雷达资料对0414号台风“云娜”数值预报的改进

运用NCEP/NCAR再分析资料,研究了2004年12月初登陆我国台湾的冬季台风"南玛都"的性质结构特征,结果表明:冬季台风"南玛都"具有与夏季台风相同的暖湿中心和低层辐合、高层辐散的环流特征,不同的是低层辐合气流中西南气流不明显,台风涡旋气流对大气的扰动偏弱.台风过程海平面气压距平场的EOF分析表明,台风生命史中的扰动主要由EOF1和EOF2构成(占90%的方差),EOF1反映台风结构和强度变化,EOF2反映了台风移动过程中的环境状态变化.     

多普勒天气雷达资料对中尺度模式短时预报的影响

利用中尺度模式ARPS(The Advanced Regional Prediction System)及其资料分析系统ADAS(ARPS Data Analysis System),将国内新一代多普勒雷达(CINRAD)反射率及径向风资料直接用于中尺度数值模拟,通过一次华北地区暴雨过程的模拟对比试验,分析了雷达资料对初始场的改进效果及其对模拟结果的影响,结果表明:(1)利用雷达径向风资料对初始风场进行调整后,自近地面到对流层顶的u,v,w都发生了变化,调整后的初始风场在对流层中层变化最大.(2)利用雷达反射率进行微物理调整和云分析能调整初始场中的云水信息,使得雷达回波附近3 km以下的水汽混合比(qv)增加,4 km以下的雨水混合比(qr)增加,对流层(约10 km以下)的云水混合比(qc)增加,4～9 km的对流层上部云冰混合比(qi)和雪混合比(qs)增加.ADAS通过非绝热初始化调整温度场,从而得到了一个动力和热力上平衡的初始场.(3)模拟的1 h雨量与实况的对比表明,同时利用雷达反射率和径向风改进过的初始场能明显增强3 h内的降水强度和落区预报,改善中尺度数值模式短时定量降水预报.模拟的1 h流场对比分析表明,经雷达径向风调整后,能够在初始场中增加气旋性涡旋等中小尺度风信息,明显减少模式的spin-up时间.(4)通过对雷达径向风和反射率对模式初始场和模拟结果影响的对比分析发现,雷达径向风主要是改进初始风场,而雷达反射率主要是改进初始场中的湿度参数,增加初始场中云水等的含量,调整温度场.通过模拟的6 h降水对比发现,利用雷达径向风调整初始场后,对降水模拟有一定的改进,但效果不甚明显,而雷达反射率资料对定量降水预报改进效果明显,同时使用雷达径向风和反射率资料改进初始场后对降水的模拟效果最明显.     

多普勒雷达资料在中尺度模式短时预报中的应用

将国内新一代多普勒雷达基数据Lev-Ⅱ的9个仰角的原始反射率和径向风资料应用于中尺度模式ARPS(the Advanced Regional Prediction System),通过对2007年7月18日济南短时特大暴雨过程的个例研究,分析了雷达资料对初始场的改进效果及其对模拟结果的影响。结果表明,同时利用雷达反射率和径向风资料改进过的初始场能明显改善中尺度数值模式短时定量降水预报。对比风场发现,雷达径向风调整后在初始场中出现明显的中小尺度特征,能减少模式的spin-up时间。随着积分的进行,对水汽场的调节也有一定作用,但相对于风场调节不够显著。雷达反射率主要是改进初始场中的湿度参数,增加初始场中云水等含量。积分开始后,对风场调整也很显著。此次模拟,控制试验较好地预报出雷达回波的分布和水汽中心,对应的降水落区与实况也较为接近,但对雷达回波中心强度的预报略大,相应地降水量的预报也偏大。     

A Numerical Simulation Study of Typhoon Rananim（0414）

Using the high-resolution non-hydrostatic model ARPS (Advanced Regional Prediction System),the Typhoon Rananim (0414) was simulated by using the CINRAD Doppler radar data.The results before and after typhoon landfall show that model ARPS performs well to simulate the track,the variation of center pressure,as well as severe heavy rain of Rananim.Meanwhile,the simulated composite reflectivity was compared with the observed radar composite reflectivity.The numerical results reveal that the asymmetrical structure of Rananim plays an important role in its westward deflecting after landfall.The sensitivity simulation experiments of terrain effects on Rananim (0414) were also investigated,and the terrain of the southeastern China has important effects on Rananim turning right slightly of its track and increasing its intensity obviously,but when typhoon is far away from the coastline,the terrain only impacts slightly on the storm intensity during its landfall.The results show that topographic lifting contributes greatly to precipitation enhancement,and makes the distribution of precipitation more uneven.     

多普勒雷达资料对中尺度系统短期预报的改进

文中给了一种由单多普勒雷达资料反演风矢量场的变分方法 ,并由此形成预报初始场。运用美国风暴分析预报中心 ( CAPS)的改进的区域系统模式对移经美国俄克拉何马州的一个强雷暴系统进行了数值预报试验 ,结果表明 ,初始场中引入多普勒雷达观测资料后明显地改进了预报。     

用多普勒雷达反射率调整模式大气的云微物理变量

一种简单云分析方案, 用于由多普勒天气雷达反射率反演中尺度大气模式初值分析中的云微物理变量（云水混合比和雨水混合比）和空气湿度变量（比湿），使模式积分初始场反映出观测空间的云微物理特征以及哪些空间位置上的大气处于饱和状态。应用于2002年6月梅雨期安徽省马鞍山市一次降水过程的临近数值预报试验结果表明，模式预报的大气综合反射率与雷达观测的回波图像相近，由云微物理变量变化表示的模式云系演变与雷达观测的回波图像一致, 伴随模拟的中小尺度云系, 模式大气能很快调整出合理的中小尺度流场辐散、辐合结构；它们明显好于模式初始场不引入雷达反射率时的结果，即这种方法对改进临近数值天气预报准确率是有效的。     

0414号台风“云娜”的数值模拟

利用美国CAPS的非静力高分辨率区域预报模式(ARPS)对0414号台风"云娜"进行数值模拟,其中把新一代多普勒天气雷达资料分析引入模式,模拟结果表明ARPS模式能较好模拟台风"云娜"的移动路径、中心气压强度变化及台风大暴雨;并利用模式输出的组合反射率与雷达组合反射率资料比较来检验模拟结果.数值模拟结果表明台风自身结构是引起0414号台风西折路径的重要原因;浙南闽北地形对0414号台风的影响作地形敏感性试验结果表明浙南闽北地形使0414号台风移动路径出现右偏现象;浙南闽北地形对0414号台风强度影响较小,地形对深入内陆后台风强度变化有较明显的影响;浙南闽北地形对0414号台风暴雨有增幅作用,降水分布更加不均匀.     

物理初值化与短时强对流降水数值模拟研究

ADAS(ARPS Data Assim ilation System)云综合分析采用的是一种物理初值化技术。本文以ARPS(Advanced Regional Pred iction System)云综合分析为基础,设计了一组非常规资料的应用试验,应用ARPS模式对一次典型的短时强降水个例进行了数值模拟研究。结果表明:(1)在ADAS云分析中引入雷达及卫星资料可对湿度场的水平、垂直结构有一定的调整,改善了与对流降水有重要关系的高湿区的分析;(2)引入雷达和卫星资料的云综合分析,可以明显改善强降水发生时间相对迟缓以及模拟前期降水量偏少和降水覆盖面积偏小的现象,使模拟降水场与实际观测较为接近;(3)物理初值化能有效提取非常规资料中的云微物理信息,提高了模式初值质量,从而明显改善模式对强对流降水系统的短时模拟效果。     

天气雷达反射率资料订正前后在ARPS模式中的同化试验对比

以TRMM/PR反射率资料作参照,对常州、南京天气雷达反射率资料进行一致性订正,再利用中尺度模式ARPS及其数据同化系统ADAS对订正前、后的天气雷达反射率因子进行同化,模拟2013年6月25日和2010年8月24日江苏地区两次暴雨过程。两次暴雨过程均包括3组试验:控制试验和雷达资料订正前、后的同化试验。结果表明:(1)雷达反射率因子的同化很好地改进了模式初始湿度场,使降水预报在分布和强度上更接近实况;(2)雷达资料的订正进一步改进了反射率因子同化试验,并且通过调整初始湿度场和上升运动场(调整作用主要体现在前2~3 h内)改善了对降水的模拟预报,其结果证明雷达反射率因子的订正改善了雷达资料的质量。     

雷达资料同化在局地强对流预报中的应用

采用ARPS模式的资料分析系统ADAS同化多普勒雷达径向速度和反射率因子资料,分析两者对初始场的改进作用,并应用于WRF中尺度模式中对2012年8月21日江西省一次局地强对流过程进行了模拟试验。分析结果表明:（1）ADAS同化系统能够利用雷达径向速度和反射率因子资料有效增加初始场中的中小尺度风场信息和云、水物质含量,并通过湿绝热或非绝热初始化对温度场、湿度场和风场进行调整,使初始场在动力和热力上达到平衡。（2）同化径向速度后对改善模式初始场的动力场有重要贡献,而对大气水凝物和降水的预报影响较小;同化反射率因子的主要作用是调整初始场中的水凝物场和热力场,有效缩短了模式的“ spin-up”时间,明显改进了定量降水预报;同时同化雷达径向速度和反射率因子后,初始场中快速调整出了中小尺度风场水平辐合、垂直运动以及合理的温、湿分布,对3小时内雨带形状、降水落区及定量降水的预报与实况更接近。（3）模拟试验表明,同时同化径向速度和反射率因子能成功模拟出本次对流单体风暴的中β尺度三维空间分布结构及其演变过程,中低层切变线的辐合抬升强迫作用是对流单体风暴组织、发展和维持的主要动力机制之一,对流凝结潜热加热在对流单体风暴的发生发展中发挥了重要作用。因此,雷达资料同化对提高临近数值天气预报的准确率以及对强对流天气系统的模拟能力具有重要意义。     

雷达资料在登陆台风“桑美”数值模拟中的应用

将国内多普勒天气雷达的反射率因子及径向风资料引入ARPS-3DVar同化系统进行同化,针对2006年登陆浙江苍南并造成严重影响的超强台风“桑美”,探讨多普勒雷达资料同化对台风模拟初始场和预报场的改进作用。结果表明:利用ARPS-3DVar同化雷达资料可以明显改善6 h同化窗口内的降水、风场和回波结构,并能提高模式对中尺度雨团位置、强度的模拟能力;雷达资料初始场同化后模拟的台风涡旋和台风眼结构与位置更加接近实况,各物理量空间分布结构更具有明显中尺度特征,从而改善了台风路径和降水的预报。但模拟过程中台风强度模拟偏弱,有待进一步改进。     

Analysis and very short range forecast of cyclone “AILA” with radar data assimilation with rapid intermittent cycle using ARPS 3DVAR and cloud analysis techniques

In this study, both reflectivity and radial velocity are assimilated into the Weather Research and Forecasting (WRF) model using ARPS 3DVAR technique and cloud analysis procedure for analysis and very short range forecast of cyclone ÁILA. Doppler weather radar (DWR) data from Kolkata radar are assimilated for numerical simulation of landfalling tropical cyclone. Results show that the structure of cyclone AILA has significantly improved when radar data is assimilated. Radar reflectivity data assimilation has strong influence on hydrometeor structures of the initial vortex and precipitation pattern and relatively less influence is observed on the wind fields. Divergence/convergence conditions over cyclone inner-core area in the low-to-middle troposphere (600–900 hPa) are significantly improved when wind data are assimilated. However, less impact is observed on the moisture field. Analysed minimum sea level pressure (SLP) is improved significantly when both reflectivity and wind data assimilated simultaneously (RAD-ZVr experiment), using ARPS 3DVAR technique. In this experiment, the centre of cyclone is relocated very close to the observed position and the system maintains its intensity for longer duration. As compared to other experiments track errors are much reduced and predicted track is very much closer to the best track in RAD-ZVr experiment. Rainfall pattern and amount of rainfall are better captured in this experiment. The study also reveals that cyclone structure, intensification, direction of movement, speed and location of cyclone are significantly improved and different stages of system are best captured when both radar reflectivity and wind data are assimilated using ARPS 3DVAR technique and cloud analysis procedure. Thus optimal impact of radar data is realized in RAD-ZVr experiment. The impact of DWR data reduces after 12 h forecast and it is due to the dominance of the flow from large-scale global forecast system model. Successful coupling of data assimilation package ARPS 3DVAR with WRF model for Indian DWR data is also demonstrated.     

云分析系统在台风莫拉菲数值模拟中的应用

借鉴美国CAPS开发的中尺度ARPS模式的资料分析系统ADAS,设计了基于GRAPES_Meso的云分析系统,实现了地面云观测资料、卫星云图、多普勒雷达反射率资料在GRAPRS模式中的综合融合应用。通过对登陆台风莫拉菲 (0906) 数值模拟检验云分析系统的性能。结果表明:云分析系统通过地面云观测资料、卫星红外云图、可见光云图、多普勒雷达反射率的同化,能够反演出合理的三维云覆盖状况;在三维云覆盖的基础上结合云底云顶高度,进而反演出云水、云冰、雨水、雪、霰等云微物理量,并显著改善模式初始湿度场;在台风登陆过程的模拟中,虽然对台风路径的预报有所偏差,但对比控制试验,在台风登陆地点、台风强度、路径的预报中云分析发挥了正作用;通过对云分析后初始场反演的雷达回波进行检验,验证了云分析的可行性与正确性,对整体的降水预报效果也有明显改善。     

Radar data assimilation of the GRAPES model and experimental results in a typhoon case

Constructing β-mesoscale weather systems in initial fields remains a challenging problem in a mesoscale numerical weather prediction (NWP) model. Without vertical velocity matching the β-mesoscale weather system, convection activities would be suppressed by downdraft and cooling caused by precipitating hydrometeors. In this study, a method, basing on the three-dimensional variational (3DVAR) assimilation technique, was developed to obtain reasonable structures of β-mesoscale weather systems by assimilating radar data in a next-generation NWP system named GRAPES (the Global and Regional Assimilation and Prediction System) of China. Single-point testing indicated that assimilating radial wind significantly improved the horizontal wind but had little effect on the vertical velocity, while assimilating the retrieved vertical velocity (taking Richardson's equation as the observational operator) can greatly improve the vertical motion. Experiments on a typhoon show that assimilation of the radial wind data can greatly improve the prediction of the typhoon track, and can ameliorate precipitation to some extent. Assimilating the retrieved vertical velocity and rainwater mixing ratio, and adjusting water vapor and cloud water mixing ratio in the initial fields simultaneously, can significantly improve the tropical cyclone rainfall forecast but has little effect on typhoon path. Joint assimilating these three kinds of radar data gets the best results. Taking into account the scale of different weather systems and representation of observational data, data quality control, error setting of background field and observation data are still requiring further in-depth study.     

Short-range prediction of a heavy precipitation event by assimilating Chinese CINRAD-SA radar reflectivity data using complex cloud analysis

Summary With the ARPS (Advanced Regional Prediction System) Data Analysis System (ADAS) and its complex cloud analysis scheme, the reflectivity data from a Chinese CINRAD-SA Doppler radar are used to analyze 3D cloud and hydrometeor fields and in-cloud temperature and moisture. Forecast experiments starting from such initial conditions are performed for a northern China heavy rainfall event to examine the impact of the reflectivity data and other conventional observations on short-range precipitation forecast. The full 3D cloud analysis mitigates the commonly known spin-up problem with precipitation forecast, resulting a significant improvement in precipitation forecast in the first 4 to 5 hours. In such a case, the position, timing and amount of precipitation are all accurately predicted. When the cloud analysis is used without in-cloud temperature adjustment, only the forecast of light precipitation within the first hour is improved. Additional analysis of surface and upper-air observations on the native ARPS grid, using the 1 degree real-time NCEP AVN analysis as the background, helps improve the location and intensity of rainfall forecasting slightly. Hourly accumulated rainfall estimated from radar reflectivity data is found to be less accurate than the model predicted precipitation when full cloud analysis is used.     

Evaluation of radar and automatic weather station data assimilation for a heavy rainfall event in southern China

To improve the accuracy of short-term(0–12 h) forecasts of severe weather in southern China, a real-time storm-scale forecasting system, the Hourly Assimilation and Prediction System(HAPS), has been implemented in Shenzhen, China. The forecasting system is characterized by combining the Advanced Research Weather Research and Forecasting(WRF-ARW)model and the Advanced Regional Prediction System(ARPS) three-dimensional variational data assimilation(3DVAR) package. It is capable of assimilating radar reflectivity and radial velocity data from multiple Doppler radars as well as surface automatic weather station(AWS) data. Experiments are designed to evaluate the impacts of data assimilation on quantitative precipitation forecasting(QPF) by studying a heavy rainfall event in southern China. The forecasts from these experiments are verified against radar, surface, and precipitation observations. Comparison of echo structure and accumulated precipitation suggests that radar data assimilation is useful in improving the short-term forecast by capturing the location and orientation of the band of accumulated rainfall. The assimilation of radar data improves the short-term precipitation forecast skill by up to9 hours by producing more convection. The slight but generally positive impact that surface AWS data has on the forecast of near-surface variables can last up to 6–9 hours. The assimilation of AWS observations alone has some benefit for improving the Fractions Skill Score(FSS) and bias scores; when radar data are assimilated, the additional AWS data may increase the degree of rainfall overprediction.     

风廓线与测风塔资料在地面风场预报中的应用研究

运用四维同化方法将风廓线雷达和测风塔资料应用到WRF模式中,通过对比资料同化前后模式对地面风场的预报效果可知,加入风廓线雷达和测风塔资料后模式对风速的预报效果有明显提高,对风向的预报也有一定程度的改善;资料同化结束后,模式预报在49h内对地面风场预报效果仍有明显改善,但随着模式预报时间的增加,在模式积分49h以后,同化资料前后模式对地面风场的预报效果无明显变化。另外,通过对资料同化前后模式对风速预报误差的分析可知,在对模式风场预报的改进中风廓线雷达资料的贡献大于测风塔资料的贡献。     

雷达资料同化对一次飑线过程的模拟影响

雷达资料同化能够改善强对流天气的预报,但是不同的模式方案配置会得到不同的结果。本文针对中国南部2018年3月4日一次飑线过程,以全球预报模式GFS分析场和预报场为背景场,采用中尺度区域气象预报模式ARPS 3DVAR系统同化多普勒雷达径向速度,用云分析处理反射率数据,考虑同化间隔、频次、云分析中不同参数调整,采用1 h同化窗口,设计不同同化方案,最后用WRF模式进行预报,研究雷达资料同化对飑线系统触发及发展机制的影响。结果表明,同化间隔过短时,由于模式热动力变量没有平衡产生虚假回波,同化间隔过长时,系统触发和发展的特征普遍偏弱;采用12 min间隔同化得到了最好的初始场,并且同化频次越高得到的降水预报结果越好。此外,ARPS云分析能大大改善初始场,减少模式自调整时间,其中湿度调整、温度调整、雨水调整及水汽调整对系统动力过程和水凝物初始场分布都有较大的影响,而垂直速度相关参数调整影响较小。     

双多普勒雷达风场反演资料的同化应用试验

利用合肥、马鞍山2部多普勒雷达联合观测资料及多部多普勒雷达合成和连续调整技术（MUSCAT）,在笛卡尔坐标下反演得到三维风场。将得到的风场通过由CAPS（center for analysis and prediction of storm）研发的ARPS（advancedregional prediction systemv5．2．4）模式及数据处理系统ADAS（ARPS data analysissystem）进行同化试验。结果表明,反演风场信息可以改善短时模拟场、特别是风场的状况;但由于对湿度场等的改进较小,致使预报的3h降水量与实况有出入。     

台风模拟中QuikSCAT风场的非对称加强订正及同化应用

针对QuikSCAT 散射计风场资料对台风级别的强风速区还无法准确刻画这个技术问题,引入台风移速、移向、摩擦等客观因素对该风场产品资料进行非对称加强订正;然后利用四维同化技术,并设计了先同化后预报两个阶段的试验方案,对“云娜”台风进行了数值模拟试验。试验结果表明：通过模式约束调整,四维同化技术可以把QuikSCAT 海面风场资料引入到整层模式大气中,并对整层风场和气压场的强度及路径模拟都有很大的改进,而且该资料还提高了预报阶段台风强度的模拟效果,并对登陆点的预报起到了积极作用。