共查询到20条相似文献，搜索用时 656 毫秒
1.
目前国际上采用的奇异向量集合预报初值扰动法对于初值不确定性的描述存在一定的不足,为了更有效地反映初始误差的时空多尺度特性,基于GRAPES全球奇异向量计算技术,计算了不同空间分辨率及不同最优时间间隔的多个尺度的奇异向量,并采用基于高斯分布的线性组合法来构造多尺度奇异向量的扰动初值,以代表在相空间中增长最快的多尺度初值误差模态。通过2019年1月19日的初值扰动集合预报试验,对比分析了单一尺度奇异向量初值扰动法与多尺度初值扰动法的扰动特征及集合预报效果。结果表明,多尺度奇异向量初值扰动法为区域集合预报提供的初始扰动场是合理的,扰动的大小随时间增长,且在空间分布上较好地反映了当前大气的斜压不稳定特征。此外,多尺度奇异向量扰动可以描述一定的大尺度以及中小尺度运动误差特征,较单一尺度奇异向量扰动能反映出更多初始场的不确定性信息。检验分析表明,GRAPES多尺度奇异向量集合预报在集合一致性、连续等级概率评分、离群值等方面有一定的优势,相比于单一尺度奇异向量法有较好的预报技巧。因此,基于GRAPES的多尺度奇异向量初值扰动法对于集合预报的预报效果有一定的提高,能为构建一套完善的GRAPES区域奇异向量集合预报系统提供一定的科学依据和应用基础。 相似文献
2.
The impact of initialization and perturbation methods on the ensemble prediction of the boreal summer intraseasonal oscillation was investigated using 20-year hindcast predictions of a coupled general circulation model. The three perturbation methods used in the present study are the lagged-averaged forecast (LAF) method, the breeding method, and the empirical singular vector (ESV) method. Hindcast experiments were performed with a prediction interval of 10 days for extended boreal summer (May–October) seasons over a 20 year period. The empirical orthogonal function (EOF) eigenvectors of the initial perturbations depend on the individual perturbation method used. The leading EOF eigenvectors of the LAF perturbations exhibit large variances in the extratropics. Bred vectors with a breeding interval of 3 days represent the local unstable mode moving northward and eastward over the Indian and western Pacific region, and the leading EOF modes of the ESV perturbations represent planetary-scale eastward moving perturbations over the tropics. By combining the three perturbation methods, a multi-perturbation (MP) ensemble prediction system for the intraseasonal time scale was constructed, and the effectiveness of the MP prediction system for the Madden and Julian oscillation (MJO) prediction was examined in the present study. The MJO prediction skills of the individual perturbation methods are all similar; however, the MP‐based prediction has a higher level of correlation skill for predicting the real-time multivariate MJO indices compared to those of the other individual perturbation methods. The predictability of the intraseasonal oscillation is sensitive to the MJO amplitude and to the location of the dominant convective anomaly in the initial state. The improvement in the skill of the MP prediction system is more effective during periods of weak MJO activity. 相似文献
3.
不同大尺度强迫条件下考虑初始场与侧边界条件不确定性的对流尺度集合预报试验 总被引：5，自引：0，他引：5
研究了不同大尺度强迫条件下的暴雨个例中,考虑不同尺度特征的初始扰动与侧边界扰动相互作用构造对流尺度集合预报的可行性,为进一步构建“自适应”于不同强对流天气的对流尺度集合预报系统提供依据。结果表明,在大尺度强迫显著的个例1中,以大尺度扰动信息为主的动力降尺度的增长趋势较集合转换卡尔曼滤波(ETKF)更为显著,且总扰动能量在预报中后期超过集合转换卡尔曼滤波,而在大尺度强迫较弱的个例2中,集合转换卡尔曼滤波扰动能量始终高于动力降尺度。此外,当大尺度强迫显著时,初始扰动与侧边界扰动相匹配会产生相互促进的作用,而不匹配时初始扰动会在预报中后期抑制侧边界扰动的发展,当大尺度强迫较弱时,即使是互相间不匹配的初始扰动与侧边界扰动也能在大部分预报时段起到相互促进的作用,说明初始扰动与侧边界扰动的相互作用机制不仅与天气形势相关,也与二者是否匹配挂钩,另外,扰动的发展特征同样依赖于天气形势;从集合离散度的角度来看,当大尺度强迫明显时,侧边界扰动的作用会在更短的时间内取代初始扰动,从而对离散度起到主导地位;两种初始扰动方法相比,集合转换卡尔曼滤波在多数情况下对总离散度的贡献均大于动力降尺度;从降水量预报及概率预报情况来看,大尺度强迫明显的个例可预报性更高,且各集合成员间的差异较小,大尺度强迫较弱的个例则相反,且当两种初始扰动方案与侧边界扰动相结合时,较仅侧边界扰动均有一定提高。 相似文献
4.
In this paper, a nonlinear optimization method is used to explore the finite-time instability of the atmospheric circulation
with a three-level quasigeostrophic model under the framework of the conditional nonlinear optimal perturbation (CNOP). As
a natural generalization of linear singular vector (SV), CNOP is defined as an initial perturbation that makes the cost function
the maximum at a prescribed forecast time under certain physical constraint conditions. Special attentions are paid to the
different structures and energy evolutions of the optimal perturbations. 相似文献
5.
Qian ZOU Quanjia ZHONG Jiangyu MAO Ruiqiang DING Deyu LU Jianping LI Xuan LI 《大气科学进展》2023,40(3):501-513
Based on a simple coupled Lorenz model, we investigate how to assess a suitable initial perturbation scheme for ensemble forecasting in a multiscale system involving slow dynamics and fast dynamics. Four initial perturbation approaches are used in the ensemble forecasting experiments: the random perturbation(RP), the bred vector(BV), the ensemble transform Kalman filter(ETKF), and the nonlinear local Lyapunov vector(NLLV) methods. Results show that,regardless of the method used, the ensemble ave... 相似文献
6.
目前中国气象局全球集合预报系统（China Meteorological Administration Global Ensemble Prediction System,CMA-GEPS）利用CMA全球数值预报系统分析场计算奇异向量（ANSV）,欧洲中期天气预报中心采用同化背景场计算奇异向量（FCSV）,在业务流程上先于计算ANSV,可优化集合预报系统运行时间。为此,在CMA-GEPS中探索采用FCSV进行集合预报的可行性,分析ANSV和FCSV的空间分布及相似指数,进而针对夏秋季节10个个例开展采用ANSV和FCSV的全球集合预报试验,从等压面要素集合预报技巧、中国地区24 h累积降水概率预报技巧、台风路径集合预报技巧、台风中心最低海平面气压预报技巧等方面对比二者结果。结果表明:ANSV和FCSV的主要结构特征相似,两组集合预报结果相当,表明在CMA-GEPS中使用FCSV可行,可作为未来高分辨率CMA-GEPS业务系统建设的选项。 相似文献
7.
数值天气预报和气候预测可预报性研究的若干动力学方法 总被引：4，自引：2，他引：2
简要回顾了数值天气预报和气候预测可预报性研究的若干动力学方法,包括用于研究第一类可预报性问题的线性奇异向量（LSV）和条件非线性最优初始扰动（CNOP-I）方法,以及Lyapunov指数和非线性局部Lyapunov指数方法。前两种方法用于研究预报或预测的预报误差问题,可以用于估计天气预报和气候预测的最大预报误差,而且根据导致最大预报误差的初始误差结构的信息,这两种方法可以用于确定预报或预测的初值敏感区。应该指出的是,LSV是基于线性化模式,对于描述非线性大气和海洋的运动具有局限性。因而,对于非线性模式,应该选择使用CNOP-I估计最大预报误差。Lyapunov指数和非线性局部Lyapunov指数可以用于研究第一类可预报性问题中的预报时限问题,前者是基于线性模式,不能解释非线性对预报时限的影响,而非线性局部Lyapunov指数方法则考虑了非线性的影响,能够较好地估计实际天气和气候的预报时限。第二类可预报性问题的研究方法相对较少,本文仅介绍了由我国科学家提出的关于模式参数扰动的条件非线性最优参数扰动（CNOP-P）方法,该方法可以用于寻找到对预报有最大影响的参数扰动,并可以进一步确定哪些参数最应该利用观测资料进行校准。另一方面,通过对比CNOP-I和CNOP-P对预报误差的影响,可以判断导致预报不确定性的主要误差因子,进而指导人们着力改进模式或者初始场。 相似文献
8.
初始扰动振幅的大小和集合样本数对于集合预报取得更高预报技巧具有重要意义。本文将正交条件非线性最优扰动方法（orthogonal conditional nonlinear optimal perturbations,简称CNOPs）应用于概念模型Lorenz-96模式探讨了初始扰动振幅和集合样本数对集合预报技巧的影响,从而为使用更复杂模式进行集合预报提供指导。结果表明,由于CNOPs扮演了非线性系统中的最优初始扰动,从而使得当初始扰动振幅小于初始分析误差的大小时,CNOPs集合预报获得更高的预报技巧,并且CNOPs集合预报的最高预报技巧总是高于奇异向量法（singular vectors,简称SVs）集合预报的最高预报技巧。结果还表明,CNOPs集合预报倾向于具有一个合适的样本数时,达到最高技巧。更好的集合离散度——预报误差关系和更为平坦的Talagrand图（Talagrand diagram）进一步证明了CNOPs集合预报系统的可靠性,从而夯实了上述结果的合理性。因此,针对CNOPs集合预报,本文认为采用一个适当小于初始分析误差的初始扰动振幅和一个合适的集合样本数,有利于CNOPs集合预报达到最高预报技巧。 相似文献
9.
In this study, singular vector analysis was performed for the period from 1856 to 2003 using the latest Zebiak–Cane model
version LDEO5. The singular vector, representing the optimal growth pattern of initial perturbations/errors, was obtained
by perturbing the constructed tangent linear model of the Zebiak–Cane model. Variations in the singular vector and singular
value, as a function of initial time, season, ENSO states, and optimal period, were investigated. Emphasis was placed on exploring
relative roles of linear and nonlinear processes in the optimal perturbation growth of ENSO, and deriving statistically robust
conclusions using long-term singular vector analysis. It was found that the first singular vector is dominated by a west–east
dipole spanning most of the equatorial Pacific, with one center located in the east and the other in the central Pacific.
Singular vectors are less sensitive to initial conditions, i.e., independence of seasons and decades; while singular values
exhibit a strong sensitivity to initial conditions. The dynamical diagnosis shows that the total linear and nonlinear heating
terms play opposite roles in controlling the optimal perturbation growth, and that the linear optimal perturbation is more
than twice as large as the nonlinear one. The total linear heating causes a warming effect and controls two positive perturbation
growth regions: one in the central Pacific and the other in the eastern Pacific; whereas the total linearized nonlinear advection
brings a cooling effect controlling the negative perturbation growth in the central Pacific. 相似文献
10.
基于奇异矢量的优化短期集合预报 总被引：2，自引：1，他引：1
在1-2 d的短期预报中,由奇异矢量构建的初始扰动主要是线性发展,为了防止在积分终止时刻,由同一奇异矢量导出的正负初始扰动的积分在集合平均时互相抵消,文中首先通过理论推导和实际计算证明了对集合成员进行优化的必要性,以及从不同奇异矢量导出的集合成员中,表现好于控制预报的一组成员相对于控制预报的离差恒大于或恒小于表现劣于控制预报的另一组成员,利用这个特征,在做集合预报时,把奇异矢量导出的正负两组预报分成离差相对大一组、离差相对小一组,就可以避免求集合平均时成员相互抵消,从而提出了一种优化基于奇异矢量的短期集合预报的方法.文中使用NCAR/PSU(美国国家大气研究中心/宾夕法尼亚大学)中尺度有限区域模式MM5第1版,及其对应切线性、伴随模式,对1999年夏季发生的两个梅雨锋低涡个例作了分析,在计算奇异矢量时采用了干能量模,分析结果表明:相对于正负两个初始扰动都入选的集合,严格按照这种方法挑选出来的优化集合可以有效地提高集合平均的精确度.在生成初始扰动的方法上,文中的计算表明,相对于用单个奇异矢量生成初始扰动,把正交的多个奇异矢量累加起来导出的初始扰动具有更大的增长率,能有效地增大集合成员间的离差,提高集合成员的预报精度. 相似文献
11.
采用线性化物理过程方案的GRAPES全球模式奇异向量在进行非线性模式积分时会有部分奇异向量出现崩溃问题,这说明奇异向量结构可能存在扰动变量之间不协调之处,需要对奇异向量扰动的计算方法优化,进而改进基于奇异向量的集合预报初值扰动,提高GRAPES全球集合预报效果。基于原有的GRAEPS全球奇异向量计算方法,在求解奇异向量时,对气压扰动的处理进行改进,将初始时刻的气压扰动分量通过位温扰动根据静力平衡关系导出获得,其他保持一致,发展了静力平衡奇异向量改进方法。基于有两个台风过程的个例（2019年8月8日12时（世界时））,分别采用原奇异向量方法和静力平衡奇异向量改进方法进行热带气旋目标区奇异向量的计算求解,并进行相应奇异向量的非线性模式积分,对比分析奇异向量非线性积分的稳定性。进而,对比分析奇异向量求解方法改进前、后热带气旋奇异向量的结构特征和初值扰动特征,开展了集合预报试验,评估改进后的奇异向量求解方法对GRAPES全球集合预报系统预报性能的影响。试验结果表明,静力平衡奇异向量改进方法通过产生协调的气压扰动和位温扰动场,解决了奇异向量非线性积分崩溃的问题,消除了原来不利于积分稳定性的气压扰动过于局地化的小尺度结构。静力平衡奇异向量改进方法对奇异向量中位温扰动分量和纬向风扰动分量结构影响较小,使得气压扰动分量的大值区位于台风附近,更好地描述热带气旋初值不确定性,与位温扰动分量的分布更加协调。采用静力平衡奇异向量改进方法,可以提高GRAPES全球集合预报在北半球和南半球等压面要素集合预报技巧和中国地区24 h累计降水概率预报技巧,增大台风路径集合离散度。 相似文献
12.
Study on multi-scale blending initial condition perturbations for a regional ensemble prediction system 总被引：9，自引：0，他引：9
An initial conditions (ICs) perturbation method was developed with the aim to improve an operational regional ensemble prediction system (REPS). Three issues were identified and investigated: (2) the impacts of perturbation scale on the ensemble spread and forecast skill of the REPS; (3) the scale characteristic of the IC perturbations of the REPS; and (4) whether the REPS's skill could be improved by adding large-scale information to the IC perturbations. Numerical experiments were conducted to reveal the impact of perturbation scale on the ensemble spread and forecast skill. The scales of IC perturbations from the REPS and an operational global ensemble prediction system (GEPS) were analyzed. A "multi-scale blending" (MSB) IC perturbation scheme was developed, and the main findings can be summarized as follows: The growth rates of the ensemble spread of the REPS are sensitive to the scale of the IC perturbations; the ensemble forecast skills can benefit from large-scale perturbations; the global ensemble IC perturbations exhibit more power at larger scales, while the regional ensemble IC perturbations contain more power at smaller scales; the MSB method can generate IC perturbations by combining the small-scale component from the REPS and the large-scale component from the GEPS; the energy norm growth of the MSB-generated perturbations can be appropriate at all forecast lead times; and the MSB-based REPS shows higher skill than the original system, as determined by ensemble forecast verification. 相似文献
13.
基于副热带奇异向量的初值扰动方法已应用于GRAPES （Global and Regional Assimilation PrEdiction System）全球集合预报系统,但存在热带气旋预报路径离散度不足的问题。通过分析发现,热带气旋附近区域初值扰动结构不合理导致预报集合不能较好地估计热带气旋预报的不确定性,是路径集合离散度不足的可能原因之一。通过建立热带气旋奇异向量求解方案,将热带气旋奇异向量和副热带奇异向量共同线性组合生成初值扰动,以弥补热带气旋区域初值扰动结构不合理这一缺陷,进而改进热带气旋集合预报效果。利用GRAPES全球奇异向量计算方案,以台风中心10个经纬度区域为目标区构建热带气旋奇异向量求解方案,针对台风“榕树”个例进行集合预报试验,并开展批量试验,利用中国中央气象台最优台风路径和中国国家气象信息中心的降水观测资料进行检验,对比分析热带气旋奇异向量结构特征和初值扰动特征,评估热带气旋奇异向量对热带气旋路径集合预报和中国区域24 h累计降水概率预报技巧的影响。结果表明,热带气旋奇异向量具有局地化特征,使用热带气旋奇异向量之后,热带气旋路径离散度增加,路径集合平均预报误差和离散度的关系得到改善,路径集合平均预报误差有所减小,集合成员更好地描述了热带气旋路径的预报不确定性;中国台风降水的小雨、中雨、大雨、暴雨各量级24 h累计降水概率预报技巧均有一定提高。总之,当在初值扰动的生成中考虑热带气旋奇异向量后,可改进热带气旋初值扰动结果,并有助于改善热带气旋路径集合预报效果。 相似文献
14.
Applications of conditional nonlinear optimal perturbation in predictability study and sensitivity analysis of weather and climate 总被引：1，自引：0，他引：1
Considering the limitation of the linear theory of singular vector (SV), the authors and their collaborators proposed conditional nonlinear optimal perturbation (CNOP) and then applied it in the predictability study and the sensitivity analysis of weather and climate system. To celebrate the 20th anniversary of Chinese National Committee for World Climate Research Programme (WCRP), this paper is devoted to reviewing the main results of these studies. First, CNOP represents the initial perturbation that has largest nonlinear evolution at prediction time, which is different from linear singular vector (LSV) for the large magnitude of initial perturbation or/and the long optimization time interval. Second, CNOP, rather than linear singular vector (LSV), represents the initial anomaly that evolves into ENSO events most probably. It is also the CNOP that induces the most prominent seasonal variation of error growth for ENSO predictability; furthermore, CNOP was applied to investigate the decadal variability of ENSO asymmetry. It is demonstrated that the changing nonlinearity causes the change of ENSO asymmetry. Third, in the studies of the sensitivity and stability of ocean’s thermohaline circulation (THC), the nonlinear asymmetric response of THC to finite amplitude of initial perturbations was revealed by CNOP. Through this approach the passive mechanism of decadal variation of THC was demonstrated; Also the authors studies the instability and sensitivity analysis of grassland ecosystem by using CNOP and show the mechanism of the transitions between the grassland and desert states. Finally, a detailed discussion on the results obtained by CNOP suggests the applicability of CNOP in predictability studies and sensitivity analysis. 相似文献
15.
DENG Guo TIAN Hu LI Xiaoli CHEN Jing GONG Jiandong JIAO Meiyan 《Acta Meteorologica Sinica》2012,26(1):52-61
To compare the initial perturbation techniques using breeding vectors and ensemble transform vectors,three ensemble prediction systems using both initial perturbation methods but with different ensembl... 相似文献
16.
Reducing the prediction uncertainties of high-impact weather and climate events: An overview of studies at LASG 
下载免费PDF全文
下载免费PDF全文 This paper summarizes recent progress at the State Key Laboratory of Numerical Modeling for Atmospheric Sciences and Geophysical Fluid Dynamics (LASG), Institute of Atmospheric Physics, Chinese Academy of Sciences in studies on targeted observations, data assimilation, and ensemble prediction, which are three effective strategies to reduce the prediction uncertainties and improve the forecast skill of weather and climate events. Considering the limitations of traditional targeted observation approaches, LASG researchers have developed a conditional nonlinear optimal perturbation-based targeted observation strategy to optimize the design of the observing network. This strategy has been employed to identify sensitive areas for targeted observations of the El Niño–Southern Oscillation, Indian Ocean dipole, and tropical cyclones, and has been demonstrated to be effective in improving the forecast skill of these events. To assimilate the targeted observations into the initial state of a numerical model, a dimension-reducedprojection- based four-dimensional variational data assimilation (DRP-4DVar) approach has been proposed and is used operationally to supply accurate initial conditions in numerical forecasts. The performance of DRP-4DVar is good, and its computational cost is much lower than the standard 4DVar approach. Besides, ensemble prediction, which is a practical approach to generate probabilistic forecasts of the future state of a particular system, can be used to reduce the prediction uncertainties of single forecasts by taking the ensemble mean of forecast members. In this field, LASG researchers have proposed an ensemble forecast method that uses nonlinear local Lyapunov vectors (NLLVs) to yield ensemble initial perturbations. Its application in simple models has shown that NLLVs are more useful than bred vectors and singular vectors in improving the skill of the ensemble forecast. Therefore, NLLVs represent a candidate for possible development as an ensemble method in operational forecasts. Despite the considerable efforts made towards developing these methods to reduce prediction uncertainties, much challenging but highly important work remains in terms of improving the methods to further increase the skill in forecasting such weather and climate events. 相似文献
17.
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. 相似文献
18.
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. 相似文献
19.
Initial perturbation scheme is one of the important problems for ensemble prediction. In this paper,
ensemble initial perturbation scheme for Global/Regional Assimilation and PrEdiction System (GRAPES)
global ensemble prediction is developed in terms of the ensemble transform Kalman filter (ETKF) method.A new GRAPES global ensemble prediction system (GEPS) is also constructed. The spherical simplex 14-member ensemble prediction experiments, using the simulated observation network and error characteristics of simulated observations and innovation-based in ation, are carried out for about two months. The structure characters and perturbation amplitudes of the ETKF initial perturbations and the perturbation growth characters are analyzed, and their qualities and abilities for the ensemble initial perturbations are given.
The preliminary experimental results indicate that the ETKF-based GRAPES ensemble initial perturbations could identify main normal structures of analysis error variance and reflect the perturbation amplitudes.The initial perturbations and the spread are reasonable. The initial perturbation variance, which is approximately equal to the forecast error variance, is found to respond to changes in the observational spatial variations with simulated observational network density. The perturbations generated through the simplex method are also shown to exhibit a very high degree of consistency between initial analysis and short-range forecast perturbations. The appropriate growth and spread of ensemble perturbations can be maintained up to 96-h lead time. The statistical results for 52-day ensemble forecasts show that the forecast scores ofensemble average for the Northern Hemisphere are higher than that of the control forecast. Provided that using more ensemble members, a real-time observational network and a more appropriate inflation factor,better
effects of the ETKF-based initial scheme should be shown. 相似文献
20.
集合预报方法是解决单一数值预报不确定性问题的有效手段,而针对强天气预报的中尺度区域集合预报技术已逐渐受到国内外的重视。对于区域集合预报而言,由于其不确定性来源较为复杂,如何发展有效的扰动方法是研究的热点和难点。本文根据国内外区域集合预报的研究进展,从初值扰动、模式扰动以及侧边界扰动三个方面进行了总结和回顾,并对扰动方法的发展趋势进行了介绍。对于初值扰动,较为主流的方法有动力降尺度,沿用传统的由全球集合扰动方法发展而来的技术为区域集合产生初值,以及专门为区域集合设计的扰动方法。鉴于这些方法各有利弊,目前对于初值扰动方法的研究已经开始发展充分包含大尺度和小尺度不确定性信息的混合扰动方法。区域集合预报模式扰动的研究以物理过程扰动为主,典型方法为多物理过程组合以及随机物理过程扰动,其中多物理过程组合方法简单有效,而随机物理过程扰动方法的物理意义更为明确,是物理过程扰动的趋势。通过多模式组合进行模式扰动的方法也开展了一些相关研究,且对于台风等强天气预报均显示出相对于单模式集合较好的效果。侧边界扰动的主流方法是由大尺度集合预报场来为区域集合提供不同的侧边界,研究结果表明此种侧边界扰动方法简便易行,且有助于提高区域集合预报较长预报时效离散度和预报技巧。 相似文献