多普勒天气雷达资料分析及同化在暴雨中尺度天气系统数值模拟中的应用研究
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摘要
梅雨锋暴雨是发生我国江淮流域和长江中下游地区春末夏初的重要天气现象。近年来的许多研究成果表明:梅雨锋暴雨过程是由活动在其上的中尺度天气系统引发的,中尺度天气系统是由许多积云尺度的系统构成。中尺度系统是在天气尺度或中间尺度(中—α尺度)系统的背景下产生的。只要有利的天气尺度条件不变,这种中尺度天气系统就可以反复地产生,从而造成较持久的降水过程,形成暴雨。
本文在全面分析了国内外梅雨锋暴雨和中尺度天气系统研究的基础上,根据我国目前正在开展的新一代天气雷达网和地面雨量站网的建设状况,以及中尺度数值模式和变分同化技术的发展状况,提出了利用江淮流域和长江中下游的多部新一代天气雷达组成的雷达网的信息和雨量信息并利用国内外成熟的先进算法进行雷达与雨量计联合定量估测降水和雷达风廓线的反演信息,并利用四维变分同化技术与中尺度数值模式MM5相结合,开展江淮流域的梅雨锋暴雨过程机理研究。对2003年6月26日12:00—27日12:00(UTC)和2003年7月8日12:00—7月9日12:00(UTC)两次梅雨锋暴雨过程进行了中尺度数值模拟,通过同化试验与控制试验的对比和分析得到如下结论:
(1) 造成上述两次暴雨过程的中尺度天气系统,主要是沿梅雨锋移动的中—β尺度的气旋,局部的对流性降水主要与其上的中—γ尺度涡旋有关。
(2) 利用我国新一代天气雷达信息和地面雨量计信息,采用目前国内外的先进的、成熟的算法开展雷达与地面雨量计联合估测降水,在国内首次利用多部新一代天气雷达组网资料开展大范围的定量估测降水工作。多部新一代天气雷达组成的定量估测降水信息,即能够反映出中尺度天气系统特征,又能够反映大尺度天气系统的结构特征。多部新一代天气雷达组网获得的定量估测降水资料是四维变分同化的理想信息。
Meiyu Front Heavy Rain Processes are important events which happen in late spring and early summer at the area of Yangdzi and Huaihe river. Recently many research demonstrate that these rainstorms are trigged by mesoscale systems which are combined by many cumulus-scale cloud subsystems. The mesoscale systems develop under meso-a or larger scale weather systems and may produce long-lasting precipitations if suitable conditions keep steady.Based on summarizing the research work on Meiyu rainstorms and mesoscale synoptic systems recently, this paper presents a method to research Meiyu rainstorms in valley of Yangdzi and Huaihe rivers with 4D variant assimilating technique by inputting estimate precipitation field which is received from CINRAD network and rain gauge network into numeric model MM5. Some results have been reached by comparison two Meiyu rainstorm cases of 2003.6.26 12:00-2003.6.27 12:00(UTC) and 2003.7.8 12:00-2003.7.9 12:00.(1) It is found that the severe storms are mainly happened in meso-p scale cyclone on Meiyu front and local convective precipitation are relative to meso-γ cyclone based on the analysis of observations and model results that use conventional weather station data as the input of numeric model MM5 from these two cases.(2) It is the first time to estimate large scale precipitation by use of multi CINRAD network radars and all available and proven radar-raingauge techniques. The precipitation field derived from radar
network can describe not only the structure of large scale synoptic system, but the features of mesoscale weather system with higher temporal and spacial resolution.(3) Precipitation estimation based on radar raingauge techniques can get rainfall field with relatively steady error especially when aggregated different results from such as average adjustment; combined Kalman filtering and optimum interpolation and combined kalman filtering and calculus of variation techniques. This could be applied to 4DVAR assimilation so as to reconstruct reliable and steady precipitation data in higher temporal and spacial resolution. Evaluation from the above two cases demonstrate that among above three methods of radar measment precipitation techniques, both combined kalman filtering and optimum interpolation and combined kalman filtering and calculus of variation are better than average adjustment, combined kalman filtering and optimum interpolation is further better than combined kalman filtering and calculus of variation.(4) Wind profile can be retrieved from single Doppler radar under the linear field assumed, which is basically quasi-geostrophic that shows the feature of large scale motion. The wind profile data from multi Doppler radars are then interpolated into model grids hourly by use of MM5 objective software LITTLE_R to adjust the whole wind field. Results show that wind profiles derived from weather radar have high resolution in time and in vertical direction and are able to assimilate meso-scale model from local feature. This may change the structure of atmospheric stability and cause the distribution of relative factors more reliable. The wind profile derived from CINRAD radar shows the property of wind changing in boundary layer even the low level southwest jet and plays an important role in simulating vapor transmission in meso-p cyclone after assimilating it into the model. This promoted the ability of simulating mesoscale weather
system in boundary layer especially for southwest jet. On the other hand, the numeric model simulates the storm more clear after using radar wind profile data and the gravity wave is excitated which is one of the most important factor for severe storm forming.(5) This paper focus on the 4DVAR used high temporal and spacial resolution data of radar quantitative estimate of rainfall. Results are as follow based on the two cases of Meiyu front storm:4DVAR assimilation produces a model harmonious initial precipitation in physics from radar estimated precipitation, increases initial cloud water and forms precipitation at early steps that
本文在全面分析了国内外梅雨锋暴雨和中尺度天气系统研究的基础上,根据我国目前正在开展的新一代天气雷达网和地面雨量站网的建设状况,以及中尺度数值模式和变分同化技术的发展状况,提出了利用江淮流域和长江中下游的多部新一代天气雷达组成的雷达网的信息和雨量信息并利用国内外成熟的先进算法进行雷达与雨量计联合定量估测降水和雷达风廓线的反演信息,并利用四维变分同化技术与中尺度数值模式MM5相结合,开展江淮流域的梅雨锋暴雨过程机理研究。对2003年6月26日12:00—27日12:00(UTC)和2003年7月8日12:00—7月9日12:00(UTC)两次梅雨锋暴雨过程进行了中尺度数值模拟,通过同化试验与控制试验的对比和分析得到如下结论:
(1) 造成上述两次暴雨过程的中尺度天气系统,主要是沿梅雨锋移动的中—β尺度的气旋,局部的对流性降水主要与其上的中—γ尺度涡旋有关。
(2) 利用我国新一代天气雷达信息和地面雨量计信息,采用目前国内外的先进的、成熟的算法开展雷达与地面雨量计联合估测降水,在国内首次利用多部新一代天气雷达组网资料开展大范围的定量估测降水工作。多部新一代天气雷达组成的定量估测降水信息,即能够反映出中尺度天气系统特征,又能够反映大尺度天气系统的结构特征。多部新一代天气雷达组网获得的定量估测降水资料是四维变分同化的理想信息。
Meiyu Front Heavy Rain Processes are important events which happen in late spring and early summer at the area of Yangdzi and Huaihe river. Recently many research demonstrate that these rainstorms are trigged by mesoscale systems which are combined by many cumulus-scale cloud subsystems. The mesoscale systems develop under meso-a or larger scale weather systems and may produce long-lasting precipitations if suitable conditions keep steady.Based on summarizing the research work on Meiyu rainstorms and mesoscale synoptic systems recently, this paper presents a method to research Meiyu rainstorms in valley of Yangdzi and Huaihe rivers with 4D variant assimilating technique by inputting estimate precipitation field which is received from CINRAD network and rain gauge network into numeric model MM5. Some results have been reached by comparison two Meiyu rainstorm cases of 2003.6.26 12:00-2003.6.27 12:00(UTC) and 2003.7.8 12:00-2003.7.9 12:00.(1) It is found that the severe storms are mainly happened in meso-p scale cyclone on Meiyu front and local convective precipitation are relative to meso-γ cyclone based on the analysis of observations and model results that use conventional weather station data as the input of numeric model MM5 from these two cases.(2) It is the first time to estimate large scale precipitation by use of multi CINRAD network radars and all available and proven radar-raingauge techniques. The precipitation field derived from radar
network can describe not only the structure of large scale synoptic system, but the features of mesoscale weather system with higher temporal and spacial resolution.(3) Precipitation estimation based on radar raingauge techniques can get rainfall field with relatively steady error especially when aggregated different results from such as average adjustment; combined Kalman filtering and optimum interpolation and combined kalman filtering and calculus of variation techniques. This could be applied to 4DVAR assimilation so as to reconstruct reliable and steady precipitation data in higher temporal and spacial resolution. Evaluation from the above two cases demonstrate that among above three methods of radar measment precipitation techniques, both combined kalman filtering and optimum interpolation and combined kalman filtering and calculus of variation are better than average adjustment, combined kalman filtering and optimum interpolation is further better than combined kalman filtering and calculus of variation.(4) Wind profile can be retrieved from single Doppler radar under the linear field assumed, which is basically quasi-geostrophic that shows the feature of large scale motion. The wind profile data from multi Doppler radars are then interpolated into model grids hourly by use of MM5 objective software LITTLE_R to adjust the whole wind field. Results show that wind profiles derived from weather radar have high resolution in time and in vertical direction and are able to assimilate meso-scale model from local feature. This may change the structure of atmospheric stability and cause the distribution of relative factors more reliable. The wind profile derived from CINRAD radar shows the property of wind changing in boundary layer even the low level southwest jet and plays an important role in simulating vapor transmission in meso-p cyclone after assimilating it into the model. This promoted the ability of simulating mesoscale weather
system in boundary layer especially for southwest jet. On the other hand, the numeric model simulates the storm more clear after using radar wind profile data and the gravity wave is excitated which is one of the most important factor for severe storm forming.(5) This paper focus on the 4DVAR used high temporal and spacial resolution data of radar quantitative estimate of rainfall. Results are as follow based on the two cases of Meiyu front storm:4DVAR assimilation produces a model harmonious initial precipitation in physics from radar estimated precipitation, increases initial cloud water and forms precipitation at early steps that
引文
[1] 陶诗言,中国之暴雨,科学出版社,1978年5月
[2] Clifford F. Mass & David P, Dempsey, A One-level, Mesoscale Model For Diagnosing Surface Winds in Mountainous and Coastal Regions, Monthly Weather Review, February 1985
[3] 张丙辰,长江中下游梅雨锋暴雨的研究,气象出版社,1990
[4] 王鹏云,中尺度雨带和对称不稳定,气象,1987年第3期
[5] 励申申等,登陆台风与其外围暴雨的相互作用,南京气象学院学报
[6] 陆汉城、沈新勇、余志豪,梅雨锋内Wave-CISK条件对称不稳定——梅雨锋内多雨带生成的可能机制,教学与研究,空军气象学院,(4),1985。
[7] Kerry A. Emanuel, On Assessing Local Conditional Symmetric Instability From Atomspheric Soundings, Monthly Weather Review, July 1983
[8] Clifford Mass, The Topographically Forced Diurnal Circulations OF Western Washington State and Their Influence On Precipitation, Monthly Weather Review, December 1981
[9] Richard A. Anthes,美国宾州大学和国家大气研究中心中尺度模式应用近况,暴雨研究和试验译文选P333-P358,海洋出版社,1991
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[17] Wang Z, K K Droegemeier, L White et al. Application of a new adjoint Newton algorithm to the 3D ARPS storm-scale model using simulated data. Mon Wea Rev, 1997, 125: 2460～2478
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[20] Zupanski M. Regional four-dimensional variational data assimilation in a quasi-operational forecasting environment. Mon Wea Rev, 1993, 121: 2396～2408
[21] Zupanski D. The effects of discontinuities in the Betts-Miler cumulus convection scheme on four-dimensional variational data assimilation. Tellus, 1993, 45A: 511～524
[22] Parrish D F, J C D Derber, J Purser et al. The NMC global analysis systems recent improvements and future plans. J Meteor Soc Japan, 1996, 75: 18359～18365
[23] 张昕,刘月巍,王斌 季仲贞,987特大暴雨降水资料的四维变分同化研究,自然科学进展
[24] Zhang D.-L, K. Gao and D. B. Parsons, 1989: Numerical simulation of an intense squall line during 10-11 June 1985 PRE-STORM. Part Ⅰ: Model verification. Mon. Wea. Rev., 117, 960-994.
[25] Stensrud D. J. and J. M. Fritsch, 1994: Mesoscale convective systems in weakly forced large-scale environment. Part II: Generation of a mesoscale initial condition. Mon. Wea. Rev., 122, 2068-2083.
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[27] Seminar 1980, Data Assimilation Methods, Reading, United Kingdom, ECMWF, 195-217.
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[30] Krishnamurti T. N. and H. S. Bedi, 1988: Cumulus parameterization and rainfall rates. Mon. Wea. Rev., 116, 583-599.
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[33] Le Dimet, F. X. and 0. Talagrand, 1986: Variational algorithms for analysis and assimilation of meteorological observations: Theoretical aspects. Tellus, 38A, 97-110.
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[35] Zupanski D. and F. Mesinger, 1995: Four-dimensional variational assimilation of precipitation data. Mon. Wea. Rev., 123, 1112-1127.
[36] Kuo Y.-H, Zou X. and Guo Y.-R., 1995: Variational assimilation of precipitable water using a nonhydrostatic mesoscale adjoint model. Part Ⅰ: Moisture retrieval and sensitivity experiments, Mon. Wea. Rev., 124, 122-147.
[37] Kuo Y.-H, Y.-R. Guo and E. R. Westwater, 1993: Assimilation of precipitable water measurements into a mesoscale numerical model. Mon. Wea. Rev., 121, 1215-1238.
[38] Zou X., F. Vandenberhe, B. Wang, M. E. Gorbunov, Y. -H. Kuo, S. Sokolovskiy, J. C. Chang, J. G. Sela and R. A. Anthes, 1999: A ray-racing operator and its adjoint for the use of GPS/MET refraction angle measurements, J. Geophys. Res., 104: 22301-22318.
[39] Zou X., Kuo Y.-H. and Guo Y.-R., 1995: Assimilation of atmospheric radio refractivity using a nonhydrostatic adjoint model, Mon. Wea. Rev., 123, 2229-2249.
[40] Zou X. and Y.-H. Kuo, 1996: Rainfall assimilation through an optimal control of initial and boundary conditions in a limited-area mesoscale model. Mon. Wea. -Rev., 124, 2859-2882.
[41] Kuo Y. -H, X. Zou and Y.-R. Guo, 1996: Variational assimilation of precipitable water using a nonhydrostatic mesoscale adjoint model. Part Ⅰ: Moisture retrieval and sensitivity experiments. Mon. Wea. Rev., 124, 1409-1434
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[46] 云迹风资料同化对暴雨预报影响的数值模拟,周兵,徐海明,吴国雄,何金海,《气象学报》2002年6月第60卷第3期
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[48] Emanuel,K.,and Co-authors, 1995:Report of the First Prospectus Development Team of the U. S. Weather Research Program to NOAA and the NFS, Bull. Amer. Met. Soc., 76, 1194-1208.
[2] Clifford F. Mass & David P, Dempsey, A One-level, Mesoscale Model For Diagnosing Surface Winds in Mountainous and Coastal Regions, Monthly Weather Review, February 1985
[3] 张丙辰,长江中下游梅雨锋暴雨的研究,气象出版社,1990
[4] 王鹏云,中尺度雨带和对称不稳定,气象,1987年第3期
[5] 励申申等,登陆台风与其外围暴雨的相互作用,南京气象学院学报
[6] 陆汉城、沈新勇、余志豪,梅雨锋内Wave-CISK条件对称不稳定——梅雨锋内多雨带生成的可能机制,教学与研究,空军气象学院,(4),1985。
[7] Kerry A. Emanuel, On Assessing Local Conditional Symmetric Instability From Atomspheric Soundings, Monthly Weather Review, July 1983
[8] Clifford Mass, The Topographically Forced Diurnal Circulations OF Western Washington State and Their Influence On Precipitation, Monthly Weather Review, December 1981
[9] Richard A. Anthes,美国宾州大学和国家大气研究中心中尺度模式应用近况,暴雨研究和试验译文选P333-P358,海洋出版社,1991
[10] Anthes R. A. , A Cumulus Parameterization Scheme Utilizing A One-Dimensional Cloud Model, Mon. Wea. Rev. 105, 270-287
[11] Fritsch J. M. and Chappell C. F. , Numerical Prediction of Convectively Driven Mesoscale Pressure Systems, Part Ⅰ : Convective Parameterization, J. Atoms. Sci. , 37, 1722-1733
[12] Fritsch J. M. and Chappell C. F., Numerical Prediction of Convectively Driven Mesoscale Pressure Systems, Part Ⅱ : Mesoscale Model. J. Atoms. Sci. , 37, 1734-1762
[13] 雷达和卫星资料在中尺度模式中的初步应用,徐枝芳
[14] Lindskog, M. et al. 2002: Development of Doppler radar wind data assimilation for the HIRLAM 3D-VAR. HIRLAM Technical Report 52, HIRLAM-5 Project.
[15] 中尺度大气数值模式发展现状和应用前景,程麟生
[16] Grell G A, J Dudhia, D R Stauffer. A Description of the Fifth-Generation Penn State/NCAR Mesoscale Model (MM5). NCAR Technical Note, NCAR/TN-398+STR, 1994. 138
[17] Wang Z, K K Droegemeier, L White et al. Application of a new adjoint Newton algorithm to the 3D ARPS storm-scale model using simulated data. Mon Wea Rev, 1997, 125: 2460～2478
[18] Errico R M, K Raeder, T Vukicevic. Mesoscale adjoint modeling system version 1. NCAR Tech Note, NCAR/TN-41+IA, 1994
[19] Zou X, Y H Kuo, Y R Guo. Assimulation of atmospheric radio refractivity using a nonhydrostatic adjoint model. Mon Wea Rev, 1995, 123: 2229～2249
[20] Zupanski M. Regional four-dimensional variational data assimilation in a quasi-operational forecasting environment. Mon Wea Rev, 1993, 121: 2396～2408
[21] Zupanski D. The effects of discontinuities in the Betts-Miler cumulus convection scheme on four-dimensional variational data assimilation. Tellus, 1993, 45A: 511～524
[22] Parrish D F, J C D Derber, J Purser et al. The NMC global analysis systems recent improvements and future plans. J Meteor Soc Japan, 1996, 75: 18359～18365
[23] 张昕,刘月巍,王斌 季仲贞,987特大暴雨降水资料的四维变分同化研究,自然科学进展
[24] Zhang D.-L, K. Gao and D. B. Parsons, 1989: Numerical simulation of an intense squall line during 10-11 June 1985 PRE-STORM. Part Ⅰ: Model verification. Mon. Wea. Rev., 117, 960-994.
[25] Stensrud D. J. and J. M. Fritsch, 1994: Mesoscale convective systems in weakly forced large-scale environment. Part II: Generation of a mesoscale initial condition. Mon. Wea. Rev., 122, 2068-2083.
[26] Stensrud D. J. and J. M. Fritsch, 1994: Mesoscale convective systems in weakly forced large-scale environment. Part III: Numerical simulations and implications for operational forecasting. Mon. Wea. Rev., 122, 2084-2104.
[27] Seminar 1980, Data Assimilation Methods, Reading, United Kingdom, ECMWF, 195-217.
[28] Fiorino M. and T. T. Warner, 1981: Incorporating surface winds and rainfall rates into the initialization of a mesoscale hurricane model. Mon. Wea. Rev., 109, 1914-1929.
[29] Krishnamurti T. N. , K. Ingles, S. Cocke, R. Pasch and T. Kitade, 1984: Details of low latitude medium range numerical weather prediction using a global spectral model II. Effect of orography and physical initialization. J. Meteor. Soc. Japan, 62, 613-649.
[30] Krishnamurti T. N. and H. S. Bedi, 1988: Cumulus parameterization and rainfall rates. Mon. Wea. Rev., 116, 583-599.
[31] Donner L. J., 1988: An initialization for cumulus convection in numerical weather prediction models. Mon. Wea. Rev. , 116, 377-385.
[32] Krishnamurti T. N. and H. S. Bedi and K. Ingles, 1993: Physical initialization using SSM/I rain rates. Tellus, 45A, 247-269.
[33] Le Dimet, F. X. and 0. Talagrand, 1986: Variational algorithms for analysis and assimilation of meteorological observations: Theoretical aspects. Tellus, 38A, 97-110.
[34] Navon I. M. , X. Zou, J. Derber and J. Sela, 1992: Variational datat assimilation with an adiabatic version of NMC spectral model. Mon. Wea. Rev., 120, 1433-1446.
[35] Zupanski D. and F. Mesinger, 1995: Four-dimensional variational assimilation of precipitation data. Mon. Wea. Rev., 123, 1112-1127.
[36] Kuo Y.-H, Zou X. and Guo Y.-R., 1995: Variational assimilation of precipitable water using a nonhydrostatic mesoscale adjoint model. Part Ⅰ: Moisture retrieval and sensitivity experiments, Mon. Wea. Rev., 124, 122-147.
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