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1.
EnSRF雷达资料同化在一次飑线过程中的应用研究 总被引：3，自引：1，他引：2
本文利用包含复杂冰相微物理过程的WRF（Weather Research and Forecasting）模式,针对2007年4月23日发生在我国华南地区的一次典型飑线天气过程,分别进行了确定性预报和集合预报试验,发现确定性预报能大致捕捉到飑线系统的发生发展过程,但对飑线后部的层云区模拟效果较差。集合预报能够有效地减少模式的不确定性,大部分集合成员对飑线的模拟效果优于确定性预报。进一步将集合预报得到的40个成员作为背景场,采用EnSRF（Ensemble Square Root Filter）同化多普勒天气雷达资料,并将分析得到的集合作为初始场进行集合预报,通过与未同化雷达资料的集合对比,考察了EnSRF同化多部雷达资料对飑线系统的影响。结果表明:EnSRF雷达资料同化增加了模式初始场的中小尺度信息,大部分集合成员的分析场能够较准确地再现飑线的热力场、动力场和微物理场的细致特征,并且模拟出飑线后部的层云结构。通过对EnSRF分析的集合进行模拟发现,大部分集合成员较未同化雷达资料时模拟效果有明显改善。同化后的集合预报ETS（Equitable Threat Score）评分最高,其次是未同化的集合预报,确定性预报的最低。 相似文献
2.
敏感性试验表明集合变换卡尔曼滤波(Ensemble Transform Kalman Filter,ETKF)方法在混合(Hybrid)同化过程中易受观测资料数量变化的影响而产生较大程度的协方差震荡,从而可能导致系统不稳定。为设计一种简便、稳定的Hybrid同化系统,构建了一种基于物理控制变量扰动及多物理参数化方案的Hybrid同化及预报系统。本系统随着循环的进行,不断对Hybrid同化分析场进行控制变量扰动得到集合成员初始场,并且对各集合成员采用不同物理参数化方案以更合理地表征背景场的误差特征。连续10 d的循环同化及预报试验表明,本文同化方案效果明显优于三维变分方案,动力场的整体同化和预报效果与ETKF方案基本相当。本方案相比于ETKF方法不受观测波动影响,在没有经任何参数调试情况下,取得了良好同化和预报效果,为Hybrid同化的便捷运行提供了一种稳定可靠的手段。 相似文献
3.
Preliminary Comparison of the CMA, ECMWF, NCEP, and JMA Ensemble Prediction Systems 总被引：1，自引：0，他引：1 
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下载免费PDF全文 Based on The Observing System Research and Predictability Experiment (THORPEX) Interactive Grand Global Ensemble (TIGGE) dataset,using various verification methods,the performances of four typical ense... 相似文献
4.
集合卡尔曼滤波同化多普勒雷达资料的观测系统模拟试验 总被引：4，自引：1，他引：3
本文将集合卡尔曼滤波同化技术应用到对流尺度系统中,实施了基于WRF模式的同化单部多普勒雷达径向风和反射率因子的观测系统模拟试验,验证了其在对流尺度中应用的可行性和有效性,并对同化系统的特性进行了探讨。试验表明:WRF-EnKF雷达资料同化系统能较准确分析模式风暴的流场、热力场、微物理量场的细致特征;几乎所有变量的预报和分析误差经过同化循环后都能显著下降,同化分析基本上能使预报场在各层上都有所改进,对预报场误差较大层次的更正更为显著;约8个同化循环后,EnKF能在雷达反射率、径向风观测与背景场间建立较可靠的相关关系,使模式各变量场能被准确分析更新,背景场误差协方差在水平方向和垂直方向都有着复杂的结构,是高度非均匀、各项异性和流依赖的;集合平均分析场做的确定性预报在短时间内能较好保持真值场风暴的细节结构,但预报误差增长较快。 相似文献
5.
选取2022年川渝地区发生的16个强降水个例开展对流尺度集合预报批量试验,并通过对31组初值采用不同集合成员数时的降水集合预报技巧进行检验评估和综合分析。结果表明:集合成员的降水预报技巧总体上大致相当,因而采用不同成员数时预报技巧差异也不明显;表征降水总体分布特征的Talagrand分布和预报失误概率以及表征降水概率预报技巧的相对作用特征面积随着成员数的增加而逐渐改进,但当成员数达到一定数值后继续增大成员数对预报改进不明显。总体而言,对流尺度集合预报成员数设置为16~18最适宜。 相似文献
6.
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
7.
两种样本生成方法对DRP-4DVar同化方法性能影响的比较及分析 总被引：1，自引：0，他引：1
降维投影四维变分同化方法(DRP-4DVar)利用历史预报的集合来统计背景误差协方差,并将分析变量投影到样本空间下求解代价函数,因而集合样本的质量对DRP-4DVar同化方法的性能有着重要影响.文中尝试使用三维变分(3DVar)控制变量的扰动方法来产生集合样本,并与原来的历史预报扰动方法做比较.历史预报扰动样本具有随流... 相似文献
8.
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
9.
The present study designs experiments on the direct assimilation of
radial velocity and reflectivity data collected by an S-band Doppler weather
radar (CINRAD WSR-98D) at the Hefei Station and the reanalysis data produced by
the United States National Centers for Environmental Prediction using the Weather
Research and Forecasting (WRF) model, the WRF model with a three-dimensional
variational (3DVAR) data assimilation system and the WRF model with an ensemble
square root filter (EnSRF) data assimilation system. In addition, the present
study analyzes a Meiyu front heavy rainfall process that occurred in the Yangtze
-Huaihe River Basin from July 4 to July 5, 2003, through numerical simulation.
The results show the following. (1) The assimilation of the radar radial velocity
data can increase the perturbations in the low-altitude atmosphere over the heavy
rainfall region, enhance the convective activities and reduce excessive simulated
precipitation. (2) The 3DVAR assimilation method significantly adjusts the
horizontal wind field. The assimilation of the reflectivity data improves the
microphysical quantities and dynamic fields in the model. In addition, the
assimilation of the radial velocity and reflectivity data can better adjust the
wind fields and improve the intensity and location of the simulated radar echo
bands. (3) The EnSRF assimilation method can assimilate more small-scale wind
field information into the model. The assimilation of the reflectivity data alone
can relatively accurately forecast the rainfall centers. In addition, the
assimilation of the radial velocity and reflectivity data can improve the
location of the simulated radar echo bands. (4) The use of the 3DVAR and EnSRF
assimilation methods to assimilate the radar radial velocity and reflectivity
data can improve the forecast of precipitation, rain-band areal coverage and the
center location and intensity of precipitation. 相似文献
10.
By sampling perturbed state vectors from each ensemble prediction run at properly selected time levels in the vicinity of the analysis time, the recently proposed time-expanded sampling approach can enlarge the ensemble size without increasing the number of prediction runs and, hence, can reduce the computational cost of an ensemble-based filter. In this study, this approach is tested for the first time with real radar data from a tornadic thunderstorm. In particular, four assimilation experiments were performed to test the time-expanded sampling method against the conventional ensemble sampling method used by ensemble- based filters. In these experiments, the ensemble square-root filter (EnSRF) was used with 45 ensemble members generated by the time-expanded sampling and conventional sampling from 15 and 45 prediction runs, respectively, and quality-controlled radar data were compressed into super-observations with properly reduced spatial resolutions to improve the EnSRF performances. The results show that the time-expanded sampling approach not only can reduce the computational cost but also can improve the accuracy of the analysis, especially when the ensemble size is severely limited due to computational constraints for real-radar data assimilation. These potential merits are consistent with those previously demonstrated by assimilation experiments with simulated data. 相似文献
11.
This study explores the use of the hierarchical ensemble filter to determine the localized influence of ob-servations in the Weather Research and Forecasting ensemble square root filtering (WRF-EnSRF) assimilation system. With error correlations between observations and background field state variables considered, the adaptive localization approach is applied to conduct a series of ideal storm-scale data assimilation experiments using simulated Doppler radar data. Comparisons between adaptive and empirical localization methods are made, and the feasibility of adaptive locali-zation for storm-scale ensemble Kalman filter assimilation is demonstrated. Unlike empirical localization, which relies on prior knowledge of distance between observations and background field, the hierarchical ensemble filter provides con-tinuously updating localization influence weights adaptively. The adaptive scheme improves assimilation quality during rapid storm development and enhances assimilation of reflectivity observations. The characteristics of both the observation type and the storm development stage should be considered when identifying the most appropriate localization method. Ultimately, combining empirical and adaptive methods can optimize assimilation quality. 相似文献
12.
Ensemble mean forecast skill and applications with the T213 ensemble prediction system 总被引：1，自引：0，他引：1
Ensemble forecasting has become the prevailing method in current operational weather forecasting. Although ensemble mean forecast skill has been studied for many ensemble prediction systems(EPSs) and different cases, theoretical analysis regarding ensemble mean forecast skill has rarely been investigated, especially quantitative analysis without any assumptions of ensemble members. This paper investigates fundamental questions about the ensemble mean, such as the advantage of the ensemble mean over individual members, the potential skill of the ensemble mean, and the skill gain of the ensemble mean with increasing ensemble size. The average error coefficient between each pair of ensemble members is the most important factor in ensemble mean forecast skill, which determines the mean-square error of ensemble mean forecasts and the skill gain with increasing ensemble size. More members are useful if the errors of the members have lower correlations with each other, and vice versa. The theoretical investigation in this study is verified by application with the T213 EPS. A typical EPS has an average error coefficient of between 0.5 and 0.8; the 15-member T213 EPS used here reaches a saturation degree of 95%(i.e., maximum 5% skill gain by adding new members with similar skill to the existing members) for 1–10-day lead time predictions, as far as the mean-square error is concerned. 相似文献
13.
Assimilation of Doppler Radar Observations with an Ensemble Square Root Filter：A Squall Line Case Study 下载免费PDF全文
下载免费PDF全文 The effectiveness of using an Ensemble Square Root Filter（EnSRF） to assimilate real Doppler radar observations on convective scale is investigated by applying the technique to a case of squall line on 12July 2005 in midwest Shandong Province using the Weather Research and Forecasting（WRF） model.The experimental results show that：（1） The EnSRF system has the potential to initiate a squall line accurately by assimilation of real Doppler radar data.The convective-scale information has been added into the WRF model through radar data assimilation and thus the analyzed fields are improved noticeably.The model spin-up time has been shortened,and the precipitation forecast is improved accordingly.（2） Compared with the control run,the deterministic forecast initiated with the ensemble mean analysis of EnSRF produces more accurate prediction of microphysical fields.The predicted wind and thermal fields are reasonable and in accordance with the characteristics of convective storms.（3） The propagation direction of the squall line from the ensemble mean analysis is consistent with that of the observation,but the propagation speed is larger than the observed.The effective forecast period for this squall line is about 5-6 h,probably because of the nonlinear development of the convective storm. 相似文献
14.
以GRAPES中尺度有限区模式作为试验模式, 从模式的不确定性方面来构造中尺度的集合预报, 重点考虑物理因子与初始条件的扰动作用。针对2004年7月10日北京城区的突发性暴雨过程进行了36 h的集合预报试验。结果表明:GRAPES模式可有效地捕捉到中尺度过程的信息; 中尺度集合预报是可行的, 可改进中尺度暴雨过程落区、强度的预报; 不同集合方案的预报结果各不相同, 同一方案各个成员的预报结果也有差异, 即存在适宜的离散度; 在离散度分析中发现在北京附近存在一个明显大值区, 且在大气中低层的垂直结构表现出一致性, 表明这一区域的预报不确定性很大。从集合检验结果中得到:单纯考虑模式物理扰动来构造中尺度集合预报系统有一定难度, 当加入初始场不确定信息后, 同时考虑模式的不确定性和初始场的不确定性, 有助于捕捉更多的中尺度系统的不确定信息, 有助于构造更为有效的中尺度集合预报系统。 相似文献
15.
基于集合卡尔曼变换与三维变分(ETKF-3DVAR)混合资料同化系统和欧洲中期天气预报中心(ECWMF)的全球集合预报,以"梅花"台风为例,分析了台风系统预报误差的流依赖特征,讨论了耦合系数在混合同化和预报中的敏感性及其对预报质量的影响。结果显示,台风系统的预报误差协方差具有显著的中小尺度结构特征,集合估计的预报误差协方差结构能够再现其流依赖属性。相对于3DVAR方案,混合资料同化方案的最优耦合系数对台风系统的分析和预报质量具有更好的改善;但不同的耦合系数对台风路径预报有明显的影响,不合适的耦合系数甚至可能导致更坏的结果,只有耦合了相对合适的预报误差协方差的流依赖信息,混合资料同化方案才可能对分析和预报质量有正效果。这表明在混合资料同化系统中,构造一种具有自适应能力的耦合权重函数,实现相对最优权重的自动选择,对充分发挥混合资料同化方案的潜在优势具有重要意义。 相似文献
16.
利用自主构建的基于风暴尺度的WRF-En SRF系统同化模拟多普勒雷达资料,讨论了微物理方案及其参数的不确定性对同化效果的影响。试验采用组合微物理方案以及扰动微物理方案中的参数的方法,结果表明,模式误差非常小甚至可以忽略时,使用单个微物理方案并扰动参数能够使真实风暴的主要特征在分析场中较未扰动参数得到更好地反映;存在模式误差时,使用单个微物理方案并扰动参数后,分析场中的各要素的分布较未扰动参数更加接近真实风暴,同化效果得到改进,且改进效果比模式误差非常小时更为明显;存在模式误差时,组合微物理方案并扰动参数后,分析场中对流云团的形态较未组合方案或未扰动参数更接近真实风暴,主要要素场的配置最能反映真实风暴的特征,同化效果最为理想。结果也表明,扰动参数时、参数扰动范围较小时,同化效果较优。 相似文献
17.
Hyo-Jong Song Ji-Hyun Ha In-Hyuk Kwon Junghan Kim Jihye Kwun 《Asia-Pacific Journal of Atmospheric Sciences》2018,54(1):337-350
This study illustrates the characteristics of the data assimilation system at the Korea Institute of Atmospheric Prediction Systems (KIAPS), based on the cubed-sphere grid system. The most interesting feature is the use of spherical harmonic functions defined on cubed-sphere grid points, which makes it possible to control the allowable physical wavenumber for the analysis increments. The relevant computational costs and parallel scalability are represented. The multiple-resolution approach is a distinguishable aspect of this data assimilation system. The wavenumber, up to which the analysis is conducted, increases as the outer iteration progresses. This multiresolution strategy is based on an investigation into the change of spectral components of analysis increments. The multi-resolution outer-loop provides cost-effective analysis-improvement, by explicitly controlling the analysis increments entered into the observation operator. To utilize the high-resolution deterministic forecast as a background state, it is subtracted from the forecast ensemble, to produce ensemble forecast perturbation that is hybridized with static background error covariance. Based on the cycled analysis experiments, the higher-resolution deterministic forecast is shown to preserve the high-frequency feature of the analysis increment relative to the ensemble mean forecast. 相似文献
18.
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
19.
尽管确定性预报不是集合预报系统(EPS)的主要目的和应用方向,但其每一个成员的预报表现决定了集合预报系统的预报性能,集合平均也是实际预报业务的一个重要参考指标。为此,利用2013~2015年5~10月的欧洲中期天气预报中心(ECMWF)集合预报系统的降水预报资料,CMORPH(NOAA Climate Prediction Center Morphing Method)卫星与全国3×10~4余个自动气象观测站的逐小时降水量融合资料,研究ECMWF集合预报系统对秦岭周边地区逐日降水的控制预报、成员预报、集合平均的预报能力,并探索提高降水集合平均预报性能的有效方法。主要结论如下:(1)无论是集合平均还是控制预报,整体上都较好的刻画了秦岭周边地区降水的空间形态,比较而言,控制预报能够更好的表现了降水的方差变化。(2)泰勒分析表明,集合平均的降水方差随预报时效增加单调减小,控制预报的方差变化随预报时效的增长振荡较小,其相关系数略优于集合平均。(3)技巧评分表明,集合平均使小雨(降水发生频次)的预报偏差显著增加,增大了空报率;使大雨以上的降水预报偏差减小,增大了漏报率,从而使得大多数情况下,集合平均TS(Threat Score)、ETS(Equitable Threat Score)评分低于控制及扰动成员预报。分析认为这主要是由于降水这一要素的偏态分布特性引起的。(4)集合平均的显著贡献在于能够较好的指示可能发生降水的空间位置。通过阈值限定,调整预报偏差,减少(增大)其对小雨(暴雨)的预报频率,能够使集合平均的TS、ETS评分大幅度提升,预报技巧显著优于成员预报和控制预报。目前,预报偏差Bias订正方法已成功应用于陕西省精细化格点预报系统中。 相似文献
20.
An Adaptive Estimation of Forecast Error Covariance
Parameters for Kalman Filtering Data Assimilation 总被引：1，自引：0，他引：1
Xiaogu ZHENG 《大气科学进展》2009,26(1):154-160
An adaptive estimation of forecast error covariance matrices is proposed for Kalman filtering
data assimilation. A forecast error covariance matrix is initially estimated using an ensemble of
perturbation forecasts. This initially estimated matrix is then adjusted with scale parameters that are
adaptively estimated by minimizing -2log-likelihood of observed-minus-forecast residuals. The proposed
approach could be applied to Kalman filtering data assimilation with imperfect models when the model
error statistics are not known. A simple nonlinear model (Burgers' equation model) is used to demonstrate
the efficacy of the proposed approach. 相似文献