四维变分和集合卡尔曼滤波同化多普勒雷达资料的方法及其反演暴雨中尺度结构的研究
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摘要
采用两种四维同化方法:四维变分(4D-VAR)和集合卡尔曼滤波(EnKF),在云模式
中同化多普勒雷达资料,并利用模拟雷达资料和实测雷达资料考察了两种同化技术的同化
效果及其定量获取灾害天气中尺度信息的能力。主要研究内容和结果如下:
(1) 利用Sun and Crook(1997) 建立的云模式和4D-VAR算法,同化模拟多普勒雷达资料,
证明了4D-VAR同化技术能够从单(双)多普勒雷达资料反演大气三维风场、热力场
利微物理场。各个变量反演精度高低与同化过程中变量受约束的大小程度相关。
(2) 利用宜昌-荆州双多普勒雷达获得的长江中游一次梅雨锋暴雨过程的观测资料,证明
了多普勒雷达资料4D-VAR同化技术定量获取暴雨中尺度结构信息的能力。反演结果
显示,中尺度切变线是本次暴雨过程的主要动力特征;对流云表现为低层辐合、高层
辐散并有垂直上升运动伴随:低层是低压区,高层为高压区;中部为暖区而上、下部
为冷区;水汽、云水和雨水分别集中在对流云体内、上升气流区和强回波区。
(3) 采用合肥-无为-马鞍山双/三多普勒雷达获得的长江下游一次梅雨锋暴雨过程的观
测资料,进一步考察了4D-VAR同化算法反演暴雨中尺度结构的能力。结果表明:三
部雷达两两组合以及各自单独反演结果大体上是一致的;中尺度辐合线是本次暴雨过
程的主要动力特征:对流云表现为低层辐合、高层强辐散并伴有垂直上升运动:中低
层是相对冷区和高压区,高层是相对暖区和低压区;对流云中下部为干冷空气,云水
和雨水分别集中在上升气流区和强回波区。
(4) 不考虑模式中的湿物理过程,采用干模式来4D-VAR同化反演低层水平风场。几个个
例研究表明,利用干模式同化多普勒雷达资料反演的低层水平风场是可靠的。
(5) 采用EnKF技术,在云模式中同化模拟多普勒雷达资料。结果显示:同化几个时次的
资料后,EnKF分析结果接近真值,证明了EnKF同化多普勒雷达资料的可行性。
(6) 利用多普勒雷达资料EnKF同化技术,分析了前面所述两次暴雨过程的中尺度结构,
并将EnKF同化分析结果与4D-VAR同化反演结果进行了对比。结果表明:利用EnKF
同化技术,可以从多普勒雷达资料分析出暴雨中尺度系统的三维风场、热力场和微物
理场结构。尽管在某些细节上存在一些差别,EnKF和4D-VAR两种方法反演的暴雨
中尺度结构特征大体是一致的。
Two methods: the four-dimensional variational (4D-VAR) and the ensemble Kalman filter (EnKF), are applied to assimilate Doppler radar data in a cloud model. The quality of retrieval and performances of the two techniques in obtaining quantitatively mesoscale information of hazardous weather systems, have been tested using simulated data and real observations. The major contents and conclusions are presented as follows:Firstly, the 4D-VAR technique and the cloud model developed by Sun and Crook (1997) , are used to assimilate data from a simulated convective storm. Experiments demonstrate that the 4D-VAR assimilation approach is able to retrieve the detailed structure of wind, thermodynamics, and microphysics using either dual-Doppler or single-Doppler radar data. The quality of retrieval depends strongly on the magnitude of constraint with respect to the variable.Secondly, the 4D-VAR method is applied to a Meiyu heavy precipitation in the middle Yangtze River reach, observed by the YICHANG- JINGZHOU dual-Doppler radars. The study has clearly demonstrated the ability of the 4D-VAR assimilation approach to drive the quantitative information of rainstorm mesoscale structure from real radar observations. It has been revealed in the assimilation results that the torrential rainfall is caused by the wind shear line at low level. The convective rainfall is often related to lower level convergence and upper level divergence coupled with an updraft, characterized by high pressure at lower level and low pressure at upper level. The temperature structure of convective system shows warmer at middle level and colder at lower and upper level than the environment. The water vapor, could water and rainwater are associated with the convective cloud, the updraft and the reflectivity, respectively.Thirdly, the mesoscale feature of a Meiyu front rainstorm over the lower Yangtze River basin is investigated by the 4D-VAR assimilation of data from the HEFEI- WUWEI -MAANSHAN dual / three -Doppler radars. The assimilations of data from three groups of dual radars, or single radar, produce generally similar mesoscale structures. The convergence line is primary dynamical characteristic of the Meiyu heavy precipitation. The convective system is characterized by lower level convergence and upper level
divergence associated with the vertical motion. A region of cooling and high pressure occurs at lower and middle levels compared to warming and low pressure at upper level. The convective cloud is dry and cooling at lower and middle levels. The cloud water and rainwater are associated with the updraft and the reflectivity, respectively.Fourthly, Doppler radar radial velocity is 4D-VAR assimilated in a dry version of cloud model which excludes the moist process. A few case studies demonstrate the retrieved low-level wind fields are reliable through the assimilation of Doppler radar data in a dry version of cloud model.Fifthly, we explore the use of an ensemble Kalman filter (EnKF) to assimilate simulated Doppler radar data in a cloud model. It is found that the EnKF assimilation method is able to produce analyses that accurately approximate the true state after several assimilation cycles. Thus, the ability and feasibility of EnKF in assimilating radar data are demonstrated.Lastly, we adopt the EnKF assimilation technique to analyze the mesoscale structures of two rainstorms, which are then compared with those obtained by the 4D-VAR approach. The result indicates that the 3D wind, thermodynamical, and microphysical structures of rainstorm mesoscale convective system can be obtained from Doppler radar data using the EnKF technique. The mesoscale features of convective system revealed by the EnKF analyses, are also basically reflected in the 4D-VAR results although discrepancies existed in the detailed structure of the retrieval results.
中同化多普勒雷达资料,并利用模拟雷达资料和实测雷达资料考察了两种同化技术的同化
效果及其定量获取灾害天气中尺度信息的能力。主要研究内容和结果如下:
(1) 利用Sun and Crook(1997) 建立的云模式和4D-VAR算法,同化模拟多普勒雷达资料,
证明了4D-VAR同化技术能够从单(双)多普勒雷达资料反演大气三维风场、热力场
利微物理场。各个变量反演精度高低与同化过程中变量受约束的大小程度相关。
(2) 利用宜昌-荆州双多普勒雷达获得的长江中游一次梅雨锋暴雨过程的观测资料,证明
了多普勒雷达资料4D-VAR同化技术定量获取暴雨中尺度结构信息的能力。反演结果
显示,中尺度切变线是本次暴雨过程的主要动力特征;对流云表现为低层辐合、高层
辐散并有垂直上升运动伴随:低层是低压区,高层为高压区;中部为暖区而上、下部
为冷区;水汽、云水和雨水分别集中在对流云体内、上升气流区和强回波区。
(3) 采用合肥-无为-马鞍山双/三多普勒雷达获得的长江下游一次梅雨锋暴雨过程的观
测资料,进一步考察了4D-VAR同化算法反演暴雨中尺度结构的能力。结果表明:三
部雷达两两组合以及各自单独反演结果大体上是一致的;中尺度辐合线是本次暴雨过
程的主要动力特征:对流云表现为低层辐合、高层强辐散并伴有垂直上升运动:中低
层是相对冷区和高压区,高层是相对暖区和低压区;对流云中下部为干冷空气,云水
和雨水分别集中在上升气流区和强回波区。
(4) 不考虑模式中的湿物理过程,采用干模式来4D-VAR同化反演低层水平风场。几个个
例研究表明,利用干模式同化多普勒雷达资料反演的低层水平风场是可靠的。
(5) 采用EnKF技术,在云模式中同化模拟多普勒雷达资料。结果显示:同化几个时次的
资料后,EnKF分析结果接近真值,证明了EnKF同化多普勒雷达资料的可行性。
(6) 利用多普勒雷达资料EnKF同化技术,分析了前面所述两次暴雨过程的中尺度结构,
并将EnKF同化分析结果与4D-VAR同化反演结果进行了对比。结果表明:利用EnKF
同化技术,可以从多普勒雷达资料分析出暴雨中尺度系统的三维风场、热力场和微物
理场结构。尽管在某些细节上存在一些差别,EnKF和4D-VAR两种方法反演的暴雨
中尺度结构特征大体是一致的。
Two methods: the four-dimensional variational (4D-VAR) and the ensemble Kalman filter (EnKF), are applied to assimilate Doppler radar data in a cloud model. The quality of retrieval and performances of the two techniques in obtaining quantitatively mesoscale information of hazardous weather systems, have been tested using simulated data and real observations. The major contents and conclusions are presented as follows:Firstly, the 4D-VAR technique and the cloud model developed by Sun and Crook (1997) , are used to assimilate data from a simulated convective storm. Experiments demonstrate that the 4D-VAR assimilation approach is able to retrieve the detailed structure of wind, thermodynamics, and microphysics using either dual-Doppler or single-Doppler radar data. The quality of retrieval depends strongly on the magnitude of constraint with respect to the variable.Secondly, the 4D-VAR method is applied to a Meiyu heavy precipitation in the middle Yangtze River reach, observed by the YICHANG- JINGZHOU dual-Doppler radars. The study has clearly demonstrated the ability of the 4D-VAR assimilation approach to drive the quantitative information of rainstorm mesoscale structure from real radar observations. It has been revealed in the assimilation results that the torrential rainfall is caused by the wind shear line at low level. The convective rainfall is often related to lower level convergence and upper level divergence coupled with an updraft, characterized by high pressure at lower level and low pressure at upper level. The temperature structure of convective system shows warmer at middle level and colder at lower and upper level than the environment. The water vapor, could water and rainwater are associated with the convective cloud, the updraft and the reflectivity, respectively.Thirdly, the mesoscale feature of a Meiyu front rainstorm over the lower Yangtze River basin is investigated by the 4D-VAR assimilation of data from the HEFEI- WUWEI -MAANSHAN dual / three -Doppler radars. The assimilations of data from three groups of dual radars, or single radar, produce generally similar mesoscale structures. The convergence line is primary dynamical characteristic of the Meiyu heavy precipitation. The convective system is characterized by lower level convergence and upper level
divergence associated with the vertical motion. A region of cooling and high pressure occurs at lower and middle levels compared to warming and low pressure at upper level. The convective cloud is dry and cooling at lower and middle levels. The cloud water and rainwater are associated with the updraft and the reflectivity, respectively.Fourthly, Doppler radar radial velocity is 4D-VAR assimilated in a dry version of cloud model which excludes the moist process. A few case studies demonstrate the retrieved low-level wind fields are reliable through the assimilation of Doppler radar data in a dry version of cloud model.Fifthly, we explore the use of an ensemble Kalman filter (EnKF) to assimilate simulated Doppler radar data in a cloud model. It is found that the EnKF assimilation method is able to produce analyses that accurately approximate the true state after several assimilation cycles. Thus, the ability and feasibility of EnKF in assimilating radar data are demonstrated.Lastly, we adopt the EnKF assimilation technique to analyze the mesoscale structures of two rainstorms, which are then compared with those obtained by the 4D-VAR approach. The result indicates that the 3D wind, thermodynamical, and microphysical structures of rainstorm mesoscale convective system can be obtained from Doppler radar data using the EnKF technique. The mesoscale features of convective system revealed by the EnKF analyses, are also basically reflected in the 4D-VAR results although discrepancies existed in the detailed structure of the retrieval results.
引文
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101 刘黎平.用双多普勒雷达反演降水系统三维风场试验研究.应用气象学报,2003,14(4) : 502-504
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