A Case Study of the Error Growth and Predictability of a Meiyu Frontal Heavy Precipitation Event

The Advanced Regional Eta-coordinate Model (AREM) is used to explore the predictability of a heavy rainfall event along the Meiyu front in China during 3-4 July 2003.Based on the sensitivity of precipitation prediction to initial data sources and initial uncertainties in different variables,the evolution of error growth and the associated mechanism are described and discussed in detail in this paper.The results indicate that the smaller-amplitude initial error presents a faster growth rate and its growth is characterized by a transition from localized growth to widespread expansion error.Such modality of the error growth is closely related to the evolvement of the precipitation episode,and consequcntly remarkable forecast divergence is found near the rainband,indicating that the rainfall area is a sensitive region for error growth.The initial error in the rainband contributes significantly to the forecast divergence,and its amplification and propagation are largely determined by the initial moisture distribution.The moisture condition also affects the error growth on smaller scales and the subsequent upscale error cascade.In addition,the error growth defined by an energy norm reveals that large error energy collocates well with the strong latent heating,implying that the occurrence of precipitation and error growth share the same energy source-the latent heat.This may impose an intrinsic predictability limit on the prediction of heavy precipitation.     

The energetics of error-growth and the predictability analysis in precipitation prediction

Sensitivity simulations are conducted in AREM (Advanced Regional Eta-Coordinate numerical heavy-rain prediction Model) for a torrential precipitation in June 2008 along South China to investigate the effect of initial uncertainty on precipitation predictability. It is found that the strong initial-condition sensitivity for precipitation prediction can be attributed to the upscale evolution of error growth. However, different modality of error growth can be observed in lower and upper layers. Compared with lower-level, significant error growth in the upper-layer appears over both convective area and high jet stream. It thus indicates that the error growth depends on both moist convection due to convective instability and the wind shear associated with dynamic instability. As heavy rainfall process can be described as a series of energy conversion, it reveals that the advection-term and latent heating serve as significant energy sources. Moreover, the dominant source terms of error-energy growth are nonlinearity advection (ADVT) and difference in latent heating (DLHT), with the latter being largely responsible for the rapid error growth in the initial stage. In this sense, the occurrence of precipitation and error-growth share the energy source, which implies the inherent predictability of heavy rainfall. In addition, a decomposition of ADVT further indicates that the flow-dependent error growth is closely related to the atmospheric instability. Thus the system growing from unstable flow regime has its intrinsic predictability.     

一次江淮梅雨锋暴雨的数值模拟可预报性研究

利用中尺度非静力MM5模式研究不同初始扰动（误差）对2003年7月4—5日发生在江淮流域的一次梅雨锋暴雨数值预报不确定性的影响,并着重分析了提前36h定量降水的可预报性。结果表明,利用常规观测资料和NCEP/NCAR分析资料形成初始场的控制试验能够提前36h做出较好的模拟。扰动温度场的敏感性试验表明,扰动温度的均方差愈大,降水预报不确定性也愈大。误差演变特征和增长机制分析表明,误差增长具有升尺度特征,误差首先在对流层低层和高层增长,然后大值区向对流层中层扩展;湿降水过程是对流层中低层误差增长的主要机制;对流层高层的误差增长是大气干动力与湿过程共同作用的结果,前期以干过程为主,后期以湿过程为主。     

基于SAL方法对一次区域性大暴雨过程多模式预报空间检验及误差分析

采用SAL定量降水预报检验方法,对2017年梅雨期一次区域性极端降水过程EC-THIN、RIOF、NCEP、CMA的高分辨率数值预报产品,从结构、强度和位置3个方面进行检验对比,同时对72 h内各模式降水预报稳定性开展检验分析。在此基础上,剖析了降水预报误差成因。分析发现：（1）在降水分布上,RIOF、EC-THIN和CMA预报的雨带走向与实况基本一致,NCEP预报主雨带范围偏大,暴雨区偏东;（2）雨区结构上RIOF和EC-THIN把握较好,NCEP和CMA在降水强度方面预报较好,位置预报上各家误差均较小,其中CMA误差最小;（3）EC-THIN和NCEP在结构、强度和位置预报上均有较好的稳定性。CMA在降水强度方面预报稳定较好,位置预报上调整较大。RIOF在降水结构预报上稳定性较好,落区预报上变化幅度较大;（4）降水预报误差根本原因是由系统预报误差而形成,系统强度、位置、移动直接影响着降水偏差。垂直物理量的预报偏差对降水时段、加强、强度也具有一定影响。     

Mesoscale predictability of mei-yu heavy rainfall

Recently reported results indicate that small amplitude and small scale initial errors grow rapidly and subsequently contaminate short-term deterministic mesoscale forecasts. This rapid error growth is dependent on not only moist convection but also the flow regime. In this study, the mesoscale predictability and error growth of mei-yu heavy rainfall is investigated by simulating a particular precipitation event along the mei-yu front on 4-6 July 2003 in eastern China. Due to the multi-scale character of th...     

初始扰动对一次华南暴雨预报的影响的研究

本文选取了2006年华南前汛期的一次暴雨过程, 采用AREMv2.3中尺度数值模式进行数值模拟, 分别在模式初始场的物理量场 (温度场、 风场、 湿度场) 上加扰动, 分析不同物理量场上的扰动对降水预报的影响, 以及物理量预报误差和扰动能量的增长情况。同时, 通过本个例讨论误差增长与湿对流的关系, 扰动振幅对误差增长的影响和华南区域的中尺度降水的可预报性问题。数值试验结果表明: 初始时刻不同物理量场加实际振幅的正态分布的随机扰动时, 对降水的影响是不同的。对于24小时降水预报, 温度场对降水的影响最大。误差的增长与湿对流不稳定有着密切的关系。小尺度小振幅误差增长很快, 而且是非线性增长。这意味着短期的较小尺度降水的可预报性很小。与大振幅扰动相比, 小振幅扰动造成的误差较小。但是小振幅扰动的迅速发展, 很快就会对降水预报造成较大的影响。因此, 只能有限地提高预报质量, 而且由于扰动非线性增长很快, 在预报时间的提前上, 不会有太大的改善。     

梅雨期暴雨系统的流依赖中尺度可预报性

中尺度天气系统的初值敏感性,导致了中尺度系统预报极限的存在.中尺度系统的初始误差的快速增长及其中尺度可预报性依赖于系统流的特征.梅雨暴雨形成是多尺度天气系统共同作用的结果,决定了梅雨期暴雨的形成机制的多样性,也决定了其初值敏感性的差异性.本文重点对比分析了五种不同类型的梅雨暴雨的误差增长特征及其机制.冷空气抬升、低层涡...     

2021年7月17日长江中下游地区β中尺度低涡及其模式预报不确定性分析

2021年7月16—17日,在大尺度鞍型背景场中长江中下游地区生成了准静止的β中尺度低涡系统,造成苏皖地区出现局地特大暴雨及雷暴大风天气。欧洲中心（EC）控制预报对低涡位置的描述较实况明显偏北,由此在降水预报中也呈现出较大偏差,给预报决策带来较大误导。采用EC控制和集合预报产品,并基于“预报挑战度（MFC）”和“可预报性演变指数（PHDX）”等客观方法对低涡及降水预报不确定性进行分析,并在此基础上探讨模式偏差成因,得到以下结论：（1）对流层低层低涡东侧西南气流和东南气流的辐合以及低空急流的水平涡度输送是低涡发展的主要动力因素,而低涡东侧和南侧降水的潜热释放则构成低涡发展的热力因素;（2）EC控制预报不同起报时次均出现低涡位置偏北及雨带预报偏北现象,其集合预报产品离散度无法覆盖实况降水,揭示了此次过程的低可预报性,MFC和PHDX则能够客观指示此次过程低可预报性;（3）前期模式对低涡南侧西南气流南风分量预报偏大及对东侧辐合区刻画偏北造成前期东段降水偏北,而后在潜热释放、低空急流与低涡正反馈机制影响下,偏北的降水区造成低涡进一步预报偏北,最终导致整个时段预报较观测呈现巨大差异。     

云可分辨模式中误差的增长和传播对定量降水可预报性的影响

Limitations in the predictability of quantitative precipitation forecasting （QPF） that arise from initial errors of small amplitude and scale are investigated by means of real-case high-resolution （cloud-resolving） numerical weather prediction （NWP） integrations. The case considered is the hail and wind disaster that occurred in Sichuan on 8 April 2005. A total of three distinct perturbation methods are used. The results suggest that a tiny initial error in the temperature field can amplify and influence the weather in a large domain, changing the 12-h forecasted rainfall by as much as one-third of the original magnitude. Furthermore, the comparison of the perturbation methods indicates that all of the methods pinpoint the same region （the heavy rainfall areas in the control experiment） as suffering from limitations in predictability. This result reveals the important role of nonlinearity in severe convective events.     

THE IMPACT OF DIFFERENT PHYSICAL PROCESSES AND THEIR PARAMETERIZATIONS ON FORECAST OF A HEAVY RAINFALL IN SOUTH CHINA IN ANNUALLY FIRST RAINING SEASON

An ensemble prediction system based on the GRAPES model, using multi-physics, is used to discuss the influence of different physical processes in numerical models on forecast of heavy rainfall in South China in the annually first raining season(AFRS). Pattern, magnitude and area of precipitation, evolution of synoptic situation, as well as apparent heat source and apparent moisture sink between different ensemble members are comparatively analyzed. The choice of parameterization scheme for land-surface processes gives rise to the largest influence on the precipitation prediction. The influences of cumulus-convection and cloud-microphysics processes are mainly focused on heavy rainfall;the use of cumulus-convection parameterization tends to produce large-area and light rainfall. Change in parameterization schemes for land-surface and cumulus-convection processes both will cause prominent change in forecast of both dynamic and thermodynamic variables, while change in cloud-microphysics processes show primary impact on dynamic variables. Comparing simplified Arakawa-Schubert and Kain-Fritsch with Betts-Miller-Janjic schemes, SLAB with NOAH schemes, as well as both WRF single moment 6-class and NCEP 3-class with simplified explicit schemes of phase-mixed cloud and precipitation shows that the former predicts stronger low-level jets and high humidity concentration, more convective rainfall and local heavy rainfall, and have better performance in precipitation forecast. Appropriate parameterization schemes can reasonably describe the physical process related to heavy rainfall in South China in the AFRS, such as low-level convergence, latent heat release, vertical transport of heat and water vapor, thereby depicting the multi-scale interactions of low-level jet and meso-scale convective systems in heavy rainfall suitably, and improving the prediction of heavy rainfall in South China in the AFRS as a result.     

DRP-4DVar方法同化AIRS反演资料在一次江淮流域暴雨中的应用

利用经济省时的降维投影四维变分同化方法(DRP-4DVar),在2009年7月22～23日江淮流域的一次大暴雨过程中同化晴空条件下高光谱大气红外探测仪(AIRS)反演温度、湿度廓线,改进此次强降水过程的模拟。试验结果分析显示,同化AIRS反演的温度及湿度场后,基于四维变分同化系统的模式约束,能够改进湿度场、高度场、高低层散度场。从累积降水量偏差图及同化试验增量图可以看到,正降水量偏差对应于正湿度增量、负位势高度增量及低层负散度高层正散度增量,负降水量偏差则与之相反。同化试验较参照试验可更好地模拟出暴雨的天气形势、对暴雨的落区及强度有更好的反映。此外,从单次同化与连续同化的试验对比结果看出,连续同化试验结果较单次同化结果有进一步的改进,说明不断加入新的观测资料可以更好地模拟强降水过程。     

潜热加热纳近方法在地面降水资料同化中的应用

基于国家气象中心GRAPES_Meso高分辨率区域模式,针对中尺度数值预报模式中预报雨带形成滞后问题,研究了潜热加热纳近方法在地面降水资料同化中的应用,以期提高短时数值天气预报的水平。2013年6月20日—7月20日的初步试验结果表明：通过调整模式潜热加热廓线,可以改进初始场中温、湿、风等要素的合理分布,增加降水区的对流不稳定性;潜热加热纳近方法可以缩短模式的调整适应 (spin-up) 时间,改进短时降水预报的落区和强度,提高3 h,6 h,12 h的降水预报TS,ETS评分;与传统的冷潜热加热纳近的试验结果相比,改进的暖潜热加热纳近试验对降水落区和强度的预报更接近观测,但强降水中心范围略大。     

华南暖区暴雨研究进展

2016年4月22日06时中央气象台对中国南方地区发布24 h大暴雨、暴雨预报,但实况却以小雨天气为主。本文利用常规探空和地面观测站资料、ERA-Interim再分析资料和ECMWF预报资料,对这次个例空报的原因进行了探讨,同时用同一地区另一出现了暴雨的个例作对比分析。结果表明:高空低槽东移加深,槽前西南急流发展是我国南方暴雨常见的一种天气形势,但22日08时—23日08时西南急流中低层存在辐散,且比湿值小、湿度层浅薄,没有上下一致的上升运动,水汽通量辐合高度偏低,动力和水汽条件均不利于出现较强降水;21日20时—22日08时华南地区出现一次短历时强对流天气后,K指数、对流有效位能(CAPE)和Si指数持续偏低,假相当位温(θ_se)等值线密集区(锋区)偏北,不稳定能量弱,能量条件不利于继续出现较强降水。数值模式对这次过程存在较大预报误差,但700 hPa以上较低相对湿度不利于出现大暴雨天气,一定程度上能对降水落区进行订正。分析对照个例表明,如果中低层存在弱辐散场,但水汽条件较好,也能出现较强降水;因此,从华南槽前类暖区暴雨环境场看,中低层较高比湿(高于平均值2~3 g·kg^-1)、较好的边界层触发条件、较深厚的上升气流与更强对流不稳定都可能是我国南方春季暖区暴雨重要的预报思路。

     

集合动力因子对登陆台风“莫拉克”(0908)暴雨 落区的诊断与预报研究

“莫拉克”是2009年登陆我国热带气旋中影响范围最广、造成损失最大的台风.“莫拉克”带来的强降水导致台湾南部发生50年来最严重的水灾,福建、浙江等省的部分站点过程雨量超过50年一遇.因此,在台风暴雨(强降水)预报中,能否准确把握其落区就显得尤为重要.本文首先利用中尺度非静力数值模式WRF对台风“莫拉克”进行高分辨率数值模拟(三层嵌套,最高分辨率为2 km).模式较好地再现了台风中心的移动路径、强度;模拟的降水分布区域与实况也较为相符.利用再分析资料及模拟的高分辨率资料对暴雨成因进行诊断分析,表明造成此次强降水过程的水汽主要由西南季风输送,并且垂直运动旺盛,贯穿整个对流层.根据集合动力因子预报方法,运用广义湿位温、对流涡度矢量垂直分量及水汽散度通量对暴雨落区进行了诊断和预报,发现广义湿位温等值线的“漏斗状”区域与暴雨落区对应关系显著;基于NCEP-GFS每日四次的预报场资料,利用对流涡度矢量和水汽散度通量做出的降水落区预报表明,二者对降水落区均有一定的指示意义.强降水主要位于对流层中低层对流涡度矢量垂直积分量的梯度大值区附近,其时间演变与观测降水的演变具有相当高的一致性;水汽通量散度抓住了垂直运动和水汽散度这两个引发暴雨的关键因子,对降水的发生范围和强降水极值中心的判断更为准确.这三个动力因子都可以为“莫拉克”台风暴雨(强降水)落区提供信号,对台风暴雨落区具有一定的诊断和预报意义.     

定量降水预报技术进展

采用ECMWF interim再分析资料、ECMWF集合预报资料、自动站降水资料,运用“集合异常预报法”和“EFI（Extreme Forecast Index）阈值法”对浙江东部一次台风远距离极端降水的成因和可预报性进行探讨,结果表明：（1）台风倒槽长期存在,在北侧冷空气入侵作用下在浙江沿海地区引起锋生,并在倒槽顶端诱生低压环流,是造成浙江东北部大暴雨的主要原因。（2）36 h时效的集合预报显示,大气可降水量、850 hPa南风分量、925 hPa水汽通量和200 hPa辐散都超过气候平均值3~4个标准差,且超过3个标准差的概率也达到了70%~90%,表明浙江沿海有优越的动力和充沛的水汽条件,异常强烈的天气信号,预示发生极端降水为高概率事件。（3）随着预报时效的延长,各物理量异常概率会明显减小,因此在判断极端天气发生可能性时应降低预警的概率阈值。（4）不同时效降水的EFI对95%和99%百分位事件的预报效果均很好,可以比确定性预报提前3~4 d提供极端降水信号。EFI指数所反映的极端降水信息较确定性模式更加可靠且稳定,在决策服务中具有更高的参考价值。（5）“集合异常预报法”可以获得与极端天气相关的形势场和物理量的异常概率,而EFI在长时效的预报更具优势,两者结合、优势互补,可以为预报员提供更全面的极端天气信号。

     

陆面特征量初始扰动的敏感性及集合预报试验

文章利用中尺度模式Weather Research and Forecasting Model(WRF)3.2.1版本及National Centers for Environmental Prediction(NCEP)分析资料,研究了陆面变量(土壤湿度、土壤温度)和陆面参数(植被覆盖率)初始场随机扰动对长江中下游暴雨预报的影响并进行了集合预报试验。试验结果表明,短期暴雨过程对陆面变量(参数)扰动是敏感的;陆面变量(参数)初始场扰动影响降水的时间尺度小于10 h甚至可以小于6 h。从影响机理上来看,陆面变量(参数)扰动首先改变地表的潜热通量和感热通量,而地表通量的改变会通过陆气相互作用对局地大气的温、压、湿、风产生较大影响,从而对暴雨的强度和落区产生较大影响。集合预报结果表明,利用陆面变量(参数)扰动制作集合预报,预报的集合平均结果要好于控制预报的结果,且比集合成员稳定可靠,降水概率预报可以提供一些有用的信息,对预报强降水有一定的指示意义。在初值集合预报中,以这些参数或变量的扰动来引进集合成员是十分有意义的。     

台风"灿都"造成云南强降水过程的水汽螺旋度诊断分析

利用地面加密观测、Micaps资料和NCEP1°×1°再分析资料对1003号"灿都"台风造成云南暴雨进行诊断分析。结果表明:台风低压为高温高湿且具有强对流不稳定的深厚系统。进入云南后除了自身携带的大量水汽和能量外,先后有副热带高压西侧强盛偏南急流和孟加拉湾西南气流卷入,使得台风低压在云南持久不衰,并产生全省性强降水。诊断量"水汽螺旋度"对暴雨落区和强度有较好的对应关系,强降水多发生在水汽螺旋度正值中心的偏南侧。"水汽螺旋度"随时间变化的两个影响因子"螺旋度通量散度"和"湿螺旋度散度"对强降水的落区和强度也有较好的指示作用。若是分别对两个因子进行诊断,再综合分析环流形势,将能达到更好的强降水预报效果。     

乌鲁木齐区域数值预报业务系统降水预报检验与评估分析

基于乌鲁木齐区域数值预报业务系统,运用Ts和Bias评分方法,对2012年9月1日—2015年8月31日逐日2个起报时次的逐6 h累积降水量的年与季节预报性能进行检验,并从空间上分析了2015年全疆站点逐6 h累积降水量在4个预报时段的评分特征。结果表明:(1)2个起报时次的降水评分相差较小,00 UTC起报略优于12 UTC起报,2015年系统改进了白天大量级降水的空报现象。(2)系统对晴雨预报较为准确,Bias接近1,空报、漏报率很小;随着降水阈值的升高,Ts评分减小,Bias变幅增大,空、漏报率也随之增加。系统对强降水过程以漏报为主。(3)系统的降水预报能力存在季节差异,夏季Ts评分最高,秋季次之,冬季最小;随时间模式对四季降水预报能力均有提高,降低了冬季大量级降水的漏报率和夏季大量级降水的空报率。(4)在新疆地区,08—14 BT(Beijing Time)、14—20 BT、20—次日02 BT空报站点数多于漏报,14—20 BT空报率最高;在02—08 BT整体呈漏报。(5)各站点整体来看,白天Ts评分高于夜间,山区及邻近地区评分高于平原地区;西天山评分略优于东天山,夜间晴雨预报有天山北坡漏报、南坡空报的趋势。     

初值不确定性对暴雨数值预报影响的初步研究

为了研究初值不确定性对我国南方暴雨数值预报的影响,依据历史降水资料的统计分型,选取华南暴雨型、江淮暴雨型和黄淮暴雨型等三类型中具有代表性的暴雨过程,采用AREM模式以不同资料分析场(常规观测资料、NCEP和ERA40同化分析资料)作为初值进行暴雨预报对比试验。试验结果表明,对于各类型暴雨,不同资料初值的差异都会引起暴雨数值预报结果的较大差异;在此基础上,采用经验正交函数(EOF)分析方法,分析了各类暴雨初值误差及其变化信息,从中提取出主要的误差分量,并对这类误差的数值预报影响进行了敏感性试验和预报验证,结果显示,寻找和消除主要的初值误差,对改进暴雨数值预报结果具有重要作用。     

IMPACTS OF ATOVS DATA ASSIMILATION ON PREDICTION OF A RAINSTORM OVER SOUTHEAST CHINA

Using the Advanced Research WRF (ARW WRF) model and the Gridpoint Statistical Interpolation (GSI) three-dimensional variational analysis (3DVAR) system, the impact of assimilating ATOVS (Advanced TIROS Operational Vertical Sounder) radiance through the prototype Community Radiative Transfer Model (pCRTM) is evaluated on the forecasting of a heavy rainstorm occurring over the central Guangdong province in the southeast of China on 20-21 June 2005. A pair of comparison experiments (NODA and DA) for this case is conducted with multiple configurations, including nesting domains with 4-km and 12-km grid distances. The results showed that by changing the initial condition through data assimilation, a modified divergence and moisture field with the structure of dipoles has been added to the axis of the rainband with a southwest-northeast orientation. When more moisture carried by a southwesterly low level jet (LLJ) was converged into the northeast portion of the rainband around the observatory station of Longmen, the amplitude of moisture static energy (MSE) increased substantially at low levels much more than at middle levels, resulting in the enlarging of differences in MSE between 500 hPa and 850 hPa; the atmosphere became more unstable. Consequently, the convective rainfall increased in the northeast part of the province around the Longmen station, which was consistent with the observed distribution of rainfall.