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.     

一次梅雨暴雨预报中的误差演变及可预报性分析

针对2003年7月3—4日淮河流域梅雨暴雨过程,利用AREM模式,在分析暴雨预报对不同来源的初始资料和不同要素初始误差的敏感性的基础上,重点研究了降水过程中误差的演变特征和发展机理。分析结果表明,初始小振幅误差增长最快,而伴随着降水的发生和发展,误差演变特征表现为由局地增长发展为全局传播的过程,且误差最优增长总是出现于雨区,这意味着雨带是误差增长的敏感区域。雨区内存在的初始误差对降水预报误差具有重要贡献,初始湿度条件不仅影响误差的传播特征,还使雨带上中小尺度误差迅速增长并造成更大尺度的误差。基于误差能量公式的计算结果表明,误差增长的能量来源主要由凝结加热提供,因此,从能量角度而言,误差增长和降水增大是同"源"的,从而使暴雨可预报性受到固有的限制。     

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...     

Mesoscale predictability of moist baroclinic waves: Variable and scale-dependent error growth

This study seeks to quantify the predictability of different forecast variables at various scales through spectral analysis of the difference between perturbed and unperturbed cloud-permitting simulations of idealized moist baroclinic waves amplify- ing in a conditionally unstable atmosphere. The error growth of a forecast variable is found to be strongly associated with its reference-state （unperturbed） power spectrum and slope, which differ significantly from variable to variable. The shallower the reference state spectrum, the more spectral energy resides at smaller scales, and thus the less predictable the variable since the error grows faster at smaller scales before it saturates. In general, the variables with more small-scale components （such as vertical velocity） are less predictable, and vice versa （such as pressure）. In higher-resolution simulations in which more rigorous small-scale instabilities become better resolved, the error grows faster at smaller scales and spreads to larger scales more quickly before the error saturates at those small scales during the first few hours of the forecast. Based on the reference power spectrum, an index on the degree of lack （or loss） of predictability （LPI） is further defined to quantify the predictive time scale of each forecast variable. Future studies are needed to investigate the scale- and variable-dependent predictability under different background reference flows, including real case studies through ensemble experiments.     

我国南方持续性暴雨成因的TBB场分析

利用多种资料分析了2014年7月13—17日贵州持续性暴雨过程的中尺度环境场特征及贵阳极端降水成因,并以多个时次不同要素资料进行合成分析,构建此次梅雨锋西段持续性暴雨的天气学模型。结果表明：(1)此次贵州持续性暴雨发生在单阻型梅雨稳定的背景下,当地持续3~4 d的强降水由中低层低涡切变、低空急流及地面静止锋(梅雨锋)共同作用造成。(2)梅雨锋雨带的建立、维持及移动造成贵州不同区域出现强降水。此次过程梅雨锋雨带对贵州的影响分四个阶段,其中,第三阶段梅雨锋西段缓慢南压过程中多个β中尺度云团更替、合并及缓慢移动造成贵阳及周边部分县市降水量突破历史极值。(3)中低纬度系统相互作用使水汽输送异常偏强。7月16日白天当年第9号超强台风“威马逊”进入我国南海海面后促使副热带高压西侧向北输送的水汽加强,该水汽与来自孟加拉湾的强盛西南暖湿气流在贵州上空汇合、加强,形成异常偏强的水汽通量及水汽辐合中心,这可能是贵阳极端降水发生的重要原因。(4)相比2010—2014年5—9月贵阳发生的另外4场大暴雨过程,该过程更长的降水持续时间可能是贵阳极端降水发生的另一重要原因。(5)贵阳强降水期间,强降水的雷达回波表现为层状云-积云混合降水回波,并具有低质心暖云降水特征,同时径向速度图上可见强劲西南急流及中尺度气旋性辐合。

     

初值及物理过程对\

利用国防科技大学全球中期数值天气预报模式（YinHe Global Spetral model,YHGS）产品驱动WRF对2018年7月4日华中地区暴雨过程进行模拟,并与ERA-interim资料作初始场模拟结果对比,评估YHGS模式产品在此次暴雨过程预报中的应用能力。结果表明：（1）WRF-YHGS对2018年7月4日华中地区暴雨过程有一定的预报能力,其模拟的大尺度环流形势、水汽收支量变化趋势与WRF-ERA有着很好的一致性,YHGS模式产品驱动中尺度数值预报是可行的。（2）WRF-YHGS模拟效果较WRF-ERA差,但大雨量级WRF-ERA湿偏差较大,两组试验各物理量模拟结果存在一定差距,且随着积分时间的增加差异逐渐增大。（3）WRF-YHGS、WRF-ERA模拟结果的差异主要来自YHGS与ERA初始场中差异较大的次天气尺度运动和YHGS全球模式预报场误差两个方面。

     

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...     

Role of Parameter Errors in the Spring Predictability Barrier for ENSO Events in the Zebiak–Cane Model

ABSTRACT The impact of both initial and parameter errors on the spring predictability barrier （SPB） is investigated using the Zebiak Cane model （ZC model）. Previous studies have shown that initial errors contribute more to the SPB than parameter errors in the ZC model. Although parameter errors themselves are less important, there is a possibility that nonlinear interactions can occur between the two types of errors, leading to larger prediction errors compared with those induced by initial errors alone. In this case, the impact of parameter errors cannot be overlooked. In the present paper, the optimal combination of these two types of errors [i.e., conditional nonlinear optimal perturbation （CNOP） errors] is calculated to investigate whether this optimal error combination may cause a more notable SPB phenomenon than that caused by initial errors alone. Using the CNOP approach, the CNOP errors and CNOP-I errors （optimal errors when only initial errors are considered） are calculated and then three aspects of error growth are compared： （1） the tendency of the seasonal error growth; （2） the prediction error of the sea surface temperature anomaly; and （3） the pattern of error growth. All three aspects show that the CNOP errors do not cause a more significant SPB than the CNOP-I errors. Therefore, this result suggests that we could improve the prediction of the E1 Nifio during spring by simply focusing on reducing the initial errors in this model.     

The Mesoscale Predictability of a Heavy Precipitation Event

Using the mesoscale model MM5, the development of initial condition uncertainties at different scales and amplitudes and their influences on the mesoscale predictability of the ＂0185＂ Shanghai heavy precipitation event are investigated. It is found that different initial conditions obtained from different globe model analyses lead to large variations in the simulated location and strength of the heavy precipitation, and the scales and amplitudes of the initial condition perturbations significantly influence the model error growth. The power spectrum evolution of the difference total energy （DTE） between a control simulation and a sensitivity experiment indicates that the error growth saturates after 12 h, which is the predictable time limit of the heavy precipitation event. The power spectrum evolution of the accumulated precipitation difference between the control and sensitivity simulations suggests a loss of the mesoscale predictability for precipitation systems of scales smaller than 300 kin, i.e., the predictable space for the heavy precipitation event is beyond 300 km. The results also show that the initial uncertainties at larger scales and amplitudes generally result in larger forecast divergence than the uncertainties at smaller scales and amplitudes. The predictable forecasting time and space can be expanded （e.g., from 12 to 15 h, and from beyond 300 kin to beyond 200 km） under properly prescribed initial perturbations at smaller scales and amplitudes.     

海南岛一次特大暴雨的数值研究

利用卫星资料、NCEP资料以及AREM模式输出资料,对发生在2008年10月中旬海南岛一次大暴雨过程进行了中尺度数值模拟分析。分析结果表明:AREM模式较好地模拟了本次大暴雨过程,当初始资料选取NCEP资料时模拟效果最佳;sθe等值线密集区以及位涡梯度最大区与暴雨中心相对应;暴雨中心700 hPa以下低层对应条件对称不稳定,其上至600 hPa为对流不稳定;条件对称不稳定对其上空的对流不稳定有触发作用;对热源〈Q1〉和水汽汇〈Q2〉分析得出,大值区与降水分布基本一致,凝结潜热的释放对大暴雨过程有反馈作用;位温垂直平流项对Q1起决定性作用,位温局地变化项及水平平流项对Q1的贡献不大。在Q2诸分量中,比湿水平平流项和垂直平流项共同作用于Q2,比湿局地变化项影响较小。     

一次梅汛期强降雨过程的中期时效预报误差分析

2016年6月30日至7月4日,中国长江流域发生了入汛以来最强的一次极端降雨过程,但对雨带位置的预报却出现了显著误差。为此,本文基于欧洲中期天气预报中心的预报资料,利用天气学诊断方法,分析了确定性和集合预报的基本情况,讨论了预报误差产生原因及其演变特征。结果表明：梅雨锋上次天气尺度波动在中国黄淮—辽东半岛到朝鲜半岛—日本东部一带呈“负—正—负”的分布,它的强弱对雨带位置的变化起着重要影响。当该波动偏强时,有利于低层季风向北伸展,加之冷空气强度偏弱,进而造成雨带位置偏北,反之亦然。此外,通过对比集合预报成员中的准确和偏北成员组,发现该次天气尺度波动来源于青藏高原东北部的初始误差场。伴随着中纬度西风波动的向东传播,该误差在中低层沿着梅雨锋向东移动、并不断增强,最终造成中国长江中下游地区雨带位置明显偏北。     

利用二维极值分布模拟我国几个代表站的强降水概率特征

基于中国6个代表站5-9月的逐日降水资料,利用二维Gumbel-Logistic分布,研究了中国不同区域的过程降水量和日最大强降水雨量的联合概率特征。结果表明,各代表性台站的过程雨量和强降水雨量的联合分布均符合二维Gumbel分布。强降水雨量与过程降雨量联合分布所描述的极端事件是更小的小概率事件。相同强降水雨量条件下,过程雨量越大,重现期越长当强降水雨量增大时,同一过程雨量的重现期也延长。在同级强降水雨量出现的条件下,各地过程降雨量往往是愈往南方其条件概率愈大,而其出现的过程雨量也随之增大。这为研究强降水极端状况的全方位特征做出了新的试验．也曼加客观地揭示了极端气候事件的多方面概率特征．     

成飞飞行区域短时强降水时空分布特征及强降水指数分析

成飞飞行空域包含高原、盆地、山区等多种地形,局地气候显著,短时强降水频发。该文使用国家气象信息中心2017—2021年多资料融合逐小时降水数据、国家自动站探空观测数据。统计分析发现,盆地周围沿山地区为盆地短时强降水高发区;101～102°E,31～32°N区域为高原短时强降水高发区。利用百分位法得到高原地区强对流指数阈值：CAPE值≥1930.5 J·kg^-1,BCAPE值≥1974.7 J·kg^-1,抬升指数≥2.6℃,大气可降水量≥86.1 mm,K指数≥37.2℃,SI指数≤-0.9℃。盆地地区强对流指数阈值：CAPE值≥2230.6 J·kg^-1,BCAPE值≥2264.4 J·kg^-1,抬升指数≥1.8℃,大气可降水量≥93.0 mm,K指数≥40.8℃,SI指数≤-1.8℃。建立短时强降水不同下垫面强对流指数阈值,为今后短时强降雨客观预报提供新的思路和方向。     

The Combined Effect of Initial Error and Model Error on ENSO Prediction Uncertainty Generated by the Zebiak-Cane Model

Initial errors and model errors are the source of prediction errors. In this study, the authors compute the conditional nonlinear optimal perturbation （CNOP）-type initial errors and nonlinear forcing singular vector （NFSV）- type tendency errors of the Zebiak-Cane model with respect to El Nifio events and analyze their combined effect on the prediction errors for E1 Nino events. The CNOP- type initial error （NFSV-type tendency error） represents the initial errors （model errors） that have the largest effect on prediction uncertainties for E1 Nifio events under the perfect model （perfect initial conditions） scenario. How- ever, when the CNOP-type initial errors and the NFSV- type tendency errors are simultaneously considered in the model, the prediction errors caused by them are not am- plified as the authors expected. Specifically, the predic- tion errors caused by the combined mode of CNOP-type initial errors and NFSV-type tendency errors are a little larger than those caused by the NFSV-type tendency er- rors. This fact emphasizes a need to investigate the opti- mal combined mode of initial errors and tendency errors that cause the largest prediction error for E1 Nifio events.     

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.     

Optimal Initial Error Growth in the Prediction of the Kuroshio Large Meander Based on a High-resolution Regional Ocean Model

Based on the high-resolution Regional Ocean Modeling System(ROMS) and the conditional nonlinear optimal perturbation(CNOP) method, this study explored the effects of optimal initial errors on the prediction of the Kuroshio large meander(LM) path, and the growth mechanism of optimal initial errors was revealed. For each LM event, two types of initial error(denoted as CNOP1 and CNOP2) were obtained. Their large amplitudes were found located mainly in the upper 2500 m in the upstream region of the LM, i.e., southeast of Kyushu. Furthermore, we analyzed the patterns and nonlinear evolution of the two types of CNOP. We found CNOP1 tends to strengthen the LM path through southwestward extension. Conversely,CNOP2 has almost the opposite pattern to CNOP1, and it tends to weaken the LM path through northeastward contraction.The growth mechanism of optimal initial errors was clarified through eddy-energetics analysis. The results indicated that energy from the background field is transferred to the error field because of barotropic and baroclinic instabilities. Thus, it is inferred that both barotropic and baroclinic processes play important roles in the growth of CNOP-type optimal initial errors.     

WRF模式对江苏一次强降水过程的模拟分析

利用NCEP最终分析资料,使用WRF模式模拟了2008年7月22—23日出现在江苏的一次强降水天气过程。结果表明：WRF模式能较好地模拟出这次降水的区域,对这种中尺度天气系统具有良好的预报能力。在这次降水过程中,低空风场切变线和冷空气以及与高空急流的合理配置加强了强降水区垂直环流的发展,使降水区对流发展;而高空辐散、低空辐合的流场特征也促进了强降水的产生;这次过程的水汽输送在850hPa上最强,850hPa的强水汽输送是产生强降水必需的水汽条件;从能量方面看,江苏全境都处于K指数高值区,特别是江苏中北部有相当高的能量聚集,为强降水提供了不稳定条件。暴雨区上空螺旋度呈低层正中心、高层负值区的分布,螺旋度的高低层耦合是触发并维持低压暴雨的动力机制。     

乌鲁木齐市短时强降水分布特征及环境条件分析

利用常规地面、高空探测资料、加密自动站逐时雨量资料,分析2012—2016年乌鲁木齐市暖季的短时强降水分布特征及环境条件,得出乌鲁木齐市短时强降水的空间分布、月变化及小时雨强特征;通过分析22场短时强降水天气过程,按照500 hPa影响系统分类,得出了西西伯利亚低槽、中亚低涡和西北气流3类环流形势及概念模型;统计得出临近短时强降水时段,K、SI、LI等不稳定指数的月变化差异较大,6—7月各指数集中度高,指示意义最好;5月、9月短时强降水的水汽特征量值明显小于6—8月,7月水汽量值最高。     

Analysis Study on Perturbation Energy and Predictability of Heavy Precipitation in South China

The AREMv2.3 mesoscale numerical model is used to explore storm processes in South China during the pre-rainy season in 2006 by imposing perturbations on the initial fields of physical variables (temperature, humidity, and wind fields). Sensitivity experiments are performed to examine the impacts of initial uncertainties on precipitation, on the error growth, and on the predictability of mesoscale precipitation in South China. The primary conclusion is that inherent initial condition uncertainties can signi...     

一种基于可预报性的暴雨预报评分新方法(Ⅱ):暴雨检验评分模型及评估试验

利用2015-2017年6-8月ECMWF高分辨率模式（ECMWF-Hi）的加工产品,结合我国2 400多个国家级气象站逐小时降水观测资料,对ECMWF-Hi产品24 h降水预报的准确度、集中度和相关性进行了评估,并与ECMWF集合预报模式（ECMWF-EPS）24 h降水预报产品进行比较。为更好地描述预报的集中度,避免单纯用标准差比或平均值比刻画预报集中度的缺陷,建立一个综合标准差和平均值的R指数,用之定量描述模式预报的集中度。结果表明：（1）ECMWF-Hi在均方根误差的检验方面并未表现出优势;而分辨率较低的ECMWF-EPS集合平均预报误差最小。（2）ECMWF-Hi对研究区域降水预报的集中度的整体描述较为准确,离散度与观测较为相似,预报期望也与观测降水的期望最接近,ECMWF-Hi比ECMWF-EPS的集合控制预报与集合平均对观测降水集中度的刻画较为准确。（3）研究区域内各站点R指数分布表明,ECMWF-Hi与ECMWF-EPS控制预报、平均预报相比,对平均值预报不足的站点较多,且这些站点的预报集中度普遍大于观测,ECMWF-Hi的降水预报更接近观测降水。（4）评估应用结果表明,R指数不仅能定性评估模式的集中度,也可定量描述集中度大小。