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1.
利用CMA全球集合预报(CMA-GEPS)业务系统2020年6月1日至2021年5月31日一整年的500 hPa位势高度场(H500)预报数据,诊断评估了CMA-GEPS在北半球地区误差增长及预报性能的尺度依赖特征。使用谱滤波方法实现H500不同尺度(包括行星尺度、天气尺度与次天气尺度)分量的分离。从集合平均均方根误差(简称集合平均误差)-离散度关系来看,在预报前期(108 h之前),CMA-GEPS集合平均误差小于集合离散度,存在过度发散的问题,主要是由天气尺度分量离散度过大导致;在预报后期(108 h之后),CMA-GEPS集合平均误差大于集合离散度,离散度偏小,是由行星尺度与天气尺度分量离散度不足共同引起。采用Dalcher等1987年修订的误差增长模型对H500集合平均预报误差增长特征进行诊断分析,发现CMA-GEPS误差增长过程合理,初始误差在次天气尺度上增长最快,行星尺度上增长最慢;就绝对(相对)误差而言,模式误差对预报误差的影响随空间尺度的增大而增大(减小)。此外,将使用1989至2018年共计30 a的E... 相似文献
2.
一次梅雨暴雨预报中的误差演变及可预报性分析 总被引：4，自引：0，他引：4
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。 相似文献
3.
弱天气尺度强迫背景下的长江中下游暖区暴雨突发性强,高度非线性,难以准确预报,这时考虑不确定因素的集合预报成为重要选项,而对流尺度集合预报核心问题是积分一段时间后离散度偏低,会导致预报失败。比较包含不同尺度扰动信息的对流尺度集合预报方案间的差异性并据此优化初始扰动方案,针对2018年5月4—5日一次典型长江中下游暖区暴雨过程,分别采用动力降尺度（DOWN）、增长模繁殖法（BGM）、局地增长模繁殖法（LBGM）和混合扰动法（BLEND）等四种方法进行集合预报试验,以期探讨对离散度和预报效果的影响。结果表明,在模式积分0~6 h,具有中小尺度扰动信息的BGM和LBGM的离散度优于DOWN,其中LBGM相比于BGM具有一定程度上的改进,说明具有更准确中尺度特征的扰动能够在积分初始阶段获得有效增长,即考虑了中小尺度天气系统局地性的LBGM能弥补BGM的不足;但是,在模式积分12 h以后,具有更多大尺度特征扰动的DOWN优于区域模式中的增长模繁殖法BGM和LBGM,说明经过初始误差快速增长一段时间后,大尺度扰动开始起主要作用。而具有不同尺度扰动信息的BLEND方案则兼具LBGM和DOWN的优势,几乎在整个预报时段离散度较高且概率预报评分较好,体现出混合扰动的优越性。以上结果进一步说明,初始扰动的尺度特征在暖区暴雨的集合预报效果中具有关键性的作用,因而通过调整初始扰动的尺度信息来优化集合预报性能的混合扰动思想,在业务上具有一定的指导意义和推广价值。 相似文献
4.
《高原气象》2017,(3)
基于WRF模式构建集合更新预报系统,利用Haar小波分解方法分析了北京"7·21"特大暴雨过程中三种初始扰动方案所构造集合扰动的多尺度特征,基于此探讨了混合初始扰动方法的可行性,并对比了三种扰动对误差的模拟能力。其中扰动方案一是由集合转换卡尔曼滤波方法(ETKF)对NCEP全球集合预报的分析扰动更新后得到,扰动方案二(DOWN)是直接由NCEP全球集合预报扰动插值到试验所设置的模式网格中得到,而扰动方案三(BLEND)则是将上述二者通过Barnes滤波进行尺度混合后得到。结果表明:各组扰动的能量均随时间增长,其中包含分析不确定性的ETKF扰动在预报中前期有较高的中小尺度能量,而DOWN扰动有较高的大尺度能量且能量增长速度明显快于ETKF,二者能量的大值区最终都向中尺度(64~128 km)部分发展,混合后的扰动(BLEND)能量在预报中前期增长速度最快,综合表现最优。从扰动成分来看,ETKF和DOWN中在预报前期可以快速增长的部分均集中在8~32 km的小尺度上,64~128 km部分的中小尺度的扰动信息增长缓慢,而256 km的中尺度信息则很快被耗散,这为如何选取合理的滤波波段构造多尺度混合扰动提供了依据。从降水预报结果来看,控制预报误差主要集中在降水的大值区,虚假初始扰动会导致预报初期产生虚假降水区;在暖区降水阶段,扰动对误差的模拟能力较弱,而在锋面降水阶段,扰动对误差的模拟能力明显提高,总体来看大尺度的误差较难模拟,三种方案中BLEND对误差的模拟能力最强;根据扰动-误差的相关分析同样验证了BLEND在误差模拟能力方面的优势;在降水预报TS评分方面,各组集合试验均优于控制试验,其中BLEND的效果略优于ETKF和DOWN。 相似文献
5.
初始扰动对一次华南暴雨预报的影响的研究 总被引：2，自引：1，他引：1
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。 相似文献
6.
观测资料与模式的协调性对同化效果的影响 总被引：2，自引：0，他引：2
针对资料同化时模式与观测资料不协调导致的同化失效问题, 利用国家气候中心NCCo简化海气耦合模式, 基于一种提取观测资料中与模式相协调信息分量 (也称模式可协调信息) 的资料重构方法, 探讨了观测资料和模式的协调性对资料同化效果的影响。结果发现: 利用简单海气耦合模式同化海表温度距平资料时, 由于模式与资料不匹配使同化后的初始场中产生与模式动力特征不协调的小尺度扰动, 这些小扰动在预报阶段会迅速增长, 污染预报结果, 使得预报失败。研究还发现, 无论在耦合或不耦合同化形式下, 对海温资料中的模式可协调分量进行同化时, 预报效果明显好于对原观测资料的同化。其缘于对观测海温中模式可协调信息分量同化生成的初始场中, 消除了与模式动力特征不协调的小尺度扰动, 突出了原始资料中与模式协调的ENSO尺度信息分量, 改善了初始场与模式的协调性, 从而提高了模式的预报技巧。 相似文献
7.
近二十年来暴雨和强对流可预报性研究进展 总被引：1，自引：0，他引：1
大气可预报性研究是开展天气、气候预测的基础科学问题。全球变暖背景下,近年暴雨和强对流等中小尺度灾害性天气频发,如何深入认识其可预报性问题成为了天气领域研究热点,也是制约数值天气预报模式能力提升的重要因素。本文在简要回顾国内外大气可预报性研究历程的基础上,重点对近二十年（1999～2018）国际上关于暴雨和强对流可预报性方面的最新研究进展进行了系统的综述和归纳。主要包括:中小尺度可预报性研究的主要方法和评估手段及其与传统大尺度天气可预报性研究的差异,初始误差增长机制的几种主要观点及其争论（误差升尺度、误差降尺度、升降尺度并存）,数值模式误差和对流环境误差对实际预报性的影响,以及最近的中尺度可预报性科学观测试验进展等。最后,对暴雨、强对流可预报性研究存在的问题、未来发展方向进行了简要的讨论和展望。 相似文献
8.
根据地形特征,将西南地区划分为高原区、边坡区和盆地区,引入统计学"不稳定度"定量描述模式预报稳定性,对2016年6月—2017年9月全球中期天气预报(GRAPES_GFS)和欧洲中期天气预报中心(EC)在西南地区的高层形势场、主要的天气影响系统和地面要素预报性能进行了主客观检验,一定程度揭示了GRAPES_GFS和EC在西南地区的预报稳定性、地形的影响以及二模式预报性能的异同。结果显示:GRAPES_GFS高空高度场、温度场预报不稳定度分布呈北高南低型,相对湿度、风速预报不稳定度大值区在高原边缘;各要素预报不稳定度季节性周期最为显著,其位相和振幅因要素不同而有所不同;地形主要影响温度和风向预报误差值,但对相对湿度和风速预报的影响则体现在误差随时效的增长速率差异上;"漏报"是模式对西南地区天气系统的主要预报误差源,"低报"则是模式对西南地区2 m温度预报误差的最大来源;模式对西南地区降水落区预报有效率大约为50%,但强度预报通常偏低。EC与GRAPES_GFS的误差特征没有本质区别,但EC误差更小,稳定性更高。 相似文献
9.
1.引言在过去25年中,数值天气预报模式以及观测资料和分析系统的改进已使天气尺度环流的业务预报取得了稳定的改进(Shuman,1978)。但是,众所周知,在运动的尺度不能分辨(或不能观测)情况下,由于初始误差的增长,最终将影响所有尺度的运动,使大气运动的可预报性有一个固有界限。即使“完美(Perfect)”的数值模式也是这样(完美模式是指一种不增加预报误差的模式)。 相似文献
10.
集合预报初始扰动能否准确反映预报误差的结构特征是决定区域集合预报质量的关键因素之一。本文针对GRAPES区域数值预报模式,发展设计了一种基于资料同化思想的混合尺度初始扰动构造新方案。该方案以全球大尺度信息为背景场,区域模式预报作为观测资料,借助GRAPES三维变分同化系统,将高质量的全球大尺度信息与区域模式预报中质量较高的中小尺度信息有效融合,构造混合尺度区域集合预报初始扰动,并通过个例试验和批量试验,比较分析了新方案和原区域集合预报的性能。试验结果表明,基于资料同化构造的初始扰动能够有效融合全球大尺度信息和中小尺度天气系统的信息,其降水概率预报更具参考价值。总体上看,区域集合预报混合初始扰动新方案能够较好地改进区域集合预报质量,尤其是对高度场和温度场效果更为显著,但对风场的集合预报性能影响略小。 相似文献
11.
The article is to report some results of numerical experiments on the error growth and the atmosphericpredictability Experiments with two-level global baroclinic primitive equation spectral model have mainresults as follows.The magnitude of initial errors directly affects the error growth,but its distributionform has little effect on the growth.The loss of predictability resulting from small-scale error is much greaterthan that from large-scale error.The small-scale error rapidly grows and is transferred to the large-scaleerror by interaction between different scale waves,which stimulates the growth of error for the whole systemOrographic forcing restrains planetary-scale error(wavenumbers 0—3)but enhances the small-scale error(wavenumbers 8 or greater).Hence,orographic effects on the error growth closely depend on the characteris-tic scale of initial errors,and there may be a critical wavenumber between 4 and 7.The error growth is great-er in Northern Hemisphere than in Southern Hemisphere if initial errors are the same.In the end we givesome discussions about model,initialization scheme,etc.,to improve model prediction. 相似文献
12.
Climate predictability on interannual to decadal time scales: the initial value problem 总被引：1，自引：1，他引：0
M. Collins 《Climate Dynamics》2002,19(8):671-692
Any initial value forecast of climate will be subject to errors originating from poorly known initial conditions, model imperfections, and by "chaos" in the sense that, even if the initial conditions were perfectly known, infinitesimal errors can amplify and spoil the forecast at some lead time. Here the latter source of error is examined using a "perfect model" approach whereby small perturbations are made to a coupled atmosphere-ocean general circulation model and the spread of nearby model trajectories, on time and space scales appropriate to seasonal-decadal climate variability, is measured to assess the lead time at which the error saturates. The study therefore represents an estimate of the upper limit of the predictability of climate (appropriate to the initial value problem) given a perfect model and near perfect knowledge of the initial conditions. It is found that, on average, surface air temperature anomalies are potentially predictable on seasonal to interannual time scales in the tropical regions and are potentially predictable on decadal time scales over the ocean in the North Atlantic. For mid-latitude surface air temperature anomalies over land, model trajectories rapidly diverge and there is little sign of any potential predictability on time scales greater than a season or so. For mean sea level pressure anomalies, there is potential predictability on seasonal time scales in the tropics, and for some global scale annual-decadal anomalies, although not those associated with the North Atlantic Oscillation. For precipitation, the only potential for predictability is for seasonal time anomalies associated with the El-Niño Southern Oscillation. For the majority of the highly populated regions of the world, climate predictability on interannual to decadal time scales based in the initial value approach is likely to be severely limited by chaotic error growth. It is found however that there can be cases in which the potential predictability can be higher than average indicating that there is perhaps some utility in making initial value forecasts of climate in those regions which show low predictability on average. 相似文献
13.
The numerical simulations of Baumhefner (1971, 1972) and Miyakoda and Umscheid (1973) have shown that a “wall” placed at or near the equator has a serious effect on Northern Hemisphere forecasts after 10–14 days. In practice, however, numerical models have available some information from the Southern Hemisphere. The question is posed, “How much information from the Southern Hemisphere is necessary to yield a forecast for the Northern Hemisphere which is more accurate than that obtained by integrating over the Northern Hemisphere alone?” A simple numerical experiment demonstrates that a global model in which only the largest scales of the Southern Hemisphere are known at initial time yields a more accurate forecast for the Northern Hemisphere than a hemispheric model. 相似文献
14.
Evhen Yakimiw 《大气与海洋》2013,51(4):653-658
Abstract Numerical simulation experiments published in 1974 by Daley have been repeated with a much higher resolution, spectral, shallow water model. With a forecast period extending toll d, it is shown that a global model in which only the largest scales are used at initial time in the Southern Hemisphere yields a more accurate forecast for the Northern Hemisphere than a hemispheric model does. Compared with a uniform high‐resolution, global model, the error in the Northern Hemisphere forecast is high in the ultra‐long waves but decreases rather rapidly while the resolution of the initial Southern Hemispheric data is increased. 相似文献
15.
初边界条件不确定性对AREM模拟一次华南致洪暴雨的影响 总被引：1，自引：0，他引：1
利用AREM（advanced regional E-gridη-coordinates model）模式,针对2005年6月21日发生在华南的一次特大致洪暴雨过程,研究了模式初始场和边界场的不确定性对AREM模拟大暴雨过程的影响。研究表明：模式初始场和边界场对模式模拟降水的不同时段影响存在明显差异,初始扰动误差越大模式误差也越大;误差增长先在中小尺度内伴随着湿对流不稳定,且增长极其迅速,接着向大尺度传播,由于对流有效位能的逐渐释放,大气不稳定度降低,误差在大尺度上增长缓慢;在初始场和边界场相同精度的情况下,增加边界场的中尺度信息,尤其是400 km以下尺度的信息,比增加初始场的中尺度信息更能有效抑制误差的增长。 相似文献
16.
Xiaoran ZHUANG Jinzhong MIN Liu ZHANG Shizhang WANG Naigeng WU Haonan ZHU 《大气科学进展》2020,37(8):893-911
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. 相似文献
17.
In this study, the relationship between the limit of predictability and initial error was investigated using two simple chaotic systems:the Lorenz model, which possesses a single characteristic time scale, and the coupled Lorenz model, which possesses two different characteristic time scales. The limit of predictability is defined here as the time at which the error reaches 95% of its saturation level; nonlinear behaviors of the error growth are therefore involved in the definition of the limit of predictability. Our results show that the logarithmic function performs well in describing the relationship between the limit of predictability and initial error in both models, although the coefficients in the logarithmic function were not constant across the examined range of initial errors. Compared with the Lorenz model, in the coupled Lorenz model-in which the slow dynamics and the fast dynamics interact with each other-there is a more complex relationship between the limit of predictability and initial error. The limit of predictability of the Lorenz model is unbounded as the initial error becomes infinitesimally small; therefore, the limit of predictability of the Lorenz model may be extended by reducing the amplitude of the initial error. In contrast, if there exists a fixed initial error in the fast dynamics of the coupled Lorenz model, the slow dynamics has an intrinsic finite limit of predictability that cannot be extended by reducing the amplitude of the initial error in the slow dynamics, and vice versa. The findings reported here reveal the possible existence of an intrinsic finite limit of predictability in a coupled system that possesses many scales of time or motion. 相似文献
18.
In this paper we explore the impact of atmospheric nonlinearities on the optimal growth of initial condition error of El Niño and the Southern Oscillation (ENSO) prediction using singular vector (SV) analysis. This is performed by comparing and analyzing SVs of two hybrid coupled models (HCMs), one composed of an intermediate complexity dynamical ocean model coupled with a linear statistical atmospheric model, and the other one with the same ocean model coupled with a nonlinear statistical atmosphere. Tangent linear and adjoint models for both HCMs are developed. SVs are computed under the initial conditions of seasonal background and actual ENSO cycle simulated by the ocean model forced with the real wind data of 1980–1999. The optimization periods of 3, 6 and 9 months are individually considered. The results show that the first SVs in both HCMs are very similar to each other, characterized by a central east-west dipole pattern spanning over the entire tropical Pacific. The spatial patterns of the leading SV in both HCMs are not sensitive to optimization periods and initial time. However, the first singular value, indicating the optimal growth rate of prediction error, displays considerable differences between the two HCMs, indicating a significant impact of atmospheric nonlinearities on the optimal growth of ENSO prediction error. These differences are greater with increasing optimization time, suggesting that the impact of atmospheric nonlinearities on the optimal growth of prediction error becomes larger for a longer period of prediction. 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.
To investigate the impact of various types of data on medium-range forecasts, observing system experiments are performed using an assimilation algorithm based on the National Centers for Environmental Prediction (NCEP)/Department of Energy (DOE) reanalysis system. Data-denial experiments for radiosonde, satellite, aircraft, and sea surface observations, and selected data experiments for radiosonde and surface data, are conducted for the boreal summer of 1997 and the boreal winter of 1997/1998. The data assimilation system used in this study is remarkably dependent on radiosonde data, which provides information about the three-dimensional structure of the atmosphere. As expected, the impact of radiosonde observations on medium-range forecasts is strongly positive over the Northern Hemisphere and tropics, whereas the satellite system is most beneficial over the Southern Hemisphere. These results are also found in experiments simulating historical changes in observation systems. Over the tropics, assimilation without radiosonde observations generates unbalanced analyses resulting in unrealistic forecasts that must be corrected by the forecast model. Forecasts based on analysis from the observation data before the era of radiosonde observation are found to be less meaningful. In addition, the impacts on forecasts are closely related to the geographical distribution of observation data. The memory of observation data embedded in the analysis tends to persist throughout forecasts. However, cases exist where the effect of forecast error growth is more dominant than that of analysis error, e.g., over East Asia in summer, and where the deficiency in observations is supplemented or the imbalance in analysis is adjusted by the forecast model during the period of forecasts. Forecast error growth may be related to the synoptic correction performed by the data assimilation system. Over data-rich areas, analysis fields are corrected to a greater extent by the data assimilation system than are those over data-poor areas, which can cause the forecast model to produce more forecast errors in medium-range forecasts. It is found that even one month per season is sufficient for forecast skill verification in data impact experiments. Additionally, the use of upper-air observations is found to benefit areas that are downstream of observation data-rich areas. 相似文献