多普勒雷达资料4DVAR同化反演的模拟研究

DYNAMICAL AND MICROPHYSICAL RETRIEVAL FROM SIMULATED DOPPLER RADAR OBSERVATIONS USING THE 4DVAR ASSIMILATION TECHNIQUE

利用Sun等建立的同化模式和四维变分同化方法对多普勒雷达资料反演大气风场、热力场和微物理场进行了模拟试验研究。反演的基本思路是 :将 4DVAR同化方法应用到三维云模式 ,定义价值函数表征雷达资料与模式预报结果之间的差别 ,通过极小化价值函数得到反演场 ,价值函数相对模式控制变量的梯度由伴随模式求取。试验结果表明 ,4DVAR同化技术能够从单 (双 )多普勒雷达资料反演大气三维风场、热力场和微物理场。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呈正相关。速度场和雨水场反演精度较高 ,温度场、云水和水汽的反演精度次之 ,温度场的准确反演需要较长的同化时间。价值函数中加入背景场 ,哪怕是单点探空给出的平均场信息也有利于提高反演精度。在采用单部多普勒雷达资料进行反演时 ,速度场的反演误差较大。反演区相对雷达站的位置变化对速度场反演结果有一定的影响 ,而对其他变量的反演影响很小。两个时次的雷达观测资料基本足够提供反演所需的时间演变信息 ,同化更多时次的雷达资料 ,反演效果改进很小。雷达观测资料的缺值会显著降低同化效果 ,甚至可能导致同化失败 ,引入背景场可以改善这一状况。 4DVAR同化技术对于雷达观测资料误差不太敏感。利用双多普勒雷达合成风场提供水平风场边?

Based on a cloud model and the four-dimensional variational data assimilation method developed by Sun, et al., simulated experiments of dynamical and microphysical retrieval from Doppler radar data were performed. The 4DVAR data assimilation technique was applied to a cloud scale model with a warm rain parameterization scheme. The 3D wind, thermodynamical, and microphysical fields were determined by minimizing a cost function, defined by the difference between both radar observed radial velocities reflectivities and their model predictions. The adjoint of the numerical model was used to provide the gradient of the cost function with respect to the control variables. Experiments demonstrated that the 4DVAR assimilation method was able to retrieve the detailed structure of wind, thermodynamics, and microphysics using either dual-Doppler or single-Doppler information. The quality of retrieval depended strongly on the magnitude of constraint with respect to the variables. Retrieving the temperature field, cloud water and water vapor was more difficult than the recovery of the wind field and rainwater. Accurate thermodynamic retrieval requires a longer assimilation period. The inclusion of a background term, even mean fields from a single sounding, could be helpful to reduce the retrieval errors. Less accurate velocity fields were obtained when single-Doppler data were used. It was found that the retrieved velocity was sensitive to the location of the retrieval domain relative to the radars while the other fields had very little change. Two radar volumetric scans are generally adequate to providing the evolution, although the use of additional volumes improves the retrieval. As the amount of the observations decreased, the performance of the retrieval is degraded. However, the missing observations can be compensated by adding a background term to the cost function. The technique is robust to random errors in radial velocity and calibration errors in reflectivity. The boundary conditions from the dual-Doppler synthesized winds were sufficient for the retrieval. When the retrieval was mainly controlled by the observations in the regions away from the boundaries, the simple boundary conditions from VAD analysis were also available. The microphysical retrieval was sensitive to model errors.