共查询到20条相似文献，搜索用时 31 毫秒
1.
Variational data assimilation for parameter estimation: application to a simple morphodynamic model 总被引：2，自引：1，他引：1
Polly J. Smith Sarah L. Dance Michael J. Baines Nancy K. Nichols Tania R. Scott 《Ocean Dynamics》2009,59(5):697-708
Data assimilation is a sophisticated mathematical technique for combining observational data with model predictions to produce
state and parameter estimates that most accurately approximate the current and future states of the true system. The technique
is commonly used in atmospheric and oceanic modelling, combining empirical observations with model predictions to produce
more accurate and well-calibrated forecasts. Here, we consider a novel application within a coastal environment and describe
how the method can also be used to deliver improved estimates of uncertain morphodynamic model parameters. This is achieved
using a technique known as state augmentation. Earlier applications of state augmentation have typically employed the 4D-Var,
Kalman filter or ensemble Kalman filter assimilation schemes. Our new method is based on a computationally inexpensive 3D-Var
scheme, where the specification of the error covariance matrices is crucial for success. A simple 1D model of bed-form propagation
is used to demonstrate the method. The scheme is capable of recovering near-perfect parameter values and, therefore, improves
the capability of our model to predict future bathymetry. Such positive results suggest the potential for application to more
complex morphodynamic models. 相似文献
2.
This paper presents a comparison of two reduced-order, sequential, and variational data assimilation methods: the singular
evolutive extended Kalman filter (SEEK) and the reduced 4D-Var (R-4D-Var). A hybridization of the two, combining the variational
framework and the sequential evolution of covariance matrices, is also preliminarily investigated and assessed in the same
experimental conditions. The comparison is performed using the twin-experiment approach on a model of the tropical Pacific
domain. The assimilated data are simulated temperature profiles at the locations of the TAO/TRITON array moorings. It is shown
that, in a quasilinear regime, both methods produce similarly good results. However, the hybrid approach provides slightly
better results and thus appears as potentially fruitful. In a more nonlinear regime, when tropical instability waves develop,
the global nature of the variational approach helps control model dynamics better than the sequential approach of the SEEK
filter. This aspect is probably enhanced by the context of the experiments in that there is a limited amount of assimilated
data and no model error. 相似文献
3.
HUANG Sixun HAN Wei & WU Rongsheng . P. O. Box Nanjing China . Key Laboratory of Meso-Scale Severe Weather /MOE Department of Atmospheric Sciences Nanjing University Nanjing China Correspondence should be addressed to Huang Sixun 《中国科学D辑(英文版)》2004,47(7):630-638
There is an international focus on the develop-ments of data assimilation systems for meteorology and physical oceanography models. Data assimilation and inverse methods are normally used for optimal control of poorly known initial boundary conditions and model parameters by taking into account both the information about dynamics of a model and the infor-mation about the true state which is constrained by a set of measurements. The research methodology of parameter estimation in meteorology ca… 相似文献
4.
《Journal of Atmospheric and Solar》2000,62(12):1057-1070
Data assimilation combines atmospheric measurements with knowledge of atmospheric behavior as codified in computer models, thus producing a “best” estimate of current conditions that is consistent with both information sources. The four major challenges in data assimilation are: (1) to generate an initial state for a computer forecast that has the same mass-wind balance as the assimilating model, (2) to deal with the common problem of highly non-uniform distribution of observations, (3) to exploit the value of proxy observations (of parameters that are not carried explicitly in the model), and (4) to determine the statistical error properties of observing systems and numerical model alike so as to give each information source the proper weight. Variational data assimilation is practiced at major meteorological centers around the world. It is based upon multivariate linear regression, dating back to Gauss, and variational calculus. At the heart of the method is the minimization of a cost function, which guarantees that the analyzed fields will closely resemble both the background field (a short forecast containing a priori information about the atmospheric state) and current observations. The size of the errors in the background and the observations (the latter, arising from measurement and non-representativeness) determine how close the analysis is to each basic source of information. Three-dimensional variational (3DVAR) assimilation provides a logical framework for incorporating the error information (in the form of variances and spatial covariances) and deals directly with the problem of proxy observations. 4DVAR assimilation is an extension of 3DVAR assimilation that includes the time dimension; it attempts to find an evolution of model states that most closely matches observations taken over a time interval measured in hours. Both 3DVAR and, especially, 4DVAR assimilation require very large computing resources. Researchers are trying to find more efficient numerical solutions to these problems. Variational assimilation is applicable in the upper atmosphere, but practical implementation demands accurate modeling of the physical processes that occur at high altitudes and multiple sources of observations. 相似文献
5.
Inversion for seismic impedance is an inherently complicated problem. It is ill‐posed and band‐limited. Thus the inversion results are non‐unique and the process is unstable. Combining regularization with constraints using sonic and density log data can help to reduce these problems. To achieve this, we developed an inversion method by constructing a new objective function, including edge‐preserving regularization and a soft constraint based on a Markov random field. The method includes the selection of proper initial values of the regularization parameters by a statistical method, and it adaptively adjusts the regularization parameters by the maximum likelihood method in a fast simulated‐annealing procedure to improve the inversion result and the convergence speed. Moreover, the method uses two kinds of regularization parameter: a ‘weighting factor’λ and a ‘scaling parameter’δ. We tested the method on both synthetic and field data examples. Tests on 2D synthetic data indicate that the inversion results, especially the aspects of the discontinuity, are significantly different for different regularization functions. The initial values of the regularization parameters are either too large or too small to avoid either an unstable or an over‐smoothed result, and they affect the convergence speed. When selecting the initial values of λ, the type of the regularization function should be considered. The results obtained by constant regularization parameters are smoother than those obtained by adaptively adjusting the regularization parameters. The inversion results of the field data provide more detailed information about the layers, and they match the impedance curves calculated from the well logs at the three wells, over most portions of the curves. 相似文献
6.
Conventional time-space domain and frequency-space domain prediction filtering methods assume that seismic data consists of two parts, signal and random noise. That is, the so-called additive noise model. However, when estimating random noise, it is assumed that random noise can be predicted from the seismic data by convolving with a prediction error filter. That is, the source-noise model. Model inconsistencies, before and after denoising, compromise the noise attenuation and signal-preservation performances of prediction filtering methods. Therefore, this study presents an inversion-based time-space domain random noise attenuation method to overcome the model inconsistencies. In this method, a prediction error filter (PEF), is first estimated from seismic data; the filter characterizes the predictability of the seismic data and adaptively describes the seismic data’s space structure. After calculating PEF, it can be applied as a regularized constraint in the inversion process for seismic signal from noisy data. Unlike conventional random noise attenuation methods, the proposed method solves a seismic data inversion problem using regularization constraint; this overcomes the model inconsistency of the prediction filtering method. The proposed method was tested on both synthetic and real seismic data, and results from the prediction filtering method and the proposed method are compared. The testing demonstrated that the proposed method suppresses noise effectively and provides better signal-preservation performance. 相似文献
7.
Nedjeljka ?agar 《Pure and Applied Geophysics》2012,169(3):367-379
This paper reports recent advances in understanding of dynamical aspects of the tropical data assimilation. In contrast with
the mid-latitudes, there is no a well-defined approach for the tropical data assimilation in numerical weather prediction
(NWP) community which has traditionally been concentrated on the mid-latitude analysis problem. In particular, the impact
of the equatorial Rossby, inertio-gravity, and mixed Rossby-gravity waves on the tropical forecast-error covariances is difficult
to quantify. Various tropical waves are characterized by different couplings between the mass field and the wind field. The
average mixture of these waves, built into the background-error covariance matrix for data assimilation provides analysis
increments which appear nearly univariate even though they result from the advanced multivariate assimilation methodology.
This applies to both dry and moist idealized tropical systems as well as to a 4D-Var NWP assimilation system. 相似文献
8.
地球物理反演是获取地球信息的重要手段,其求解具有严重的不适定性.为获得稳定的反问题结果,通常需要在目标泛函中加入正则化约束项.正确地估计正则化参数一直是地球物理反问题中的难点.目前存在的选取方法需要根据大量的试验来确定正则化参数,工作量十分巨大,并且存在很大的经验性,很难得到最优的正则化参数.针对这个问题,本文提出了一种基于广义Stein无偏风险估计的正则化参数求取方法.该方法的具体思路是通过求解模型参数均方误差的广义Stein无偏风险估计函数,在反问题求解过程中自动求取正则化参数.本文模型测试结果表明,相比于目前常用的方法,通过该方法得到的正则化参数是最优的. 相似文献
9.
A new data assimilation method called the explicit four-dimensional variational (4DVAR) method is proposed. In this method, the singular value decomposition (SVD) is used to construct the orthogonal basis vectors from a forecast ensemble in a 4D space. The basis vectors represent not only the spatial structure of the analysis variables but also the temporal evolution. After the analysis variables are ex-pressed by a truncated expansion of the basis vectors in the 4D space, the control variables in the cost function appear explicitly, so that the adjoint model, which is used to derive the gradient of cost func-tion with respect to the control variables, is no longer needed. The new technique significantly simpli-fies the data assimilation process. The advantage of the proposed method is demonstrated by several experiments using a shallow water numerical model and the results are compared with those of the conventional 4DVAR. It is shown that when the observation points are very dense, the conventional 4DVAR is better than the proposed method. However, when the observation points are sparse, the proposed method performs better. The sensitivity of the proposed method with respect to errors in the observations and the numerical model is lower than that of the conventional method. 相似文献
10.
We explore the ocean circulation estimates obtained by assimilating observational products made available by the Global Ocean
Data Assimilation Experiment (GODAE) and other sources in an incremental, four-dimensional variational data assimilation system
for the Intra-Americas Sea. Estimates of the analysis error (formally, the inverse Hessian matrix) are computed during the
assimilation procedure. Comparing the impact of differing sea surface height and sea surface temperature products on both
the final analysis error and difference between the model state estimates, we find that assimilating GODAE and non-GODAE products
yields differences between the model and observations that are comparable to the differences between the observation products
themselves. While the resulting analysis error estimates depend on the configuration of the assimilation system, the basic
spatial structures of the standard deviations of the ocean circulation estimates are fairly robust and reveal that the assimilation
procedure is capable of reducing the circulation uncertainty when only surface data are assimilated. 相似文献
11.
本文基于YH4DVAR业务系统构建了集合资料同化试验平台,利用10个集合样本统计得到的流依赖背景误差能显著改进业务应用中背景误差方差的结构和大小.但是受样本数的限制,背景误差方差的集合估计值中引入了大量的随机取样噪声.为了降低噪声对估计值的影响,本文采用谱滤波方法,根据信号和噪声尺度的统计特征构造一个低通滤波器来滤除背景误差方差估计值中的大部分随机取样噪声.在2013年第九号台风"飞燕"的集合方差滤波试验中,10个样本的滤波结果优于30个样本的集合估计值.谱滤波方法的成功应用有效降低了集合资料同化系统对集合样本数的要求,将是集合资料同化系统未来业务化运行的一项不可或缺的关键技术. 相似文献
12.
Variational data assimilation methods optimize the match between an observed and a predicted field. These methods normally
require information on error variances of both the analysis and the observations, which are sometimes difficult to obtain
for transport and dispersion problems. Here, the variational problem is set up as a minimization problem that directly minimizes
the root mean squared error of the difference between the observations and the prediction. In the context of atmospheric transport
and dispersion, the solution of this optimization problem requires a robust technique. A genetic algorithm (GA) is used here
for that solution, forming the GA-Variational (GA-Var) technique. The philosophy and formulation of the technique is described
here. An advantage of the technique includes that it does not require observation or analysis error covariances nor information
about any variables that are not directly assimilated. It can be employed in the context of either a forward assimilation
problem or used to retrieve unknown source or meteorological information by solving the inverse problem. The details of the
method are reviewed. As an example application, GA-Var is demonstrated for predicting the plume from a volcanic eruption.
First the technique is employed to retrieve the unknown emission rate and the steering winds of the volcanic plume. Then that
information is assimilated into a forward prediction of its transport and dispersion. Concentration data are derived from
satellite data to determine the observed ash concentrations. A case study is made of the March 2009 eruption of Mount Redoubt
in Alaska. The GA-Var technique is able to determine a wind speed and direction that matches the observations well and a reasonable
emission rate. 相似文献
13.
The increasing availability of precipitation observations from space, e.g., from the Tropical Rainfall Measuring Mission (TRMM) and the forthcoming Global Precipitation Measuring (GPM) Mission, has fueled renewed interest in developing frameworks for downscaling and multi-sensor data fusion that can handle large data sets in computationally efficient ways while optimally reproducing desired properties of the underlying rainfall fields. Of special interest is the reproduction of extreme precipitation intensities and gradients, as these are directly relevant to hazard prediction. In this paper, we present a new formalism for downscaling satellite precipitation observations, which explicitly allows for the preservation of some key geometrical and statistical properties of spatial precipitation. These include sharp intensity gradients (due to high-intensity regions embedded within lower-intensity areas), coherent spatial structures (due to regions of slowly varying rainfall), and thicker-than-Gaussian tails of precipitation gradients and intensities. Specifically, we pose the downscaling problem as a discrete inverse problem and solve it via a regularized variational approach (variational downscaling) where the regularization term is selected to impose the desired smoothness in the solution while allowing for some steep gradients (called ?1-norm or total variation regularization). We demonstrate the duality between this geometrically inspired solution and its Bayesian statistical interpretation, which is equivalent to assuming a Laplace prior distribution for the precipitation intensities in the derivative (wavelet) space. When the observation operator is not known, we discuss the effect of its misspecification and explore a previously proposed dictionary-based sparse inverse downscaling methodology to indirectly learn the observation operator from a data base of coincidental high- and low-resolution observations. The proposed method and ideas are illustrated in case studies featuring the downscaling of a hurricane precipitation field. 相似文献
14.
本文在无线电掩星弯曲角射线追踪正演算子中引入水成物的影响,针对台风个例,利用FY-3c GNOS弯曲角资料的同化展开研究.通过分析水成物对掩星弯曲角正演精度的影响,指出当掩星剖面跨越一定厚度的台风区云雨大气时,多相态水成物对GNOS弯曲角正演精度的影响不可忽略.进而提出一种考虑云雨影响的掩星折射率正演算法,将掩星折射率的正演分别在晴空区和云雨区进行,在云雨区正演算子中增加多相态水成物含量对正演掩星折射率的贡献,改进了FY-3c GNOS弯曲角资料在云雨大气环境的同化方案.针对2018年24号台风个例,进行了同化的参照试验、未考虑和考虑水成物影响时GNOS弯曲角的3DVAR同化试验,考量云雨环境下的GNOS弯曲角资料同化对台风模拟的影响差异.试验结果表明,两种同化方案皆能改善台风路径预报,台风中心海平面气压模拟都能接近实际观测,台风最大风速也不同程度增大.而考虑水成物含量的影响后,资料同化能更有效缩小观测空间与背景场空间之间的偏差,同化后观测与分析的偏差更接近高斯分布,台风外围动力场和热力场环境能够得到更优的调整,使得96 h的台风路径模拟平均距离误差较不考虑水成物影响的情形减小了约14%. 相似文献
15.
Sujay V. Kumar Rolf H. Reichle Christa D. Peters-Lidard Randal D. Koster Xiwu Zhan Wade T. Crow John B. Eylander Paul R. Houser 《Advances in water resources》2008
The Land Information System (LIS) is an established land surface modeling framework that integrates various community land surface models, ground measurements, satellite-based observations, high performance computing and data management tools. The use of advanced software engineering principles in LIS allows interoperability of individual system components and thus enables assessment and prediction of hydrologic conditions at various spatial and temporal scales. In this work, we describe a sequential data assimilation extension of LIS that incorporates multiple observational sources, land surface models and assimilation algorithms. These capabilities are demonstrated here in a suite of experiments that use the ensemble Kalman filter (EnKF) and assimilation through direct insertion. In a soil moisture experiment, we discuss the impact of differences in modeling approaches on assimilation performance. Provided careful choice of model error parameters, we find that two entirely different hydrological modeling approaches offer comparable assimilation results. In a snow assimilation experiment, we investigate the relative merits of assimilating different types of observations (snow cover area and snow water equivalent). The experiments show that data assimilation enhancements in LIS are uniquely suited to compare the assimilation of various data types into different land surface models within a single framework. The high performance infrastructure provides adequate support for efficient data assimilation integrations of high computational granularity. 相似文献
16.
17.
The variational method of data assimilation is used to solve an inverse problem in the transport of sediment in river, which
plays an important role in the change of natural environment. The cost function is defined to measure the error between model
predictions and field observations. The adjoint model of IAP river sedimentation model is created to obtain the gradient of
the cost function with respect to control variables. The initial conditions are taken as the control variables; their optimal
values can be retrieved by minimizing the cost function with limited memory quasi-Newton method (LMQN). The results show that
the adjoint method approach can successfully make the model prediction well fit the simulated observations. And it is expected
to use this method to solve other inverse problems of river sedimentation. But some numerical problems need to be discussed
before applying to real river data.
Project partially supported by the State Key Laboratory of Numerical Modelling for Atmospheric Sciences and Geophysical Fluid
Dynamics, Institute of Atmospheric Physics, Chinese Academy of Sciences 相似文献
18.
Adjustment of regularization in ill-posed linear inverse problems by the empirical Bayes approach 总被引：1，自引：0，他引：1
Yuji Mitsuhata 《Geophysical Prospecting》2004,52(3):213-239
Regularization is the most popular technique to overcome the null space of model parameters in geophysical inverse problems, and is implemented by including a constraint term as well as the data‐misfit term in the objective function being minimized. The weighting of the constraint term relative to the data‐fitting term is controlled by a regularization parameter, and its adjustment to obtain the best model has received much attention. The empirical Bayes approach discussed in this paper determines the optimum value of the regularization parameter from a given data set. The regularization term can be regarded as representing a priori information about the model parameters. The empirical Bayes approach and its more practical variant, Akaike's Bayesian Information Criterion, adjust the regularization parameter automatically in response to the level of data noise and to the suitability of the assumed a priori model information for the given data. When the noise level is high, the regularization parameter is made large, which means that the a priori information is emphasized. If the assumed a priori information is not suitable for the given data, the regularization parameter is made small. Both these behaviours are desirable characteristics for the regularized solutions of practical inverse problems. Four simple examples are presented to illustrate these characteristics for an underdetermined problem, a problem adopting an improper prior constraint and a problem having an unknown data variance, all frequently encountered geophysical inverse problems. Numerical experiments using Akaike's Bayesian Information Criterion for synthetic data provide results consistent with these characteristics. In addition, concerning the selection of an appropriate type of a priori model information, a comparison between four types of difference‐operator model – the zeroth‐, first‐, second‐ and third‐order difference‐operator models – suggests that the automatic determination of the optimum regularization parameter becomes more difficult with increasing order of the difference operators. Accordingly, taking the effect of data noise into account, it is better to employ the lower‐order difference‐operator models for inversions of noisy data. 相似文献
19.
Li Xin Ma Hanqing Ran Youhua Wang Xufeng Zhu Gaofeng Liu Feng He Honglin Zhang Zhen Huang Chunlin 《中国科学:地球科学(英文版)》2021,64(10):1645-1657
The terrestrial carbon cycle is an important component of global biogeochemical cycling and is closely related to human well-being and sustainable development. However, large uncertainties exist in carbon cycle simulations and observations.Model-data fusion is a powerful technique that combines models and observational data to minimize the uncertainties in terrestrial carbon cycle estimation. In this paper, we comprehensively overview the sources and characteristics of the uncertainties in terrestrial carbon cycle models and observations. We present the mathematical principles of two model-data fusion methods, i.e., data assimilation and parameter estimation, both of which essentially achieve the optimal fusion of a model with observational data while considering the respective errors in the model and in the observations. Based upon reviewing the progress in carbon cycle models and observation techniques in recent years, we have highlighted the major challenges in terrestrial carbon cycle model-data fusion research, such as the "equifinality" of models, the identifiability of model parameters,the estimation of representativeness errors in surface fluxes and remote sensing observations, the potential role of the posterior probability distribution of parameters obtained from sensitivity analysis in determining the error covariance matrixes of the models, and opportunities that emerge by assimilating new remote sensing observations, such as solar-induced chlorophyll fluorescence. It is also noted that the synthesis of multisource observations into a coherent carbon data assimilation system is by no means an easy task, yet a breakthrough in this bottleneck is a prerequisite for the development of a new generation of global carbon data assimilation systems. This article also highlights the importance of carbon cycle data assimilation systems to generate reliable and physically consistent terrestrial carbon cycle reanalysis data products with high spatial resolution and longterm time series. These products are critical to the accurate estimation of carbon cycles at the global and regional scales and will help future carbon management strategies meet the goals of carbon neutrality. 相似文献
20.
Estimating erroneous parameters in ensemble based snow data assimilation system has been given little attention in the literature. Little is known about the related methods’ effectiveness, performance, and sensitivity to other error sources such as model structural error. This research tackles these questions by running synthetic one-dimensional snow data assimilation with the ensemble Kalman filter (EnKF), in which both state and parameter are simultaneously updated. The first part of the paper investigates the effectiveness of this parameter estimation approach in a perfect-model-structure scenario, and the second part focuses on its dependence on model structure error. The results from first part research demonstrate the advantages of this parameter estimation approach in reducing the systematic error of snow water equivalent (SWE) estimates, and retrieving the correct parameter value. The second part results indicate that, at least in our experiment, there is an evident dependence of parameter search convergence on model structural error. In the imperfect-model-structure run, the parameter search diverges, although it can simulate the state variable well. This result suggest that, good data assimilation performance in estimating state variables is not a sufficient indicator of reliable parameter retrieval in the presence of model structural error. The generality of this conclusion needs to be tested by data assimilation experiments with more complex structural error configurations. 相似文献