同化雷达反射率资料对一次飑线过程的模拟研究
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摘要
在用集合卡曼滤波方法(EnKF)同化雷达径向风、雷达反演风和CGPS水汽资料的基础上,对2014年7月30日发生在安徽中东部的一次飑线过程采用雷达反射率资料对初始水汽场进行调整。该方法相对EnKF的模拟结果,在飑线强度、位置、持续时间、产生降水和地面风场方面均有改进。改进湿度场后飑线前部的地面辐合区模拟效果较好,这可能是飑线强度和位置模拟效果改进的原因之一。没有调整湿度场时飑线维持时间较短,且强度较弱,这是由于飑线后部的中层干冷空气夹卷较弱,且冷池很快远离飑线,不利飑线维持。调整湿度场后,飑线后部干冷空气夹卷较强,且在对流区下沉形成冷池,冷池位于飑线后部,有利飑线维持。夹卷加强的可能原因是:采用雷达反射率资料调整湿度场增加了中低层(600～900 hPa)湿度,大气不稳定性增加,对流发展造成低值系统增强,其南部的偏西风增强,导致飑线后部的干冷空气夹卷增强。该试验揭示了湿度调整、大气不稳定度改变造成的动力场调整对对流发展和组织的重要作用。
Based on the assimilation of radar radial wind, radar retrieval wind and GPS water vapor data with the ensemble Kalman filtering(EnKF) method, the initial water vapor field of a squall line that occurred on 30 July 2014 in east-central Anhui was adjusted according to radar reflectivity data. Compared with the simulation results of EnKF, this method has improved the simulation of intensity, location, duration, precipitation and surface wind of the squall line. Simulation performance of ground convergence zone in the front of the squall line was improved after adjusting humidity field, leading to better simulation results of the intensity and location of the squall line. In addition, the squall line maintained for a shorter time without humidity adjustment and the intensity was weak. This can be explained by the fact that midlevel dry air entrainment was weak in the rear of the squall line, and the cold pool moved quickly away from the squall line, which was unfavorable for the maintenance of squall line. In contrast, after the humidity field was adjusted, dry air entrainment in the rear of the squall line was strong, and the resultant downdraft generated the cold pool, which was located in the rear of the squall line, favorable for the maintenance of squall line. The possible reason for dry air entrainment strengthening tends to be that atmospheric instability was increased with the moisturizing of mid-low level(600 —900 hPa) after the humidity field adjustment, and the development of convection results in the enhancement of low-value system. The westerly in the south of low-value system intensifies, strengthening the dry cold air entrainment in the rear of the squall line. This experiment reveals that the adjustment of the humidity leads to the adjustment of the dynamic field, which plays an important role in the development and organization of the convection system.
Based on the assimilation of radar radial wind, radar retrieval wind and GPS water vapor data with the ensemble Kalman filtering(EnKF) method, the initial water vapor field of a squall line that occurred on 30 July 2014 in east-central Anhui was adjusted according to radar reflectivity data. Compared with the simulation results of EnKF, this method has improved the simulation of intensity, location, duration, precipitation and surface wind of the squall line. Simulation performance of ground convergence zone in the front of the squall line was improved after adjusting humidity field, leading to better simulation results of the intensity and location of the squall line. In addition, the squall line maintained for a shorter time without humidity adjustment and the intensity was weak. This can be explained by the fact that midlevel dry air entrainment was weak in the rear of the squall line, and the cold pool moved quickly away from the squall line, which was unfavorable for the maintenance of squall line. In contrast, after the humidity field was adjusted, dry air entrainment in the rear of the squall line was strong, and the resultant downdraft generated the cold pool, which was located in the rear of the squall line, favorable for the maintenance of squall line. The possible reason for dry air entrainment strengthening tends to be that atmospheric instability was increased with the moisturizing of mid-low level(600 —900 hPa) after the humidity field adjustment, and the development of convection results in the enhancement of low-value system. The westerly in the south of low-value system intensifies, strengthening the dry cold air entrainment in the rear of the squall line. This experiment reveals that the adjustment of the humidity leads to the adjustment of the dynamic field, which plays an important role in the development and organization of the convection system.
引文
高拴柱,2005.集合Kalman滤波资料同化技术及研究现状[J].气象,31(6):3-8. Gao S Z,2005. Review on ensemble Kalman filterdata assimilation[J]. Meteor Mon,3:1(6):3-8(in Chinese).
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李昕,王元,明杰,等,2016.雷达径向风和反演风联合同化在台风灿都(2010)数值预报中的研究[J].气象,42(6):649-663. Li X,Wang Y,Ming J,et al,2016. A combined radar data assimilation strategy of radial velocity and retrieved wind and its impact on the forecasting of tropical cyclone Chanthu(2010)[J]. Meteor Mon,42(6):649-663(in Chinese).
闵锦忠,陈杰,王世璋,等,2011. WRF-EnSRF同化系统的效果检验及其应用[J].气象科学,31(2):135-144. Min J Z,Chen J,Wang S Z,et al,2011. The tests of WRF-EnSRF data assimilation system and its applications in storm scale events[J]. J Meteor Sci,31(2):135-144(in Chinese).
邱学兴,Zhang Fuqing,2016. EnKF同化雷达资料对一次极端局地强降水事件预报影响及其可预报性分析[J].中国科学:地球科学,46(1):27-42. Qiu X X,Zhang F Q,2016. Prediction and predictability of a catastrophic local extreme precipitation event through cloud-resolving ensemble analysis and forecasting with Doppler radar observations[J]. Sci China:Earth Sci,46(1):27-42(in Chinese).
曲晓波,张建忠,2009. 2009年中国主要灾害性天气事件概述[C]//2009第六届全国灾害性天气预报技术研讨会.北京.Qu X B,Zhang J Z,2009. Summary of major disastrous weather events in China in 2009[C]//2009 Sixth National Seminar on Disastrous Weather Forecasting Technology. Beijing(in Chinese).
孙建华,郑淋淋,赵思雄,2014.水汽含量对飑线组织结构和强度影响的数值试验[J].大气科学,38(4):742-755. Sun J H,Zheng L L,Zhao S X, 2014. Impact of moisture on the organizational mode and intensity of squall lines determined through numerical experiments[J]. Chinese J Atmos Sci, 38(4):742-755(in Chinese).
许小永,刘黎平,郑国光,2006.集合卡尔曼滤波同化多普勒雷达资料的数值试验[J].大气科学,30(4):712-728. Xu X Y,Liu L P,Zheng G G,2006. Numerical experiment of assimilation of Doppler radar data with an ensemble Kalman filter[J]. Chinese J Atmos Sci,30(4):712-728(in Chinese).
张宁,苏爱芳,史一丛,2017. 2014年一次飑线的发展维持原因分析[J].气象,43(11):1383-1392. Zhang N,Su A F,Shi Y C,2017.Causation analysis of evolution of a squall line in 2014[J]. Meteor Mon,43(11):1383-1392(in Chinese).
Aksoy A,Dowell D C,Snyder C,2009. A multicase comparative assessment of the ensemble Kalman filter for assimilation of radar observations. Part I:storm-scale analyses[J]. Mon Wea Rev,137(6):1805-1824.
Aksoy A,Dowell D C,Snyder C,2010. A multicase comparative assessment of the ensemble Kalman filter for assimilation of radar observations. PartⅡ:short-range ensemble forecasts[J]. Mon Wea Rev, 138(4):1273-1292.
Barker D M,Huang W,Guo Y R,et al.2004. A three-dimensional variational data assimilation system for MM5:implementation and initial results[J]. Mon Wea Rev,132(4):897-914.
Hong S Y,Dudhia J,Chen S H,2004. A revised approach to icemicrophysical processes for the bulk parameterization of clouds and precipitation[J]. Mon Wea Rev,132(1):103-120.
Hu M,Xue M, Brewster K,2004. 3DVAR and cloud analysis with WSR-88D Level-II data for the prediction of the fort worth,texas,tornadic thunderstorms. Part I:cloud analysis and its impact[J]. Mon Wea Rev, 134(2):699-721.
Meng Z Y,Zhang F Q,2007. Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part II:imperfect model experiments[J]. Mon Wea Rev,135(4):1403-1423.
Meng Z Y,Zhang F Q,2008a. Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part III:comparison with 3DVAR in a real-data case study[J]. Mon Wea Rev,136(2):522-540.
Meng Z Y,Zhang F Q,2008b. Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part IV:comparison with 3DVAR in a month-long experiment[J]. Mon Wea Rev,136(10):3671-3682.
Noh Y,Cheon W G,Hong S Y,2003. Improvement of the K-profile model for the planetary boundary layer based on large eddy simulation data[J]. Bound Layer Meteor, 107:401-427.
Skamarock W C,Klemp J B,Dudhia J,et al,2005. A description of the advanced research WRF version 2[Z]. NCAR Tech Note NCAR/TN-4 681STR:19.
Takemi T,2006. Impacts of moisture profile on the evolution and organization of midlatitude squall lines under various shear conditions[J]. Atmos Res,82(1/2):37-54.
Takemi T, 2007a. A sensitivity of squall-line intensity to environmental static stability under various shear and moisture conditions[J]. Atmos Res,84(4):374-389.
Takemi T,2007b. Environmental stability control of the intensity of squall lines under low-level shear conditions[J]. J Geophys Res,112(D24):D24110.
Tong M,Xue M,2005. Ensemble Kalman filte assimilation of Doppler radar data with a compressible nonhydrostatic model:OSS experiments[J]. Mon Wea Rev, 133:1789-1807.
Zhang F Q,Meng Z Y, Aksoy A, 2006a. Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation.Part I:perfect model experiments[J]. Mon Wea Rev, 134(2):722-736.
Zhang F Q,Odins A M,Nielsen-Gammon J W,2006b. Mesoscale predictability of an extreme warm-season precipitation event[J].Wea Forecasting,21(2):149-166.
Zhang F Q,Snyder C,Sun J Z,2004. Impacts of initial estimate and observation availability on convective-scale data assimilation with an ensemble Kalman filter[J]. Mon Wea Rev,132(5):1238-1253.
Zhang F Q,Weng Y H,Sippel J A,et al,2009. Cloud-resolving hurricane initialization and prediction through assimilation of doppler radar observations with an ensemble Kalman filter[J]. Mon Wea Rev,137(7):2105-2125.
Zheng L L,Sun J H,Zhang X L,et al,2013. Organizational modes of mesoscale convective systems over Central East China[J]. Wea Forecasting,28(5):1081-1098.
兰伟仁,朱江,Xue Ming,等,2010a.风暴尺度天气下利用集合卡尔曼滤波模拟多普勒雷达资料同化试验1.不考虑模式误差的情形[J].大气科学,34(3):640-652. Lan W R,Zhu J,Xue M,et al,2010a. Storm-scale ensemble Kalman filter data assimilation experiments using simulated Doppler radar data. PartⅠ:perfect model tests[J]. Chinese J Atmo Sci, 34(3):640-652(in Chinese).
兰伟仁,朱江,Xue Ming,等,2010b.风暴尺度天气下利用集合卡尔曼滤波模拟多普勒雷达资料同化试验Ⅱ.考虑模式误差的情形[J].大气科学,34(4):737-753. Lan W R,Zhu J,Xue M,et al,2010b. Storm-scale ensemble Kalman filter data assimilation experiments using simulated Doppler radar data Part II:imperfect model tests[J]. Chinese J Atmos Sci, 34(4):737-753(in Chinese).
李昕,王元,明杰,等,2016.雷达径向风和反演风联合同化在台风灿都(2010)数值预报中的研究[J].气象,42(6):649-663. Li X,Wang Y,Ming J,et al,2016. A combined radar data assimilation strategy of radial velocity and retrieved wind and its impact on the forecasting of tropical cyclone Chanthu(2010)[J]. Meteor Mon,42(6):649-663(in Chinese).
闵锦忠,陈杰,王世璋,等,2011. WRF-EnSRF同化系统的效果检验及其应用[J].气象科学,31(2):135-144. Min J Z,Chen J,Wang S Z,et al,2011. The tests of WRF-EnSRF data assimilation system and its applications in storm scale events[J]. J Meteor Sci,31(2):135-144(in Chinese).
邱学兴,Zhang Fuqing,2016. EnKF同化雷达资料对一次极端局地强降水事件预报影响及其可预报性分析[J].中国科学:地球科学,46(1):27-42. Qiu X X,Zhang F Q,2016. Prediction and predictability of a catastrophic local extreme precipitation event through cloud-resolving ensemble analysis and forecasting with Doppler radar observations[J]. Sci China:Earth Sci,46(1):27-42(in Chinese).
曲晓波,张建忠,2009. 2009年中国主要灾害性天气事件概述[C]//2009第六届全国灾害性天气预报技术研讨会.北京.Qu X B,Zhang J Z,2009. Summary of major disastrous weather events in China in 2009[C]//2009 Sixth National Seminar on Disastrous Weather Forecasting Technology. Beijing(in Chinese).
孙建华,郑淋淋,赵思雄,2014.水汽含量对飑线组织结构和强度影响的数值试验[J].大气科学,38(4):742-755. Sun J H,Zheng L L,Zhao S X, 2014. Impact of moisture on the organizational mode and intensity of squall lines determined through numerical experiments[J]. Chinese J Atmos Sci, 38(4):742-755(in Chinese).
许小永,刘黎平,郑国光,2006.集合卡尔曼滤波同化多普勒雷达资料的数值试验[J].大气科学,30(4):712-728. Xu X Y,Liu L P,Zheng G G,2006. Numerical experiment of assimilation of Doppler radar data with an ensemble Kalman filter[J]. Chinese J Atmos Sci,30(4):712-728(in Chinese).
张宁,苏爱芳,史一丛,2017. 2014年一次飑线的发展维持原因分析[J].气象,43(11):1383-1392. Zhang N,Su A F,Shi Y C,2017.Causation analysis of evolution of a squall line in 2014[J]. Meteor Mon,43(11):1383-1392(in Chinese).
Aksoy A,Dowell D C,Snyder C,2009. A multicase comparative assessment of the ensemble Kalman filter for assimilation of radar observations. Part I:storm-scale analyses[J]. Mon Wea Rev,137(6):1805-1824.
Aksoy A,Dowell D C,Snyder C,2010. A multicase comparative assessment of the ensemble Kalman filter for assimilation of radar observations. PartⅡ:short-range ensemble forecasts[J]. Mon Wea Rev, 138(4):1273-1292.
Barker D M,Huang W,Guo Y R,et al.2004. A three-dimensional variational data assimilation system for MM5:implementation and initial results[J]. Mon Wea Rev,132(4):897-914.
Hong S Y,Dudhia J,Chen S H,2004. A revised approach to icemicrophysical processes for the bulk parameterization of clouds and precipitation[J]. Mon Wea Rev,132(1):103-120.
Hu M,Xue M, Brewster K,2004. 3DVAR and cloud analysis with WSR-88D Level-II data for the prediction of the fort worth,texas,tornadic thunderstorms. Part I:cloud analysis and its impact[J]. Mon Wea Rev, 134(2):699-721.
Meng Z Y,Zhang F Q,2007. Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part II:imperfect model experiments[J]. Mon Wea Rev,135(4):1403-1423.
Meng Z Y,Zhang F Q,2008a. Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part III:comparison with 3DVAR in a real-data case study[J]. Mon Wea Rev,136(2):522-540.
Meng Z Y,Zhang F Q,2008b. Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation. Part IV:comparison with 3DVAR in a month-long experiment[J]. Mon Wea Rev,136(10):3671-3682.
Noh Y,Cheon W G,Hong S Y,2003. Improvement of the K-profile model for the planetary boundary layer based on large eddy simulation data[J]. Bound Layer Meteor, 107:401-427.
Skamarock W C,Klemp J B,Dudhia J,et al,2005. A description of the advanced research WRF version 2[Z]. NCAR Tech Note NCAR/TN-4 681STR:19.
Takemi T,2006. Impacts of moisture profile on the evolution and organization of midlatitude squall lines under various shear conditions[J]. Atmos Res,82(1/2):37-54.
Takemi T, 2007a. A sensitivity of squall-line intensity to environmental static stability under various shear and moisture conditions[J]. Atmos Res,84(4):374-389.
Takemi T,2007b. Environmental stability control of the intensity of squall lines under low-level shear conditions[J]. J Geophys Res,112(D24):D24110.
Tong M,Xue M,2005. Ensemble Kalman filte assimilation of Doppler radar data with a compressible nonhydrostatic model:OSS experiments[J]. Mon Wea Rev, 133:1789-1807.
Zhang F Q,Meng Z Y, Aksoy A, 2006a. Tests of an ensemble Kalman filter for mesoscale and regional-scale data assimilation.Part I:perfect model experiments[J]. Mon Wea Rev, 134(2):722-736.
Zhang F Q,Odins A M,Nielsen-Gammon J W,2006b. Mesoscale predictability of an extreme warm-season precipitation event[J].Wea Forecasting,21(2):149-166.
Zhang F Q,Snyder C,Sun J Z,2004. Impacts of initial estimate and observation availability on convective-scale data assimilation with an ensemble Kalman filter[J]. Mon Wea Rev,132(5):1238-1253.
Zhang F Q,Weng Y H,Sippel J A,et al,2009. Cloud-resolving hurricane initialization and prediction through assimilation of doppler radar observations with an ensemble Kalman filter[J]. Mon Wea Rev,137(7):2105-2125.
Zheng L L,Sun J H,Zhang X L,et al,2013. Organizational modes of mesoscale convective systems over Central East China[J]. Wea Forecasting,28(5):1081-1098.