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An Analysis System Using Rapid-updating 4-D Variational Radar Data Assimilation Based on VDRAS
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摘要: 基于雷达资料快速更新四维变分同化 (RR4DVar) 技术和三维数值云模式，初步研发了一个针对对流尺度数值模拟的快速更新雷达四维变分分析系统。系统通过对京津冀6部多普勒天气雷达资料进行RR4DVar同化，并融合5 min自动气象站观测和中尺度数值模式结果，可快速分析得到12~18 min更新的低层大气三维动力、热力场的对流尺度结构特征。针对2009年7月22日发生在京津冀的一次强风暴个例，通过一系列敏感性试验，并利用局地加密资料进行检验对比，表明有效的雷达资料RR4DVar同化及自动气象站和中尺度模式资料融合方案、恰当的中尺度背景场设置和动力约束方法是获得合理结果的关键。研究也表明：恰当的系统配置能够模拟出与对流生消发展密切相关的近风暴环境特征，包括低层入流、垂直风切变、低层辐合上升和暖舌等，以及风暴自身形成的冷池、出流等与风暴演变密切相关的对流尺度结构。Abstract: On the basis of further improvement and development of Variational Doppler Radar Analysis System (VDRAS), a rapid-updating 4-D variational analysis system focusing on convective-scale numerical simulation and aiming at nowcasting convective storm has been preliminarily set up and tuned. The system is based on rapid-updating 4-D variational assimilation (RR4DVar) techniques of multi-Doppler-radar observations, a 3-D cloud-scale numerical model with simplified microphysics scheme which includes rainwater evaporation cooling and precipitation sedimentation processes, and an adjoint model. The system can rapidly get low-level 3-D analysis fields including convective-scale dynamical, thermo-dynamical and microphysical structures with 12-18-min updating cycles by assimilating both reflectivity and radial velocity observations from 6 CINRAD Doppler radars in Beijing-Tianjin-Hebei region using the RR4DVar scheme. It also integrates 5-min observations from regional auto weather stations (AWS) and forecast results from a meso-scale numerical model. Allowing for a strong convective storm case occurred in the region on 22 July 2009, simulated results from a series of sensitivity experiments including control, full-troposphere and full-microphysics, meso-scale background, and radar data assimilation are analyzed. These results are also compared and evaluated using intensive local observations from four wind profiler radars, two microwave radiometers, and two boundary layer towers. Some key factors for the system to produce appropriate analysis fields are illuminated. The system using low-level settings with the simplified microphysics scheme has comparable skill with full-troposphere settings and full-microphysics scheme. In the system, most significant RR4DVar assimilation of radar observations can be obtained using two or three scanning volumes from each radar within an assimilation window. As an effective supplement to radar observations on the ground, the AWS data is also very important on the RR4DVar assimilation of radar observations and simulations of dynamical and thermo-dynamical structures at several lower model levels. The meso-scale background and dynamical constraint for the RR4DVar assimilation of radar observations are sensitive to convective-scale simulation in both cold and warm start updating cycles. Results also indicate the system can produce robust pre-storm environment features including low-level inflows, vertical wind shear, low-level small-scale convergence, updraft and warm tongues. On the other hand, storm-associated convective-scale structures including cold pools and outflows can also be reasonably analyzed by the system.
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图 2 系统基本配置及观测信息
(a) 系统运算范围 (长虚线框)、结果分析范围 (短虚线框) 及观测站点位置 (加号表示天气雷达，空心三角表示风廓线，实心三角表示微波辐射仪，叉号表示观测塔; 图中粗实线为200 m地形等高线)，(b)40 dBZ以上组合反射率因子拼图指示2009年7月22日对流风暴在不同时间的强回波形态及演变过程
Fig. 2 Basic system configuration and observational information
(a) domain of system running (long dash line box), domain of result analysis (short dash line box), and location of observation sites (the plus denotes CINRAD radar site, the open triangle denotes wind profiler, the closed triangle denotes microwave radiometer, the multiplication sign denotes observation tower, thick black line indicates 200 m elevation), (b) patterns and evolution of strong storm echoes at different time on 22 Jul 2009 using mosaic of composite reflectivity no less than 40 dBZ
图 3 EXP_CTRL试验模拟的187.5 m风场 (矢量) 和扰动温度场 (填色)(白色等值线为40 dBZ以上雷达反射率因子观测值，间隔为10 dBZ)
(a)15：29，(b)18：29，(c)20：17
Fig. 3 Simulated winds (vectors) and perturbation temperature (the shaded) of 187.5 m level at 1529 BT (a), 1829 BT (b) and 2017 BT (c) from EXP_CTRL experiment (white contours indicate observations of radar reflectivity no less than 40 dBZ with 10 dBZ interval)
图 7 试验结果与风廓线雷达观测的风速偏差 (BS，实线) 和均方根误差 (RMSE，虚线)
Fig. 7 Bias (BS, solid lines) and root mean square error (RMSE, dashed lines) of wind speed profiles between results from experiments and observations from wind profiler radars
图 8 试验结果与风廓线雷达观测的风向偏差 (BS，实线) 和均方根误差 (RMSE，虚线)
Fig. 8 Bias (BS, solid lines) and root mean square error (RMSE, dashed lines) of wind direction profiles between results from experiments and observations from wind profiler radars
图 9 试验结果与微波辐射仪观测的温度偏差 (BS，实线) 和均方根误差 (RMSE，虚线)
Fig. 9 Bias (BS, solid lines) and root mean square error (RMSE, dashed lines) of temperature profiles between results from experiments and observations from microwave radiometers
表 1 敏感性试验设置
Table 1 Configuration of sensitivity experiments
试验分类 试验名称 试验描述及其与EXP_CTRL试验设置的差异 控制试验 EXP_CTRL 垂直15层，模式层高5.4375 km；雷达资料同化高度3 km；
同化窗长度365 s；计算背景场使用25 km间隔BJ-RUC模式探空；
热启动中尺度背景风场调整权重系数λ=0.4；采用简化Kessler微物理方案全对流层试验 EXP_DEEP 垂直40层，模式层高14.8125 km；雷达资料同化高度8.5 km；
采用完整Kessler微物理方案背景场敏感性试验 NO_AWS 冷启动和热启动每个循环均不使用地面AWS资料 CBG_NORUC 冷启动中尺度背景场计算没有使用BJ-RUC模式资料，仅用雷达
VAD及14:00北京加密探空和地面AWS资料分析得到CBG_RUCOUT 冷启动中尺度背景场使用BJ-RUC模式结果直接插值得到 WBG_MAWIND0 热启动中尺度背景风场调整权重系数，即热启动每个循环背景风场
不用中尺度分析风场调整，而使用来自云模式上一循环的6min预报WBG_MAWIND20 热启动中尺度背景风场调整权重系数λ=0.2 WBG_MAWIND60 热启动中尺度背景风场调整权重系数λ=0.6 WBG_MAWIND100 热启动中尺度背景风场调整权重系数λ=1，即热启动每个循环的
背景风场全部用中尺度分析风场替代雷达资料同化敏感性试验 NO_CLOSERADAR2C 不同化离风暴最接近的两部C波段雷达 (张北和承德) 的资料 NO_CLOSERADAR2S 不同化离风暴最接近的两部S波段雷达 (北京和天津) 的资料 NO_ALLRADAR 不同化所有6部雷达的资料，只用中尺度分析场 RADAR_3V 同化窗长度为730 s RADAR_6V 同化窗长度为1830 s 
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