张弛逼近约束下的雷达资料变分同化技术研究
中文摘要
很多研究表明，使用三维变分（3DVar）方法同化雷达资料可以改善强对流天气的降水预报。然而，一些研究发现，将雷达观测资料同化到数值预报模式中，可能会产生虚假的降水和较大的降水位置和量级误差。造成这一问题的一种可能原因是在同化雷达反射率资料时，所使用的云分析方案过高地估计了云内湿度；另一种原因是在使用3DVar同化雷达资料后，动力场和微物理场中缺乏适当的平衡。另外，背景误差协方差（BE）的选取极大地影响雷达资料同化的效果。
为此，本文基于WRF（Weather Rearch and Forecasting Model）及其3DVar （WRFDA）系统，首先利用NMC方法分别统计了以流函数ψ和非平衡速度势χu 为动量控制变量的BE和以水平风UV为动量控制变量的BE，比较了使用两组BE矩阵同化雷达径向风和反射率资料对一次分散性对流降水预报的影响。试验结果表明，两组控制变量特征长度尺度的差异主要体现在动量控制变量上的差别。同化真实雷达观测后，以ψχu为动量控制变量的BE产生的风场分析增量影响范围远远大于以UV为动量控制变量的BE，后者产生的分析增量局地性更强。使用以UV为动量控制变量的BE同化雷达资料对分散性对流降水预报的改进更多，且明显减少了以ψχu为动量控制变量的BE同化雷达资料预报的虚假的降水。
九年级物理试卷>嗜字怎么读然后探讨了雷达资料同化频率对一次强对流天气降水短时预报的影响。测试了15min和1h间隔同化频率
的敏感性，结果表明较高的雷达资料循环同化频率（15min间隔）虽然可以提高技巧评分，但导致了更大的降水过度预报偏差。这可能是因为间接雷达反射率同化方案高估了对流区域内的湿度，且这种湿偏差在快速循环同化过程中逐渐累积，最终影响到降水预报的效果。对分析场的诊断表明，较高频率循环同化试验产生了过大的风速和过强的低层水汽辐合，并产生了更强的上升运动。随后，利用谱Nudging技术对区域模式施加大尺度约束，即在预报期间谱Nudging GFS（Global Forecast System）预报场资料。结果表明，谱Nudging的应用可以显著地减小降水预报的位置偏差和降水过度预报的量级；可以有效地调节大尺度环流场，从而改善水汽辐合条件；此外，谱Nudging还改进了对风、温度和湿度等地表变量的预报。
论文最后设计了一种Nudging逼近多时刻3DVar分析场方法，即用观测Nudging方法同化多时刻3DVar同化雷达资料后的分析场，并将该方法应用于苏
皖地区夏季两个不同类型对流个例的同化试验，与仅使用3DVar的循环同化预报结果进行对比，考察该方法对对流降水预报的改进效果。结果表明，3DVar产生的初始场水汽辐合区和垂直运动上升区的范围明显偏大，这导致其产生虚假的和过强的降水预报。与3DVar方法相比，Nudging逼近多时刻3DVar分析场方法初始场中水平风、温度、湿度的均方根误差明显更低，水汽辐合区和垂直运动上升区与雷达观测的对流更为匹配，更接近实况，从而改善了雷达回波和降水的预报，减少了虚报的降水，缓解了降水的高估，尤其是对于强降水的预报改进更为明显。
关键词：雷达资料，同化，背景误差协方差，三维变分，同化频率，Nudging，强对流
心理疏导方式
杨昌鹏雨过山村唐王建RESEARCH ON VARIATIONAL ASSIMILATION TECHNIQUE OF RADAR DATA UNDER NUDGING
CONSTRAINTS
Abstract
南无地藏王菩萨Many studies have shown that using three-dimensional variational(3DVar)to assimilate radar data can improve the precipitation forecast of vere convective weather.However,some studies have found that assimilating radar obrvation data into numerical prediction model may produce spurious precipitation and large errors in the location and magnitude of precipitation.One possible reason for this problem is that the cloud analysis scheme ud in assimilating radar reflectivity data overestimates the humidity in the cloud.Another reason is the lack of proper balance in the dynamical and microphysical fields after assimilating radar data with3DVar.In addition,the lection of background error covariance(BE)greatly affects the effect of radar data assimilation.
To this end,bad on the WRF(Weather Rearch and Forecasting Model)and its 3DVar(WRFDA)system,this paper firstly calculates the BE with the stream function ψand the nonequi
librium velocity potentialχu as the momentum control variables and the BE with the horizontal wind UV as the momentum control variables,respectively, using the NMC method.The effects of two groups of BE matrixes assimilating radar radial wind and reflectivity data on a dispersive convective precipitation forecast are compared.The results show that the difference of characteristic length scale between the two groups of control variables is mainly reflected in the difference of momentum control variables.After assimilating the real radar obrvations,the BE withψχu as momentum control variable produces a much larger influence range of wind field analysis increment than the BE with UV as momentum control variable,which produces a more localized analysis increment.The u of BE assimilated radar data with UV as the momentum control variable provides more improvement in the forecast of dispersive convective precipitation and significantly reduces the spurious
precipitation forecast by BE assimilated radar data withψχu as the momentum control variable.
Then,the impact of radar data assimilation frequency on the short-term precipitation forecast of a vere convective is explored.The nsitivity of15min and 1h interval assimilation frequency is tested,and the results show that higher radar data assimilation frequency(15min interval)can improve the skill score,but it leads to greater precipitation overprediction bias.This may be due to the fact that the indirect radar reflectivity assimilation scheme overestimates the humidity in the convective region,
松江欢乐谷and this wet bias gradually accumulates in the rapid cycle assimilation process,which eventually affects the effectiveness of precipitation forecasting.A diagnosis of the analysis fields shows that the higher-frequency cyclic assimilation produces excessive wind speed and water vapor convergence at low levels and exaggerated upward movement.Then,the large-scale constraint is impod on the regional model by the spectral nudging technique,which nudging Global Forecast System(GFS)forecast field data during the forecast periods.The results demonstrate that the application of spectral nudging could significantly reduce the positional deviation of the precipitation forecast and the magnitude of overpredicted precipitation.Spectral nudging can effectively adjust large-scale circulation fields, thereby improving the conditions of water vapor convergence.Moreover,spectral nudging also improves the forecasts of surface variables such as wind,temperature, and humidity.
Finally,the paper designs a nudging multi-time3DVar analysis fields method, that is,using the obrvation nudging method to assimilate the analysis fields of multi-time3DVar assimilated radar data.This method is applied to the assimilation experiments of two summer convective precipitation cas in Jiangsu and Anhui Province,and compared with the cyclic assimilation results of3DVar only,the improvement of this method on convective precipitation forecasting is investigated. The resu
lts show that the range of water vapor convergence and vertical movement rising in the initial field generated by3DVar is obviously larger,which leads to spurious and overpredicted precipitation.Compared with the3DVar method,the root mean square error of horizontal wind,temperature and humidity in the initial field of the nudging multi-time3DVar analysis fields method is significantly lower,and the water vapor convergence and vertical movement rising area are more matched with
the radar obrved convection.Thus,improving the prediction of radar echoes and precipitation,reducing the fal precipitation,and alleviating the overestimation of precipitation.In particular,the improvement is more obvious for the forecast of heavy precipitation.
开车姿势Key words:radar data,assimilation,background error covariance,3DVar, assimilation frequency,Nudging,strong convection
