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。     

影响河北两次相似路径台风的湿位涡对比分析

利用常规的探空和地面资料以及NCEP／NCAR全球再分析资料，对9711号台风温妮和0509号台风麦莎的变性过程和影响河北暴雨过程的湿位涡场进行了诊断分析。结果表明：温妮变性再加强过程是一个温带气旋强烈发展的过程，主要与高层湿位涡扰动下传、热带气旋低压环流两者之间的相互作用有关。而麦莎变性过程中，没有高层湿位涡扰动下传和热带气旋低压环流之间的相互作用过程，无再加强过程。对流层中低层MPV1〈0、MPV2〉0区域对应暴雨区，对此类暴雨具有较好的指示意义；对流层高层高值湿位涡下传，有利于位势不稳定能量的储存和释放，使降水增幅。     

新疆夏季一次特大暴雨天气成因分析

通过对新疆2007.07.17大暴雨过程的T213产品物理场进行解释分析,根据揭示其动力、热力和水汽场特征,进而找出新疆大暴雨天气预报的指标。结果表明:新疆2007.07.17大暴雨天气发生在对流层中层强烈的上升运动区、对流层中低层Ω型θse高能舌中心附近以及对流层中低层偏东气流与中层偏南气流和高层偏北气流交汇处的重合区内;而对流层中低层东路水汽的输送对大暴雨的贡献最为重要。T213产品的涡度、散度、垂直运动、风场、流场、水汽通量、比湿、T-Td场在预报时效上与暴雨区一致;假相当位温场、水汽通量散度场在预报时效上超前于暴雨。     

影响山东的台风暴雨天气的湿位涡诊断分析

应用湿位涡理论 ,对发生在山东境内由台风和台风减弱的低压引发的两场大暴雨过程进行诊断。结果表明 :这两场暴雨都产生在θe 陡立密集区附近 ,θe 陡立密集区附近易导致湿斜压涡度发展 ;对流层中低层MPV1 <0 ,850hPa上MPV2 >0 ,综合反映了暴雨区对流不稳定和斜压不稳定的发展 ;对流层高层高值湿位涡下传 ,有利于位势不稳定能量的储存和释放 ,使降水增幅。     

华南暖区暴雨预报的模式初始场质量敏感性分析北大核心

本文利用高分辨率中尺度数值预报模式WRF和两组再分析资料,在研究不同模式初值对华南暖区暴雨预报质量差异明显的基础上,利用合成初值方法进行了模式初值对暖区暴雨预报的敏感性数值试验研究,讨论了模式初始场关键物理量对暖区暴雨预报质量的影响,重点开展了模式初值湿度场质量对华南暖区暴雨降水预报的敏感性分析。结果表明:模式初始场质量的较小差异,可显著影响本次华南暖区暴雨预报的降水强度、降水落区以及降水发生时间等的质量。初始水汽场对暖区暴雨预报影响最大,也最为敏感,是准确预报对流单体的发生发展以及地面强降水的基础。风场和温度场对暖区暴雨预报的影响相对较小。对流层低层较强的风速辐合是本次暖区暴雨强对流单体触发、生成和加强发展以至产生暖区强降水的物理基础。     

一次西南地区暴雨的湿位涡及不稳定性分析

分析2004年5月28~30日我省发生的一次连续性暴雨天气过程的天气形势、物理量、湿位涡,结果表明,此次降水主要由于亚洲中高纬环流调整带来冷空气,与西南暖湿空气相遇,在适宜的动力、热力条件下产生的,此次过程中包含两次强降水过程,都有对流层中低层的湿位涡负值区相配合,对流层中低层湿位涡及其垂直和水平分量的分布演变情况与暴雨的时空演变有密切的对应关系;在降水的初期,对流性不稳定发挥了主要作用,在后一次降水中主要是对称不稳定。     

江淮梅雨锋强暴雨低涡系统发生发展的数值研究

利用美国新一代中尺度WRF(Weather Research and Forecast)模式,采用高分辨率的细网格距和适当的物理方案,对2003年7月4—5日的江淮梅雨锋强暴雨中尺度系统进行了数值模拟研究。模拟结果很好地描述了本次暴雨及其中尺度系统发生、发展的时空演变过程,并较理想地预报出了该次降水的落区、强度及降水中心的位置。着重分析了低空急流、倾斜不稳定发展及中尺度低涡系统和对流层高层小扰动,并进一步指出了其形成、发展的物理机制。从数值模拟分析结果看,倾斜垂直涡度的发展是造成低空涡旋生成和发展的动力机制。充足的湿有效能量和凝结潜热的释放为本次大降水过程提供了物质条件。     

2006年8月海河流域暴雨过程的成因分析

在当前气候日趋变暖的大背景下,极端天气事件频发,为了认识暴雨的形成机理,提高海河流域暴雨的预报能力,利用NCEP 1°×1°的6小时再分析资料和常规观测资料以及FY-2C卫星云图资料,对2006年8月25-26日海河流域的暴雨过程,尤其是河北东部的大暴雨进行了天气学诊断分析.分析结果表明,暴雨是产生在前期大气对流不稳定区域里,25日20时到26日02时6小时雨量超过20mm的站点基本分布在对流层中低层湿位涡的负值区内,低空急流为暴雨区输送大量的不稳定能量、热量以及动量,为暴雨的产生提供了充足的能量条件和水汽条件;流域东部的大暴雨区处在正螺旋度大值中心以西地区,暴雨区上空有较强的旋转上升气流;暴雨期间中低层辐合、高层辐散,高层辐散强于中低层辐合的抽吸作用,有利于加强低层辐合和对流上升运动,为大暴雨的产生提供了动力条件;对流层中高层的水汽对强降水云团的发展起了重要作用.     

北京一次强降水过程的数值模拟

利用新一代中尺度数值预报模式WRF3.3和1°×1°的NCEP气象再分析资料,对2011年7月24日北京强降水天气过程进行数值模拟,并利用模式输出的高分辨率资料进行诊断分析。结果表明：WRF模式能较好地模拟出这次强降水过程。该过程不仅受到对流层中低层长波低槽和地面辐合区系统性的动力抬升作用,还受到对流层高层辐散的强迫作用。在这种配置下,中低层大尺度动力抬升与高层强辐散呈现出垂直耦合状态,有利于强降水区垂直环流和对流的发展。同时北京地区上空500 hPa以下相对湿度大于70 %,在降水区形成了深厚的高湿环境,为降水的产生、加强和维系提供了充沛的水汽条件。从大气稳定度方面看,北京市全境均处于K指数高值区,高峰值为42.5 ℃,反映了大气层结非常不稳定。从动力作用分析发现,高空辐散、低空辐合的流场特征促进了降水的产生,螺旋度低层正值、高层负值的耦合结构是触发并维持降水的动力机制。     

华南一次暴雨过程热力和动力特征的诊断分析

2015年5月19—20日广东省强降水过程具有降水集中、强度大和局地性强的特点,利用广东省自动气象站观测资料、ECMWF_FINE再分析资料,对此次强降水过程进行分析发现:华南地区受低槽东移影响,强降水发生在切变线南侧偏南暖湿流场中,粤北降水属于锋面降水,粤东降水属于锋前暖区降水,两者在水汽输送和动力机制上有显著区别。孟加拉湾和南海输送的水汽在这次强降水过程中占主导地位,南边界和东边界为水汽的流入边界,整体水汽输送以经向输入为主。暖区降水区域处于较强的水汽平流环境中,具有更大的水汽净输送量,造成粤东地区的降水量更大。对流层高层辐散比中低层辐合更为重要,是粤东暖区降水重要的动力属性,且暖区中低层流场的旋转效应弱,有区别于典型的梅雨锋降水。利用绝热无摩擦湿位涡守恒进行诊断发现对流不稳定是此次强降水发展的主要机制,暴雨发生区域对应湿位涡垂直分量为负值,水平分量为正值,底层MPV1＜0和MPV2＞0综合反映了大气对流不稳定和斜压不稳定的增强过程。降水区对流层低层受负湿位涡控制,低层湿位涡负值区与强降水落区有较好的对应关系。      

初始场中尺度信息对暴雨预报的影响

由于观测资料分辨率与模式分辨率的不同,利用高分辨率模式对暴雨进行预报时,常规观测资料形成的初始场不能直接分辨出中尺度系统,这种中尺度系统特征的缺少可以认为是初始场的一种信息误差。利用中尺度天气分析的尺度分离方法可以提取这种中尺度信息。通过分析初始场中尺度信息的结构、演变特征及其对暴雨预报影响的机理,发现初始场中尺度信息的结构在主要雨带的对流敏感区具有明确的天气学意义,包含了有利暴雨产生的信息;其能量随时间也是增长的,特别是在积分12小时以后,能量迅速增长然后趋于稳定,超过了初始随机扰动的能量增长。利用减弱和增强初始场中中尺度信息的两种初始场作暴雨预报,其结果反映了初始场中尺度信息对暴雨预报的重要性,特别是对雨团位置和强度的预报,这些信息会直接影响暴雨的精细预报。     

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.     

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...     

Insights into Convective-scale Predictability in East China: Error Growth Dynamics and Associated Impact on Precipitation of Warm-Season Convective Events

This study investigated the regime-dependent predictability using convective-scale ensemble forecasts initialized with different initial condition perturbations in the Yangtze and Huai River basin(YHRB) of East China. The scale-dependent error growth(ensemble variability) and associated impact on precipitation forecasts(precipitation uncertainties) were quantitatively explored for 13 warm-season convective events that were categorized in terms of strong forcing and weak forcing. The forecast error growth in the strong-forcing regime shows a stepwise increase with increasing spatial scale,while the error growth shows a larger temporal variability with an afternoon peak appearing at smaller scales under weak forcing. This leads to the dissimilarity of precipitation uncertainty and shows a strong correlation between error growth and precipitation across spatial scales. The lateral boundary condition errors exert a quasi-linear increase on error growth with time at the larger scale, suggesting that the large-scale flow could govern the magnitude of error growth and associated precipitation uncertainties, especially for the strong-forcing regime. Further comparisons between scale-based initial error sensitivity experiments show evident scale interaction including upscale transfer of small-scale errors and downscale cascade of larger-scale errors. Specifically, small-scale errors are found to be more sensitive in the weak-forcing regime than those under strong forcing. Meanwhile, larger-scale initial errors are responsible for the error growth after 4 h and produce the precipitation uncertainties at the meso-β-scale. Consequently, these results can be used to explain underdispersion issues in convective-scale ensemble forecasts and provide feedback for ensemble design over the YHRB.     

Spatial Propagation of Different Scale Errors in Meiyu Frontal Rainfall Systems

The spatial propagation of meso- and small-scale errors in a Meiyu frontal heavy rainfall event,which occurred in eastern China during 4 -6 July 2003,is investigated by using the mesoscale numerical mo...     

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...     

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...     

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.     

What Kind of Initial Errors Cause the Severest Prediction Uncertainty of El Nino in Zebiak-Cane Model

With the Zebiak-Cane （ZC） model, the initial error that has the largest effect on ENSO prediction is explored by conditional nonlinear optimal perturbation （CNOP）. The results demonstrate that CNOP-type errors cause the largest prediction error of ENSO in the ZC model. By analyzing the behavior of CNOPtype errors, we find that for the normal states and the relatively weak E1 Nifio events in the ZC model, the predictions tend to yield false alarms due to the uncertainties caused by CNOP. For the relatively strong E1 Nino events, the ZC model largely underestimates their intensities. Also, our results suggest that the error growth of E1 Nifio in the ZC model depends on the phases of both the annual cycle and ENSO. The condition during northern spring and summer is most favorable for the error growth. The ENSO prediction bestriding these two seasons may be the most difficult. A linear singular vector （LSV） approach is also used to estimate the error growth of ENSO, but it underestimates the prediction uncertainties of ENSO in the ZC model. This result indicates that the different initial errors cause different amplitudes of prediction errors though they have same magnitudes. CNOP yields the severest prediction uncertainty. That is to say, the prediction skill of ENSO is closely related to the types of initial error. This finding illustrates a theoretical basis of data assimilation. It is expected that a data assimilation method can filter the initial errors related to CNOP and improve the ENSO forecast skill.     

What Kind of Initial Errors Cause the Severest Prediction Uncertainty of EI Nino in Zebiak-Cane Model

With the Zebiak-Cane (ZC) model, the initial error that has the largest effect on ENSO prediction is explored by conditional nonlinear optimal perturbation (CNOP). The results demonstrate that CNOP-type errors cause the largest prediction error of ENSO in the ZC model. By analyzing the behavior of CNOP- type errors, we find that for the normal states and the relatively weak EI Nino events in the ZC model, the predictions tend to yield false alarms due to the uncertainties caused by CNOP. For the relatively strong EI Nino events, the ZC model largely underestimates their intensities. Also, our results suggest that the error growth of EI Nino in the ZC model depends on the phases of both the annual cycle and ENSO. The condition during northern spring and summer is most favorable for the error growth. The ENSO prediction bestriding these two seasons may be the most difficult. A linear singular vector (LSV) approach is also used to estimate the error growth of ENSO, but it underestimates the prediction uncertainties of ENSO in the ZC model. This result indicates that the different initial errors cause different amplitudes of prediction errors though they have same magnitudes. CNOP yields the severest prediction uncertainty. That is to say, the prediction skill of ENSO is closely related to the types of initial error. This finding illustrates a theoretical basis of data assimilation. It is expected that a data assimilation method can filter the initial errors related to CNOP and improve the ENSO forecast skill.