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利用相临过去时段预报结果中同一时刻不同时效的模式预报场差异,计算预报误差协方差,并基于集合-变分混合同化系统将其与静态背景场误差协方差结合,从而在同化系统中构建了具有各向异性和一定流依赖特征的背景场误差协方差。单点观测理想试验显示本方案改善了静态模型化背景场误差协方差的各向同性和流依赖性问题。“凡亚比”台风的一系列同化及模拟试验表明,从台风路径、强度等方面本文方案的效果都要优于三维变分法。本文方案在不需要集合预报,计算量与三维变分法相当的情况下,给同化系统引入了各向异性、一定流依赖特征的背景误差协方差,因此本方案适于在计算资源较为紧缺情况下,对时效要求较高的预报业务中应用。 相似文献
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Temporal evolution of innovation and residual statistics in the ECMWF variational data assimilation systems 总被引：1，自引：0，他引：1
HEIKKI JÄRVINEN 《地球，A辑:动力气象学与海洋学》2001,53(3):333-347
The temporal evolution of innovation and residual statistics of the ECMWF 3D‐ and 4D‐Var data assimilation systems have been studied. First, the observational method is applied on an hourly basis to the innovation sequences in order to partition the perceived forecast error covariance into contributions from observation and background errors. The 4D‐Var background turns out to be ignificantly more accurate than the background in the 3D‐Var. The estimated forecast error variance associated with the 4D‐Var background trajectory increases over the assimilation window. There is also a marked broadening of the horizontal error covariance length scale over the assimilation window. Second, the standard deviation of the residuals, i.e., the fit of observations to the analysis is studied on an hourly basis over the assimilation window. This fit should, in theory, reveal the effect of model error in a strong constraint variational problem. A weakly convex curve is found for this fit implying that the perfect model assumption of 4D‐Var may be violated with as short an assimilation window as six hours. For improving the optimality of variational data assimilation systems, a sequence of retunes are needed, until the specified and diagnosed error covariances agree. 相似文献
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Based on the optimal interpolation objective analysis of the Argo data, improvements are made to the em- pirical formula of a background error covariance matrix widely used in data assimilation and objective anal- ysis systems. Specifically, an estimation of correlation scales that can improve effectively the accuracy of Ar- go objective analysis has been developed. This method can automatically adapt to the gradient change of a variable and is referred to as ＂gradient-dependent correlation scale method＂. Its effect on the Argo objective analysis is verified theoretically with Gaussian pulse and spectrum analysis. The results of one-dimensional simulation experiment show that the gradient-dependent correlation scales can improve the adaptability of the objective analysis system, making it possible for the analysis scheme to fully absorb the shortwave information of observation in areas with larger oceanographic gradients. The new scheme is applied to the Argo data obiective analysis system in the Pacific Ocean. The results are obviously improved. 相似文献
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A series of test simulations are performed to evaluate the impact of satellite-derived meteorological data on numerical typhoon track prediction. Geostationary meteorological satellite (GMS-5) and NOAA‘ s TIROS operational vertical sounder (TOVS) observations are used in the experiments. A three-dimensional variational (3D-Var) assimilation scheme is developed to assimilate the satellite data directly into the Penn State-NCAR nonhydrostatic meteorological model (MM5). Three-dimensional objective analysis fields based on the T213 results and conventional observations are employed as the background fields of the initialization. The comparisons of the simulated typhoon tracks are carried out, which correspond respectively to assimilate different kinds of satellite data. It is found that, compared with the experiment without satellite data assimilation, the 3D-Var assimilation schemes lead to significant improvements on typhoon track prediction. Track errors reduce from approximately 25% at 24 h to approximately 30% at 48 h for 3D-Var assimilation experiments. 相似文献
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The previously derived formulations for using the relative entropy and Shannon entropy difference ( SD ) to measure information content from observations are revisited in connection with another known information measure—degrees of freedom for signal, which is defined as the statistical average of the signal part of the relative entropy. For a linear assimilation system, the statistical average of the relative entropy reduces to the SD . The formulations are extended for four-dimensional variational data assimilation (4DVar). The extended formulations reveal that the information content increases (or decreases) as the model error increase (or decrease) and/or become strongly (or weakly) correlated in 4-D space. These properties are also highlighted by illustrative examples, and the extended formulations are shown to be potential useful for designing optimum phased-array radar scan configurations to maximize the extractable information contents from radar observations by a 4DVar analysis system. 相似文献
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Yosuke Fujii 《Journal of Oceanography》2005,61(1):167-181
I present the derivation of the Preconditioned Optimizing Utility for Large-dimensional analyses (POpULar), which is developed for adopting a non-diagonal background error covariance matrix in nonlinear variational analyses (i.e., analyses employing a non-quadratic cost function). POpULar is based on the idea of a linear preconditioned conjugate gradient method widely adopted in ocean data assimilation systems. POpULar uses the background error covariance matrix as a preconditioner without any decomposition of the matrix. This preconditioning accelerates the convergence. Moreover, the inverse of the matrix is not required. POpULar therefore allows us easily to handle the correlations among deviations of control variables (i.e., the variables which will be analyzed) from their background in nonlinear problems. In order to demonstrate the usefulness of POpULar, we illustrate two effects which are often neglected in studies of ocean data assimilation before. One is the effect of correlations among the deviations of control variables in an adjoint analysis. The other is the nonlinear effect of sea surface dynamic height calculation required when sea surface height observation is employed in a three-dimensional ocean analysis. As the results, these effects are not so small to neglect. 相似文献
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Surface currents measured by high frequency （HF） radar arrays are assimilated into a regional ocean model over Qingdao coastal waters based on Kalman filter method. A series of numerical experiments are per- formed to evaluate the performance of the data assimilation schemes. In order to optimize the analysis pro- cedure in the traditional ensemble Kalman filter （ENKF）, a different analysis scheme called quasiensemble Kaman filter （QENKF） is proposed. The comparisons between the ENKF and the QENKF suggest that both them can improve the simulated error and the spatial structure. The estimations of the background error covariance （BEC） are also assessed by comparing three different methods： Monte Carlo method; Canadian quick covariance （CQC） method and data uncertainty engine （DUE） method. A significant reduction of the root-mean-square （RMS） errors between model results and the observations shows that the CQC method is able to better reproduce the error statistics for this coastal ocean model and the corresponding external forcing. In addition, the sensibility of the data assimilation system to the ensemble size is also analyzed by means of different scales of the ensemble size used in the experiments. It is found that given the balance of the computational cost and the forecasting accuracy, the ensemble size of 50 will be an appropriate choice in the Qingdao coastal waters. 相似文献
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This paper compares contending advanced data assimilation algorithms using the same dynamical model and measurements. Assimilation experiments use the ensemble Kalman filter (EnKF), the ensemble Kalman smoother (EnKS) and the representer method involving a nonlinear model and synthetic measurements of a mesoscale eddy. Twin model experiments provide the “truth” and assimilated state. The difference between truth and assimilation state is a mispositioning of an eddy in the initial state affected by a temporal shift. The systems are constructed to represent the dynamics, error covariances and data density as similarly as possible, though because of the differing assumptions in the system derivations subtle differences do occur. The results reflect some of these differences in the tangent linear assumption made in the representer adjoint and the temporal covariance of the EnKF, which does not correct initial condition errors. These differences are assessed through the accuracy of each method as a function of measurement density. Results indicate that these methods are comparably accurate for sufficiently dense measurement networks; and each is able to correct the position of a purposefully misplaced mesoscale eddy. As measurement density is decreased, the EnKS and the representer method retain accuracy longer than the EnKF. While the representer method is more accurate than the sequential methods within the time period covered by the observations (particularly during the first part of the assimilation time), the representer method is less accurate during later times and during the forecast time period for sparse networks as the tangent linear assumption becomes less accurate. Furthermore, the representer method proves to be significantly more costly (2–4 times) than the EnKS and EnKF even with only a few outer iterations of the iterated indirect representer method. 相似文献
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Assimilation systems absorb both satellite measurements and Argo observations. This assimilation is essential to diagnose and evaluate the contribution from each type of data to the reconstructed analysis, allowing for better configuration of assimilation parameters. To achieve this, two comparative reconstruction schemes were designed under the optimal interpolation framework. Using a static scheme, an in situ-only field of ocean temperature was derived by correcting climatology with only Argo ... 相似文献
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文章基于天气研究和预报(weather research and forecasting, WRF)模式中的FY-3D卫星微波湿度计Ⅱ(micro-wave humidity sounder 2, MWHS-2)辐射率资料的直接同化模块, 采用三维变分(three dimensional variation, 3DVar)方法在晴空条件下同化MWHS-2辐射率资料, 考察MWHS-2辐射率资料同化对台风“米娜”(2019)预报的影响。文中设计了4组试验, 第一组试验不同化任何资料, 第二组试验同化了单独的全球通信系统(global telecommunications system, GTS)常规资料, 第三组试验联合同化了GTS常规资料和MWHS-2辐射率资料, 第四组试验将MWHS-2辐射率资料换成先进技术微波探测计(advanced technology microwave sounder, ATMS)辐射率资料同化。研究结果表明: 偏差订正后各通道观测和背景场差值的均值趋于0, 同化后分析场相对观测的标准差与均方根误差较背景场显著减小, 同化过程是有效的。与仅同化GTS常规资料和同化ATMS资料的试验相比, 同化晴空MWHS-2辐射率资料后的增量场在台风中心附近有负的高度增量和正的温度增量, 从动力与热力上有助于台风的维持。在确定性预报最后的12h, 同化晴空MWHS-2辐射率资料的试验能够改进500hPa环流形势的模拟, 加强西南方向引导气流的强度, 从而最终减小台风路径预报的误差。 相似文献
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Part 1's localization method, Ensemble COrrelations Raised to A Power (ECO-RAP), is incorporated into a Local Ensemble Transform Kalman Filter (LETKF). Because brute force incorporation would be too expensive, we demonstrate a factorization property for Part 1's Covariances Adaptively Localized with ECO-rap (CALECO) forecast error covariance matrix that, together with other simplifications, reduces the cost. The property inexpensively provides a large CALECO ensemble whose covariance is the CALECO matrix. Each member of the CALECO ensemble is an element-wise product between one raw ensemble member and one column of the square root of the ECO-RAP matrix. The LETKF is applied to the CALECO ensemble rather than the raw ensemble. The approach enables the update of large numbers of variables within each observation volume at little additional computational cost. Under plausible assumptions, this makes the CALECO and standard LETKF costs similar. The CALECO LETKF does not require artificial observation error inflation or vertically confined observation volumes both of which confound the assimilation of non-local observations such as satellite observations. Using a 27 member ensemble from a global Numerical Weather Prediction (NWP) system, we depict four-dimensional (4-D) flow-adaptive error covariance localization and test the ability of the CALECO LETKF to reduce analysis error. 相似文献
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在海雾的短时临近预报中,初始场的水汽凝结状态扮演着重要角色。为了改进初始场的云水含量,本文提出直接同化雾体云水信息的思路。针对2011年5月一次大范围的黄海海雾,借助EnKF (Ensemble Kalman Filter)方法,尝试进行了极轨卫星反演云水路径数据的同化试验。结果表明:(1)通过利用EnKF将云水混合比增加到背景场和分析场的控制变量中,构建云水观测数据与背景场之间的关系,实现云水路径数据的直接同化是可行的;(2)同化云水路径可显著改善海面气温与湿度状态,大幅提高海雾预报效果;(3)EnKF能够基于集合体动态统计流依赖的背景误差协方差是其取得良好同化效果的主要原因。值得指出的是,受集合样本误差的影响,需要特别关注云水含量与风之间的相关关系。 相似文献
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针对海表高度计资料的同化,考查了背景误差协方差矩阵的不同求逆方案对同化效果的影响。所使用的求逆方案包括避免求逆的经验正交函数方案(EOF/EOF_var)、递归滤波方案(RF/RF_var)以及采用初等变换法直接求逆的方案(Inv)。基于上述方案开展了热带太平洋地区2002年1-7月的TOPEX/Poseidon高度计资料同化试验,并利用SODA再分析资料和TAO观测资料评估了各方案对温度场的同化效果,主要得到如下结果:与SODA相比较,Inv方案对模式温度场改进甚微,其余四种方案在100~300 m深度之间对温度场改进较多,在其它深度范围内则改进较少;与TAO观测相比较,EOF_var、RF_var方案对模式温度场改进最多,EOF和RF方案次之,Inv方案则对温度场改进甚少。 相似文献
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In order to improve the ocean forecasting in the North Sea and Baltic Sea, an assimilation scheme based on a bottom-topography-dependent anisotropic recursive filter has been used in this study. This scheme can stretch or flatten the shape of a local representative contour surface of the background error covariance function into the form of an ellipse. Furthermore, the computing efficiency has been largely improved due to implicit computation of the background error covariance. A two-month experiment has been used for verifying the impact of assimilating ocean profile observations on ocean forecasting. The results indicate that the use of temperature and salinity profiles can largely improve the oceanic forecasting. The root mean square differences between the forecasts and observations for temperature and salinity have been reduced by 36% and 18% in the experiment period, respectively. Moreover, it is found that the anisotropic recursive filter approach is especially efficient in areas with complex coastlines and sharp fronts, e.g., inner Danish waters. The results also show that the propagation of observation information from an observation position to its neighboring grid points is closely related to currents. 相似文献
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With the adjoint of a data assimilation system, the impact of any or all assimilated observations on measures of forecast skill can be estimated accurately and efficiently. The approach allows aggregation of results in terms of individual data types, channels or locations, all computed simultaneously. In this study, adjoint-based estimates of observation impact are compared with results from standard observing system experiments (OSEs) using forward and adjoint versions of the NASA GEOS-5 atmospheric data assimilation system. Despite important underlying differences in the way observation impacts are measured in the two approaches, the results show that they provide consistent estimates of the overall impact of most of the major observing systems in reducing a dry total-energy metric of 24-h forecast error over the globe and extratropics and, to a lesser extent, over the tropics. Just as importantly, however, it is argued that the two approaches provide unique, but complementary, information about the impact of observations on numerical weather forecasts. Moreover, when used together, they reveal both redundancies and dependencies between observing system impacts as observations are added or removed from the data assimilation system. Understanding these dependencies appears to pose an important challenge in making optimal use of the global observing system for numerical weather prediction. 相似文献
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The ability of data assimilation systems to infer unobserved variables has brought major benefits to atmospheric and oceanographic sciences. Information is transferred from observations to unobserved variables in two ways: through the temporal evolution of the predictive equations (either a forecast model or its adjoint) or through an error covariance matrix (or a parametrized approximation to the error covariance). Here, it is found that high frequency information tends to flow through the former route, low frequency through the latter. It is also noted that using the Kalman Filter analysis to estimate the correlation between the observed and unobserved variables can lead to a biased result because of an error correlation: this error correlation is absent when the Kalman Smoother is used. 相似文献
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A data assimilation scheme used in the updated Ocean three-dimensional Variational Assimilation System (OVALS),OVALS2,is described.Based on a recursive filter (RF) to estimate the background error covariance (BEC) over a predetermined scale,this new analysis system can be implemented with anisotropic and isotropic BECs.Similarities and differences of these two BEC schemes are briefly discussed and their impacts on the model simulation are also investigated.An idealized experiment demonstrates the ability of the updated analysis system to construct different BECs.Furthermore,a set of three years experiments is implemented by assimilating expendable bathythermograph (XBT) and ARGO data into a Tropical Pacific circulation model.The TAO and WOA01 data are used to validate the assimilation results.The results show that the model simulations are substantially improved by OVALS2.The inter-comparison of isotropic and anisotropic BEC shows that the corresponding temperature and salinity produced by the anisotropic BEC are almost as good as those obtained by the isotropic one.Moreover,the result of anisotropic RF is slightly closer to WOA01 and TAO than that of isotropic RF in some special area (e.g.the cold tongue area in the Tropic Pacific). 相似文献