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1.
The ensemble Kalman filter (EnKF), as a unified approach to both data assimilation and ensemble forecasting problems, is used to investigate the performance of dust storm ensemble forecasting targeting a dust episode in the East Asia during 23–30 May 2007. The errors in the input wind field, dust emission intensity, and dry deposition velocity are among important model uncertainties and are considered in the model error perturbations. These model errors are not assumed to have zero-means. The model error me... 相似文献
2.
In this study, we assimilated sea surface temperature (SST) data of the past 120 years into an oceanic general circulation model (OGCM) for El Niño-southern oscillation (ENSO) retrospective predictions using ensemble Kalman filter (EnKF). It was found that the ensemble covariance matrix in EnKF can act as a time-variant transfer operator to project the SST corrections onto the subsurface temperatures effectively when initial perturbations of ensemble were constructed using vertically coherent random fields. As such the increments of subsurface temperatures can be obtained via the transfer operator during assimilation cycles. The results show that the SST assimilation improves the model simulation skills significantly, not only for the SST anomalies over the whole assimilated domain, but also for the subsurface temperature anomalies of the upper 100 m over the tropical Pacific off the equator. Along the equator, the improvement of the assimilation is confined within the mixing layer because strong upwelling motions there prevent the downward transfer of SST information. The retrospective prediction skills of ENSO over the past 120 years from 1881 to 2000 were significantly improved by the SST assimilation at all leads of 1–12 months, especially for the 3–6 months leads, compared with those initialized by the control run without assimilation. The skilful predictions by the assimilation allow us to further study ENSO predictability using this coupled model. 相似文献
3.
Ensemble hindcasts of ENSO events over the past 120 years using a large number of ensembles 总被引：1，自引：0，他引：1
Based on an intermediate coupled model (ICM), a probabilistic ensemble prediction system (EPS) has been developed. The ensemble Kalman filter (EnKF) data assimilation approach is used for generating the initial ensemble conditions, and a linear, first-order Markov-Chain SST anomaly error model is embedded into the EPS to provide model-error perturbations. In this study, we perform ENSO retrospective forecasts over the 120 year period 1886–2005 using the EPS with 100 ensemble members and with initial conditi... 相似文献
4.
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
5.
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
6.
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
7.
Jieshun Zhu Bohua Huang Magdalena A. Balmaseda James L. Kinter III Peitao Peng Zeng-Zhen Hu Lawrence Marx 《Climate Dynamics》2013,41(9-10):2785-2795
Currently, ensemble seasonal forecasts using a single model with multiple perturbed initial conditions generally suffer from an “overconfidence” problem, i.e., the ensemble evolves such that the spread among members is small, compared to the magnitude of the mean error. This has motivated the use of a multi-model ensemble (MME), a technique that aims at sampling the structural uncertainty in the forecasting system. Here we investigate how the structural uncertainty in the ocean initial conditions impacts the reliability in seasonal forecasts, by using a new ensemble generation method to be referred to as the multiple-ocean analysis ensemble (MAE) initialization. In the MAE method, multiple ocean analyses are used to build an ensemble of ocean initial states, thus sampling structural uncertainties in oceanic initial conditions (OIC) originating from errors in the ocean model, the forcing flux, and the measurements, especially in areas and times of insufficient observations, as well as from the dependence on data assimilation methods. The merit of MAE initialization is demonstrated by the improved El Niño and the Southern Oscillation (ENSO) forecasting reliability. In particular, compared with the atmospheric perturbation or lagged ensemble approaches, the MAE initialization more effectively enhances ensemble dispersion in ENSO forecasting. A quantitative probabilistic measure of reliability also indicates that the MAE method performs better in forecasting all three (warm, neutral and cold) categories of ENSO events. In addition to improving seasonal forecasts, the MAE strategy may be used to identify the characteristics of the current structural uncertainty and as guidance for improving the observational network and assimilation strategy. Moreover, although the MAE method is not expected to totally correct the overconfidence of seasonal forecasts, our results demonstrate that OIC uncertainty is one of the major sources of forecast overconfidence, and suggest that the MAE is an essential component of an MME system. 相似文献
8.
A Multivariate Empirical Orthogonal Function-Based Scheme for the Balanced Initial Ensemble Generation of an Ensemble Kalman Filter 
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下载免费PDF全文 The initial ensemble perturbations for an ensemble data assimilation system are expected to reasonably sample model uncertainty at the time of analysis to further reduce analysis uncertainty. Therefore, the careful choice of an initial ensemble perturbation method that dynamically cycles ensemble perturbations is required for the optimal performance of the system. Based on the multivariate empirical orthogonal function (MEOF) method, a new ensemble initialization scheme is developed to generate balanced initial perturbations for the ensemble Kalman filter (EnKF) data assimilation, with a reasonable consideration of the physical relationships between different model variables. The scheme is applied in assimilation experiments with a global spectral atmospheric model and with real observations. The proposed perturbation method is compared to the commonly used method of spatially-correlated random perturbations. The comparisons show that the model uncertainties prior to the first analysis time, which are forecasted from the balanced ensemble initial fields, maintain a much more reasonable spread and a more accurate forecast error covariance than those from the randomly perturbed initial fields. The analysis results are further improved by the balanced ensemble initialization scheme due to more accurate background information. Also, a 20-day continuous assimilation experiment shows that the ensemble spreads for each model variable are still retained in reasonable ranges without considering additional perturbations or inflations during the assimilation cycles, while the ensemble spreads from the randomly perturbed initialization scheme decrease and collapse rapidly. 相似文献
9.
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
10.
A dual-resolution(DR) version of a regional ensemble Kalman filter(EnKF)-3D ensemble variational(3DEnVar) coupled hybrid data assimilation system is implemented as a prototype for the operational Rapid Refresh forecasting system. The DR 3DEnVar system combines a high-resolution(HR) deterministic background forecast with lower-resolution(LR) EnKF ensemble perturbations used for flow-dependent background error covariance to produce a HR analysis. The computational cost is substantially reduced by running the ensemble forecasts and EnKF analyses at LR. The DR 3DEnVar system is tested with 3-h cycles over a 9-day period using a 40/13-km grid spacing combination. The HR forecasts from the DR hybrid analyses are compared with forecasts launched from HR Gridpoint Statistical Interpolation(GSI) 3D variational(3DVar)analyses, and single LR hybrid analyses interpolated to the HR grid. With the DR 3DEnVar system, a 90% weight for the ensemble covariance yields the lowest forecast errors and the DR hybrid system clearly outperforms the HR GSI 3DVar.Humidity and wind forecasts are also better than those launched from interpolated LR hybrid analyses, but the temperature forecasts are slightly worse. The humidity forecasts are improved most. For precipitation forecasts, the DR 3DEnVar always outperforms HR GSI 3DVar. It also outperforms the LR 3DEnVar, except for the initial forecast period and lower thresholds. 相似文献
11.
基于集合Kalman滤波数据同化的热带气旋路径集合预报研究 总被引：1，自引：2，他引：1
构建了一个基于集合Kalman滤波数据同化的热带气旋集合预报系统，通过积云参数化方案和边界层参数化方案的9个不同组合，采用MM5模式进行了不同时间的短时预报。对预报结果使用“镜像法”得到18个初始成员，为同化提供初始背景集合。将人造台风作为观测场，同化后的结果作为集合预报的初值，通过不同参数组合的MM5模式进行集合预报。对2003～2004年16个台风个例的分析表明，初始成员产生方法能够对热带气旋的要素场、中心强度和位置进行合理扰动。同化结果使台风强度得到加强，结构更接近实际。基于同化的集合路径预报结果要优于未同化的集合预报。使用“镜像法”增加集合成员提高了预报准确度，路径预报误差在48小时和72小时分别低于200 km和250 km。 相似文献
12.
Using the Met Office Global and Regional Ensemble Prediction System (MOGREPS) implemented at the Korea Meteorological Administration (KMA), the effect of doubling the ensemble size on the performance of ensemble prediction in the warm season was evaluated. Because a finite ensemble size causes sampling error in the full forecast probability distribution function (PDF), ensemble size is closely related to the efficiency of the ensemble prediction system. Prediction capability according to doubling the ensemble size was evaluated by increasing the number of ensembles from 24 to 48 in MOGREPS implemented at the KMA. The initial analysis perturbations generated by the Ensemble Transform Kalman Filter (ETKF) were integrated for 10 days from 22 May to 23 June 2009. Several statistical verification scores were used to measure the accuracy, reliability, and resolution of ensemble probabilistic forecasts for 24 and 48 ensemble member forecasts. Even though the results were not significant, the accuracy of ensemble prediction improved slightly as ensemble size increased, especially for longer forecast times in the Northern Hemisphere. While increasing the number of ensemble members resulted in a slight improvement in resolution as forecast time increased, inconsistent results were obtained for the scores assessing the reliability of ensemble prediction. The overall performance of ensemble prediction in terms of accuracy, resolution, and reliability increased slightly with ensemble size, especially for longer forecast times. 相似文献
13.
Improvement of seasonal forecasts with inclusion of tropical instability waves on initial conditions 总被引：2，自引：0，他引：2
Positive impacts of tropical instability waves (TIWs) in initial conditions on seasonal forecasts are investigated using a air-sea coupled GCM. Due to coarse observational networks and deficiencies in widely-used initialization methods (e.g. 3DVAR or OI methods), TIW variability in oceanic initial conditions is excessively suppressed. It ruins the interaction between TIWs and climate states, therefore, degrades the climate forecast skills. To settle this problem, TIW patterns obtained from free integration is added to the spatially-smoothed initial conditions to simulate realistic seasonal TIW variability (TIWV). Through 20-year ensemble forecast experiments, it is shown that seasonal TIWV with TIWs-seeded initial conditions is significantly stronger until 2-month lead time. In addition, enhanced TIWV amplifies nonlinear relationship between TIWs and ENSO, which leads realistic simulation of the El Ni?o-La Ni?a asymmetry. As a result of better ENSO simulation, correlation improvement of simulated NINO3 index with TIWs-seeded initial conditions is over 0.1 at 4-month lead time. 相似文献
14.
为描述GRAPES全球模式初始条件的不确定性,基于适合集合预报应用的GRAPES全球奇异向量技术,依据大气初始误差符合正态分布的特征,采用高斯取样奇异向量来构造全球集合预报初始扰动,在此基础上建立了GRAPES全球集合预报系统(GRAPES-GEPS)。利用GRAPES全球同化分析场,对采用初始扰动的GRAPES-GEPS连续试验预报结果进行检验和分析。结果表明:GRAPES-GEPS中高度场、风场及温度场预报的集合离散度能有效快速增加,集合平均均方根误差与集合离散度的关系合理;相对控制预报的均方根误差,集合平均的预报优势在预报中期非常显著。为进一步体现GRAPES-GEPS中模式物理过程的不确定性,发展了模式物理过程倾向随机扰动技术(SPPT)。试验结果表明:SPPT方案的应用有效提高了GRAPES-GEPS在南、北半球和热带地区等压面要素预报的集合离散度,同时一定程度减小了集合平均误差,进而改进了集合平均误差与集合离散度的关系,其中SPPT方案在热带地区的改进最为显著。本文发展的基于奇异向量的初始扰动方法和模式扰动SPPT方案在中国气象局2018年12月业务化运行的GRAPES-GEPS中得到了应用。 相似文献
15.
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
16.
The authors apply the technique of conditional nonlinear optimal perturbations (CNOPs) as a means
of providing initial perturbations for ensemble forecasting by using a barotropic quasi-geostrophic
(QG) model in a perfect-model scenario. Ensemble forecasts for the medium range (14 days) are made
from the initial states perturbed by CNOPs and singular vectors (SVs). 13 different cases have been
chosen when analysis error is a kind of fast growing error. Our experiments show that the introduction
of CNOP provides better forecast skill than the SV method. Moreover, the spread-skill relationship
reveals that the ensemble samples in which the first SV is replaced by CNOP appear superior to those
obtained by SVs from day 6 to day 14. Rank diagrams are adopted to compare the new method with the
SV approach. The results illustrate that the introduction of CNOP has higher reliability for
medium-range ensemble forecasts. 相似文献
17.
集合变换卡尔曼滤波(ensemble transform Kalman filter, ETKF)是一种有效的集合预报初始扰动构造方案。但是,有限的集合样本、相同的集合成员设置以及预报模式误差等可能会使两个距离较远的状态变量产生虚假相关,从而影响ETKF集合扰动的质量。为了有效解决远距离虚假相关问题,将局地化思想引入ETKF方案。本文针对GRAPES区域集合预报系统(GRAPES REPS),对ETKF初值扰动局地化方案的效果进行了试验分析,为进一步改善和优化局地化方案(LETKF方案)提供依据。通过一周的连续试验,从暴雨个例、集合预报多种评分检验等方面分析了LETKF初始扰动方案所产生的集合预报质量。结果表明,区域集合预报中集合变换卡尔曼滤波初始扰动的局地化方案能够更加合理地捕捉到快速增长的分析误差的物理结构,更准确地再现数值模式预报误差的线性与非线性传播和演变特征。该局地化方案可以较好地改进预报质量,提高降水预报的准确率,尤其是针对小雨、中雨、暴雨量级的预报。相对于现有区域集合预报的业务系统GRAPES REPS,基于局地化ETKF初始扰动方案的区域集合预报具有较明显的优势。总体来看,LETKF初始扰动方案可更好地改善区域集合预报的质量。 相似文献
18.
风暴尺度天气下利用集合卡尔曼滤波模拟多普勒雷达资料同化试验 II. 考虑模式误差的情形 总被引：8，自引：6，他引：2
文章的第I部分(兰伟仁等, 2010), 利用模拟雷达资料在假定模式无误差的情况下进行了一系列的集合卡尔曼滤波(EnKF)敏感性试验, 验证了EnKF方法在风暴尺度天气资料同化中的作用。本文继续探讨EnKF在有显著模式误差的情况下同化模拟多普勒雷达资料的效果问题。试验中假定模式误差主要来源于微物理过程参数化的不确定性。结果表明: 模式误差在不同程度上影响了EnKF分析的效果, 对微物理量的影响尤其明显; 在EnKF分析中, 利用微物理过程参数化集合的方法来考虑模式误差, 对速度场、 位温场以及比湿场有较明显的正作用, 但对于微物理量场分析效果较差; 若包含控制试验的微物理过程参数化方案, 则EnKF对所有变量都有正效果, 随着同化循环次数的增加, 分析结果更加合理; 只考虑冰相过程的微物理过程参数化方案的集合, 分析效果进一步提高。 相似文献
19.
风暴尺度天气下利用集合卡尔曼滤波模拟多普勒雷达资料同化试验I.不考虑模式误差的情形 总被引：11，自引：8，他引：3
本文在假定模式无偏差的情况下, 利用一次风暴过程的模拟多普勒雷达资料进行一系列风暴天气尺度的集合卡尔曼滤波资料同化试验, 检验集合卡尔曼滤波在风暴天气尺度资料同化方面的效果, 并验证各集合卡尔曼滤波参数对同化效果的影响。试验结果表明, 集合卡尔曼滤波能有效地应用于风暴尺度的资料同化; 40个集合成员以及6 km的局地化尺度能较好地滤除采样误差造成的虚假相关, 同时可以将观测信息传递到无观测的模式格点; 利用背景场加上空间平滑的高斯型随机扰动生成初始成员的方式较未经过平滑的方式有更好的分析效果; 背景场扰动方法能够提高样本的离散度; 只同化反射率的同化试验表明, 反射率的同化效果较明显, 也证明了集合卡尔曼滤波在非常规资料同化中的作用; 增加径向风资料同化的效果优于只进行反射率同化的结果。 相似文献
20.
提高月预报业务水平的动力相似集合方法 总被引：3，自引：0，他引：3
针对基于大气环流模式的月预报问题,提出了一种能有效减小预报误差并提高预报技巧的动力相似集合预报新方法。该方法着眼于动力模式与统计经验的内在结合,在模式积分过程中通过提取大气环流历史相似性信息,对模式误差进行参数化处理,形成多个时变的相似强迫量来扰动生成预报的集合成员。将这一集合新方法应用到中国国家气候中心业务大气环流模式(BCC AGCM1.0),一组10 a准业务环境下回报试验结果显示,相比于业务集合预报,动力相似集合预报方法能有效改进模式对于大气环流的纬向平均、超长波和长波预报,从而有效提高了月平均环流预报技巧(几乎达到业务可用标准)和逐日环流预报技巧,并显著降低了预报误差,合理增加集合离散度,使二者配置关系得以改善,有望在业务预报中应用。 相似文献