共查询到20条相似文献，搜索用时 31 毫秒
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Assimilating AMSU-a radiance data with the WRF hybrid En3DVAR system for track predictions of Typhoon Megi (2010) 总被引：1，自引：0，他引：1
The impact of assimilating radiances from the Advanced Microwave Sounding Unit-A (AMSU-A) on the track prediction of Typhoon Megi (2010) was studied using the Weather Research and Forecasting (WRF) model and a hybrid ensemble three-dimensional variational (En3DVAR) data assimilation (DA) system. The influences of tuning the length scale and variance scale factors related to the static background error covariance (BEC) on the track forecast of the typhoon were studied. The results show that, in typhoon radiance data assimilation, a moderate length scale factor improves the prediction of the typhoon track. The assimilation of AMSU-A radiances using 3DVAR had a slight positive impact on track forecasts, even when the static BEC was carefully tuned to optimize its performance. When the hybrid DA was employed, the track forecast was significantly improved, especially for the sharp northward turn after crossing the Philippines, with the flow-dependent ensemble covariance. The flow-dependent BEC can be estimated by the hybrid DA and was capable of adjusting the position of the typhoon systematically. The impacts of the typhoon-specific BEC derived from ensemble forecasts were revealed by comparing the analysis increments and forecasts generated by the hybrid DA and 3DVAR. Additionally, for 24 h forecasts, the hybrid DA experiment with use of the full flow-dependent background error substantially outperformed 3DVAR in terms of the horizontal winds and temperature in the lower and mid-troposphere and for moisture at all levels. 相似文献
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全球资料同化中误差协方差三维结构的准确估计与应用Ⅱ:背景误差协方差调整与数值试验分析 总被引：6，自引：1，他引：6
利用NMC方法针对背景误差协方差的方差、三维相关与特征长度来揭示T213L31模式的误差主要特点,并与传统更新矢量方法的计算结果进行了对比与调整。结果表明NMC方法结果与更新矢量方法结果在大体特征上基本吻合,但细节上的差异不可忽视,特别是对背景误差方差与特征长度的估计存在显著的差异,其主要原因是NMC方法倾向于高估天气尺度波的背景误差,而低估次天气尺度到中尺度波的背景误差。通过对背景误差方差、特征长度的调整,显著改善了背景误差功率谱的分布特点,使得NMC方法结果与更新矢量方法结果更为吻合。通过三维变分同化与最优插值中观测与背景误差相对重要性的比较,发现两者结果基本一致,但三维变分同化在850 hPa以下的温度场和300 hPa以上的风场统计结果都表现出背景误差相对于观测误差偏小的特点。背景误差相对于观测误差偏小有助于保证分析场中质量场与风场平衡,消除了大气底层和高层质量场与风场不匹配现象。在数值试验中,针对不同的背景误差均方差与特征长度的特点,分析了分析增量和预报效果的差异,结果表明,准确的背景误差估计与优化工作改善了预报效果,使得北半球三维变分同化的120 h预报效果整体好于现有最优插值。 相似文献
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在四维变分同化中运用集合协方差的试验 总被引：1，自引：1，他引：1
利用浅水方程模式和模式模拟资料进行数值试验比较3种不同的背景误差协方差矩阵处理方法对四维变分(4DVAR)资料同化的影响.3种背景误差协方差矩阵分别是:(1)对单一变量将背景误差协方差矩阵简化为对角矩阵;(2)将背景误差协方差矩阵的作用简化为高斯过滤;(3)由预报集合生成背景误差协方差矩阵并利用奇异值分解技术解决矩阵的求逆.通过一系列数值试验,比较不同观测密度、不同观测误差下3种背景误差协方差处理方法对4DVAR同化效果的影响.结果表明,背景误差协方差的结构对4DVAR有重大影响.当观测资料的空间密度不够高时,采用对角矩阵得不到满意的结果.高斯过滤方案可以明显改善同化结果,但是对背景误差特征长度比较敏感.第3种方法采用的背景误差协方差矩阵是流型依赖的,而且并不以显式的方式出现在目标函数中.避免了对它求逆的复杂运算.由于做了降维处理,在观测点的密度较低和观测误差较大时可望取得较好的同化结果,同化效果较为稳定. 相似文献
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目前多数快速更新循环同化系统在各分析时刻常使用固定的背景场误差协方差。为在快速更新循环同化系统中采用日变化的背景场误差协方差,基于RMAPS-ST系统分析了其夏季和冬季日变化背景场误差协方差特征,并进行了同化及预报对比试验。结果表明,该系统夏、冬两季的背景场误差协方差均呈现出明显的日变化特征,且夜间各变量(U、V、T、RH)的误差标准差与特征值均大于日间,反映模式系统夜间的预报误差大于日间;而夏季各变量误差标准差和特征值大于冬季,也说明系统在夏季的模式预报误差比冬季大;连续3 d的循环同化试验初步表明,采用日变化背景场误差协方差可以提高同化及预报效果。 相似文献
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对于中尺度数值天气预报来说,初始条件的准确与否已成为影响预报技巧的主要因素之一。现有的大气观测资料在时空分布上的不均匀,以及存在的观测误差,使得我们必须引进资料同化方法,为中尺度数值模式提供最优的初始场。由于传统的三维变分同化(3DVar)方法缺乏模式约束以及背景误差协方差矩阵(B矩阵)不具有流依赖性,因此本文提出一种基于历史样本投影的3DVar(HSP-3DVar)方法,它不仅具有流依赖的B矩阵,而且比传统的3DVar简单易行。为了评价HSP-3DVar的同化性能,我们基于区域暴雨预报模式AREM(Advanced Regional Eta Model)对其进行了观测系统模拟试验(OSSE),结果表明:HSP-3DVar能够有效融合观测信息,模式初值在各层的均方根误差都显著地降低。 相似文献
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Satellite data obtained over synoptic data-sparse regions such as an ocean contribute toward improving the quality of the initial state of limited-area models. Background error covariances are crucial to the proper distribution of satellite-observed information in variational data assimilation. In the NMC (National Meteorological Center) method, background error covariances are underestimated over data-sparse regions such as an ocean because of small differences between different forecast times. Thus, it is necessary to reconstruct and tune the background error covariances so as to maximize the usefulness of the satellite data for the initial state of limited-area models, especially over an ocean where there is a lack of conventional data. In this study, we attempted to estimate background error covariances so as to provide adequate error statistics for data-sparse regions by using ensemble forecasts of optimal perturbations using bred vectors. The background error covariances estimated by the ensemble method reduced the overestimation of error amplitude obtained by the NMC method. By employing an appropriate horizontal length scale to exclude spurious correlations, the ensemble method produced better results than the NMC method in the assimilation of retrieved satellite data. Because the ensemble method distributes observed information over a limited local area, it would be more useful in the analysis of high-resolution satellite data. Accordingly, the performance of forecast models can be improved over the area where the satellite data are assimilated. 相似文献
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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
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Ting Wang Jie Xiang Jianfang Fei Yi Wang Chunxia Liu Yuanxiang Li 《Acta Meteorologica Sinica》2017,31(6):1123-1132
This paper presents an evaluation of the observational impacts on blended sea surface winds from a two-dimensional variational data assimilation (2D-Var) scheme. We begin by briefly introducing the analysis sensitivity with respect to observations in variational data assimilation systems and its relationship with the degrees of freedom for signal (DFS), and then the DFS concept is applied to the 2D-Var sea surface wind blending scheme. Two methods, a priori and a posteriori, are used to estimate the DFS of the zonal (u) and meridional (v) components of winds in the 2D-Var blending scheme. The a posteriori method can obtain almost the same results as the a priori method. Because only by-products of the blending scheme are used for the a posteriori method, the computation time is reduced significantly. The magnitude of the DFS is critically related to the observational and background error statistics. Changing the observational and background error variances can affect the DFS value. Because the observation error variances are assumed to be uniform, the observational influence at each observational location is related to the background error variance, and the observations located at the place where there are larger background error variances have larger influences. The average observational influence of u and v with respect to the analysis is about 40%, implying that the background influence with respect to the analysis is about 60%. 相似文献
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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
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基于资料同化集合设计了流依赖球面小波背景场误差协方差模型中背景误差方差和局地垂直相关协方差的统计计算方法。为了提高背景误差方差的估计精度,采用客观滤波技术来减少因集合样本个数不足而引入的随机取样噪声。最后在银河四维变分同化业务系统(YH4DVar)上设计了集合资料同化的试验系统,以流依赖背景误差方差为重点验证了模型的有效性。结果表明:基于流依赖球面小波背景误差协方差模型能够有效估计出随天气状态变化的背景场误差方差,对台风等剧烈变化的天气过程的同化分析和预报都具有一定的正效果。 相似文献
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The use of global Atmospheric Motion Vectors (AMV) satellite observations in the meteorological data assimilation system based on Local Ensemble Transform Kalman Filter (LETKF) algorithm is considered. The height assignment is the most crucial error source for AMV observations. To reduce its impact, the AMV height reassignment method is implemented; it is based on the consistency coefficient bet ween the observed and the background winds. The other way to improve the analysis quality is a more accurate specification of AMV observation errors. This necessitates the use of the nondiagonal observation-error covariance matrix R in the data assimilation scheme. The first results of these studies are presented. It is demonstrated that the use of AMV observations in the data assimilation system reduces the errors of forecasts computed from the initial data of this system. 相似文献
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随着气旋内部资料(Inner core data)在热带气旋预报中的使用,其重要性逐渐受到人们越来越多的关注。为了研究该资料中尾部机载雷达(Tail Doppler Radar,TDR)资料在业务系统中的应用效果,本文利用2012年飓风等级热带气旋Isaac期间的TDR资料,采用业务HWRF(Weather Research and Forecasting model for Hurricane)数值模式与业务GSI(Grid-point Statistical Interpolation system)三维变分同化(Three-Dimensional Variational Data Assimilation, 3DVar)系统对TDR资料进行了同化,展开了一系列预报试验,并对其效果进行了分析和研究。结果表明与HWRF的业务预报相比,GSI系统同化TDR资料后对热带气旋的路径和强度预报有明显改进;但其同化效果同时也表明业务三维变分中的静态背景误差协方差在TDR资料的应用中仍需要进一步的改进。 相似文献
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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
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资料同化方法研究进展 总被引：3，自引：0，他引：3
数值天气预报模式的不断完善和大气观测探测资料（特别是卫星、雷达等非常规探测资料）的大量涌现,推动着资料同化方法的逐步发展.文章主要回顾了资料同化方法研究的发展过程、目前的应用现状以及对未来同化方法的展望.随着人们对资料同化含义的深入理解,对于资料同化的研究由初始的探索阶段逐步发展到以经验性为主的同化方法,主要包括SCM和nudging;统计方法的引入,成为资料同化方法研究发展道路上具有重要意义的一个里程碑,从而出现了多元统计插值同化,比如OI,3D-Var以及PSAS;针对背景误差协方差固定不变与实际情况的差异,考虑时间维的四维资料同化方法成为目前国际上较为主流的同化研究方法,其中以4D-Var和Kalman滤波为代表;随着计算机技术的进步,更加合理的四维资料同化方法将会成为未来业务预报中主要的资料同化方法. 相似文献
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R. B. Zaripov Yu. V. Martynova V. N. Krupchatnikov A. P. Petrov 《Russian Meteorology and Hydrology》2016,41(11-12):808-815
The system of the cyclic assimilation of data on atmospheric conditions used in the West Siberian Administration for Hydrometeorology and Environmental Monitoring is described. It is based on the WRF-ARW mesoscale atmospheric model and on the WRF 3D-Var system of the three-dimensional variational analysis of data. The system is verified when the first approximation data (6-hour forecast) and WRF-ARW forecasts with the lead time up to 24 hours are compared with the observational data. The problems of assimilation of observations from the AMSU-A and AIRS satellite instruments are considered. The effect of using AMSU-A and AIRS for the analysis in the Novosibirsk region is estimated. The experiments demonstrated that the cyclic data assimilation system operates successfully. The AMSU-A observations improve the quality of analyses and forecasts in winter. In summer the impact of satellite observations on the forecast skill scores is ambiguous. Good short-term forecasts are provided by the initial conditions obtained using the system of detailing of the NCEP large-scale analysis. 相似文献
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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
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Kefeng ZHU Ming XUE Yujie PAN Ming HU Stanley G. BENJAMIN Stephen S. WEYGANDT Haidao LIN 《大气科学进展》2019,36(12):1308-1326
A regional ensemble Kalman filter (EnKF) data assimilation (DA) and forecast system was recently established based on the Gridpoint Statistical Interpolation (GSI) analysis system. The EnKF DA system was tested with continuous threehourly updated cycles followed by 18-h deterministic forecasts from every three-hourly ensemble mean analysis. Initial tests showed negative to neutral impacts of assimilating satellite radiance data due to the improper bias correction procedure. In this study, two bias correction schemes within the established EnKF DA system are investigated and the impact of assimilating additional polar-orbiting satellite radiance is also investigated. Two group experiments are conducted. The purpose of the first group is to evaluate the bias correction procedure. Two online bias correction methods based on GSI 3DVar and EnKF algorithms are used to assimilate AMSU-A radiance data. Results show that both variational and EnKF-based bias correction procedures effectively reduce the observation and background radiance differences, achieving positive impacts on forecasts. With proper bias correction, we assimilate full radiance observations including AMSU-A, AMSU-B, AIRS, HIRS3/4, and MHS in the second group. The relative percentage improvements(RPIs) for all forecast variables compared to those without radiance data assimilation are mostly positive, with the RPI of upper-air relative humidity being the largest. Additionally, precipitation forecasts on a downscaled 13-km grid from 40-km EnKF analyses are also improved by radiance assimilation for almost all forecast hours. 相似文献
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为有效引入“流依赖”的背景场误差协方差,同时降低集合预报带来的计算量,尝试通过优选与同化时刻天气形势更相似的历史预报样本,并结合预报过程中的时间滞后样本,将两种样本引入集合-变分混合同化系统中,构建基于优选历史预报样本和时间滞后样本的集合-变分混合同化方案。单点观测理想试验表明,优选历史预报样本结合时间滞后样本,既能够缓解样本不足所导致的采样误差,又能够为同化系统提供“流依赖”的背景场误差协方差。连续一周的循环同化及预报试验结果显示,相较于ERA5资料和探空资料,三维变分方案整体表现稍差,样本组合混合同化方案分析场和预报场的均方根误差最小,且比仅用时间滞后样本的混合同化方案有所改进;降水评分整体也表现最优,尤其对中雨和暴雨的模拟改进较明显,较好地模拟出了强降水中心的强度和位置,且改善了降水过报的问题。 相似文献