与集合变换卡尔曼滤波方法介绍.热带气象学报, 2007, 23(2):201-204. http://www.cnki.com.cn/Article/CJFDTOTAL-RDQX200702013.htm [7] 田伟红. 集合变换卡尔曼滤波方法在集合预报和适应性观测中的初步应用. 北京: 中国气象科学研究院, 2008. [8] 薛纪善.新世纪我国数值天气预报的科技创新研究.应用气象学报, 2006, 17(5):602-610. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=200605103&flag=1 [9] 黄丽萍, 伍湘君, 金之雁.GRAPES模式标准初始化方案设计与实现.应用气象学报, 2005, 16(3):374-384. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20050346&flag=1 [10] 伍湘君, 金之雁, 黄丽萍, 等.GRAPES模式软件框架与实现.应用气象学报, 2005, 16(4):539-546. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20050468&flag=1 [11] 陈德辉, 沈学顺.新一代数值预报系统GRAPES研究进展.应用气象学报, 2006, 17(6):773-777. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=200606125&flag=1 [12] 张林, 朱宗申.GRAPES模式切线性垂直扩散方案的误差分析和改进.应用气象学报, 2008, 19(2):194-200. http://qikan.camscma.cn/jams/ch/reader/view_abstract.aspx?file_no=20080235&flag=1 [13] 薛纪善, 陈德辉.数值预报系统GRAPES的科学设计与应用.北京:科学出版社, 2008:1-383. [14] 马旭林.基于集合卡尔曼变换 (ETKF) 理论的适应性观测研究与应用.南京:南京信息工程大学, 2008:1-126. [15] Bishop C H, Toth Z.Ensemble transform and adaptive ob-servations.J Atmos Sci, 1999, 56:1748-1765. doi: 10.1175/1520-0469(1999)056<1748:ETAAO>2.0.CO;2 [16] Young-Youn Park, Roberto Buizza, Martin Leutbecher.TIGGE: Preliminary Results on Comparing and Combining Ensembles.EC-MWF Technical Memoranda, 2008. [17] Bishop C H, Etherton B J, Majumdar S J.Verification region selection and dataassimilation for adaptive sampling.Q J R Meteorol Soc, 2006, 132:915-933. doi: 10.1256/qj.05.48 [18] Reynolds C A, Rosmond T E.Nonlinear growth of singular-vector-based perturbations.Q J R Meteorol Soc, 2003, 129: 3059-3078. doi: 10.1256/qj.02.193 [19] Petersen G N, Majumdar S J, Thorpe A J.The properties of sensitive area predictions based on the ensemble transform Kalman filter (ETKF).Q J R Meteorol Soc, 2007, 133:697-710. doi: 10.1002/(ISSN)1477-870X [20] Palmer T N, Gelaro R, Barkmeijer J, et al.Singular vectors, metrics, and adaptive observations.J Atmos Sci, 1998, 55: 633-653. doi: 10.1175/1520-0469(1998)055<0633:SVMAAO>2.0.CO;2 -
下载:
计量
- 摘要浏览量: 4268
- HTML全文浏览量: 921
- PDF下载量: 1763
- 被引次数: 0
