共查询到20条相似文献，搜索用时 763 毫秒
1.
《中国科学:地球科学》2016,(1)
局地强降水可以引发山洪、泥石流等次生灾害,目前准确预报局地强降水依然是天气预报业务的难点.本文针对一次发生在西北太平洋副热带高压边缘、导致12人死亡的极端局地强降水事件,利用集合卡尔曼滤波(En KF)开展多普勒雷达径向风观测资料同化试验,并对En KF同化过程不确定性进行分析.结果表明:不同化观测资料,采用单一初值的确定性预报或增加初值扰动、采用多物理过程的集合预报均不能正确预报强降水发生位置,而利用En KF同化雷达径向速度观测资料能有效改进确定性和集合预报效果,特别是强降水位置预报.通过En KF同化雷达资料,建立深厚的中尺度对流系统是改进降水预报效果的直接原因.在具备了对流发生条件的大尺度环境背景场中,上游地区、对流层中下层经向风和水汽场的合理扰动是影响同化过程和降水预报的关键因素.该个例预报过程受实际可预报性影响,具有不确定性,大尺度初始条件的差异或初始扰动场振幅偏小导致的En KF分析场差异都会对模拟结果造成较大影响,而采用En KF循环同化有助于提高该个例的预报准确性.敏感性试验还表明未来通过改进数值模式或改善观测系统,提供更准确观测信息,可以对此类短时强降水事件做出更准确预报. 相似文献
2.
同化站点观测和SSM／I亮温改善冻土活动层状态变量的模拟精度 总被引：2，自引：0，他引：2
以考虑了土壤冻融过程的一维水-热-盐分耦合模型SHAW为冻土活动层数据同化系统的动力学约束框架,通过集合卡尔曼滤波算法同化土壤水分和温度的站点观测数据以及被动微波辐射计SSM／I19GHz亮温观测数据,以改善冻土活动层水热状态变量的估计精度,实现模型模拟和观测信息的融合。冬季活动层冻结,同化的关键变量为土壤温度;而夏季同化的关键变量为土壤水分。通过单点同化试验表明,该同化系统能显著改善土壤表层水分和温度的估计精度;同时,在同化过程中给定合理的模型误差协方差项,可将表层优化后的信息迅速传递给深层土壤,达到改善整个土壤廓线状态变量估计的目的。同化结果表明,相对于SHAW模拟结果,同化4cm土壤温度观测后,各层土壤温度RMSE平均减小0.96℃,而同化4cm土壤水分观测数据后,各层土壤水分RMSE平均减小0.020m^3·m^-3;同化SSM／I 19GHz亮温后,各层土壤温度RMSE平均减小0.76℃,各层土壤水分RMSE平均减小0.018m^3·m^-3。 相似文献
3.
土壤水分在干旱监测、洪水预报和水资源管理中发挥着重要作用.数据同化方法能结合模型和遥感的优势,生产高精度、时空连续的土壤水分数据.目前,陆面数据同化面临的主要难题之一是模型和观测误差的准确估计.为此,我们前面提出了双循环同化算法,实现了模型和观测误差、模型算子和观测算子参数的同时估计.由于该算法需要设置较大的集合个数才能实现稳定的参数估计,导致参数估计效率低,限制了其在大范围陆面同化系统中的应用.为此,本研究通过:(1)采用二元数自动微分方法计算陆面过程模型(LSM)的雅可比矩阵以替代获取复杂的LSM的切线性模型;(2)采用扩展卡尔曼滤波(EKF)框架替代集合卡尔曼滤波框架,提高了双循环算法的参数估计效率.在模型积分步长为1h和参数优化窗口为90天的条件下,改进后的双循环算法参数优化效率提高近60倍.为检验双循环算法在区域尺度陆面数据同化中的效果,我们在青藏高原区域同化SMAP土壤水分产品,并利用四个不同地理气候环境下的土壤水分观测网数据验证同化结果.结果显示,在青藏高原区域,相较于基于默认参数的模型模拟结果,基于EKF改进的双循环同化算法能够校正模型参数和观测的偏差,估计的土壤水分精... 相似文献
4.
数据同化是提升复杂机理过程模型精度的关键技术之一,而湖泊藻类模型的敏感参数具有随时间动态变化的特征,导致数据同化过程中无法精准更新某一时段的敏感参数,影响数据同化的模型精度提升效果.针对上述问题,本研究耦合了参数敏感性分析与集合卡尔曼滤波,研发了一种能够实时识别模型敏感参数的新型数据同化算法;为验证研发算法的效率,依托巢湖的高频水质自动监测数据,测试算法对藻类动态模型的精度提升效果.测试结果表明：研发算法能够精准跟踪模型敏感参数的动态变化,并根据监测数据实时更新模型敏感参数,实现了水质高频自动监测数据与藻类动态模型的深度融合,藻类生物量模拟精度提升了55%,即纳什系数（NSE）从0.49提升到0.76,模拟精度提升效果也显著优于传统数据同化算法（NSE=0.63）.研发算法可应用于其它水生态环境模型的数据同化,为水生态环境相关要素的精准模拟预测提供关键技术支撑. 相似文献
5.
基于残差重采样粒子滤波的土壤水分估算和水力参数同步优化 总被引：1，自引：0，他引：1
陆面数据同化由于能将观测数据和模型模拟有机结合,已逐步发展为地球科学研究的重要方法之一.通过数据同化方法在模型中不断融入新的观测数据,一方面可以有效地校正陆面过程模型的预测轨迹,提高模型状态变量的估算精度,另一方面可以不断减小模型中的不确定因素,优化模型中的相关参数.在众多数据同化算法中,粒子滤波算法不受模型线性和误差高斯分布假设的约束,适用于任意非线性非高斯动态系统,逐渐成为当前数据同化算法研究的热点.本研究基于残差重采样粒子滤波算法发展了一个数据同化方案,将微波亮温数据同化到大尺度半分布式VIC(Variable Infiltration Capacity)陆面水文模型中,对土壤水分进行估算,并对模型中的三个水力参数进行同步优化.最后设计了一系列对比实验并利用美国亚利桑那州在SMEX04(Soil Moisture Experiment 2004)期间获取的一套完整的实验数据对该同化方案进行了验证.结果表明,该同化方案能够大幅度提高土壤水分估算精度,同时模型中的三个水力参数也得到了较好的优化,从而证明了该数据同化方案的有效性. 相似文献
6.
基于集合Kalman滤波及SCE-UA(shuffled complex evolution)算法发展了能够直接同化微波亮温的土壤湿度同化方案. 该方案以陆面过程模式CLM 3.0中的土壤水模型作为预报算子, 以辐射传输模型作为观测算子. 整个同化过程分为参数优化和土壤湿度同化两个阶段, 利用SCE-UA算法优化辐射传输模型中难以确定的植被光学厚度参数和地表粗糙度参数, 并利用优化参数作为观测算子的模型参数进行同化. 通过人工理想试验表明该同化方案可以明显改善表层土壤湿度的模拟精度, 并且对深层土壤湿度的模拟也有一定程度的改善; 利用AMSR-E亮温(10.65 GHz垂直极化)所进行的实际同化试验表明顶层(0~10 cm)土壤湿度同化结果与观测的均方根误差(RMSE)由模拟的0.05052减小到0.03355, 相对减小了33.6%, 而较深层(10~50 cm)平均减小了20.9%. 这些同化试验显示该同化方案的合理性. 相似文献
7.
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
8.
太湖叶绿素a同化系统敏感性分析 总被引：1，自引：1，他引：0
太湖叶绿素a同化系统对于不同参数的敏感性将直接影响到该系统能否精确的估算太湖叶绿素a的浓度分布.利用2009年4月21日环境一号卫星(HJ-1B CCD2)影像数据反演太湖叶绿素a浓度场信息.以此作为背景场信息,结合基于集合均方根滤波的太湖叶绿素a同化系统,分析和评价了样本数目、同化时长、背景场误差、观测误差和模型误差对于同化系统性能的影响.结果表明:从计算成本、系统运行时间和同化效果等方面分析,当集合样本数目达到30~40左右时同化系统取得了较好的结果;同化系统对于背景场误差的估计变化不是很敏感,即初始场的估计是否准确对于同化系统的性能影响不是很大;同化系统对于模型误差和观测误差的变化较为敏感,不同的测试点位由于水体动力学性质不一,其敏感性的表现形式有所差异;利用数据同化方法可以有效地估算太湖叶绿素a浓度. 相似文献
9.
湖泊富营养化响应与流域优化调控决策的模型研究进展 总被引：2，自引：0，他引：2
湖泊富营养化是全球水环境领域面临的长期挑战,富营养化响应与流域优化决策模型是制定经济和高效调控方案的关键.然而已有的模型研究综述主要集中于模型开发、案例应用、敏感性分析、不确定性分析等单一方面,而缺少针对非线性响应、生态系统长期演变等最新湖泊治理挑战的研究总结.本文对数据驱动的统计模型、因果驱动的机理模型和决策导向的优化模型进行了综述.其中,统计模型包含经典统计、贝叶斯统计和机器学习模型,常用于建立响应关系、时间序列特征分析以及预报预警;机理模型包含流域的水文与污染物输移模拟以及湖泊的水文、水动力、水质、水生态等过程的模拟,用于不同时空尺度的变化过程模拟,其中复杂机理模型的敏感性分析、参数校验、模型不确定性等需要较高的计算成本;优化模型结合机理模型形成“模拟优化”体系,在不确定性条件下衍生出随机、区间优化等多种方法,通过并行计算、简化与替代模型可一定程度上解决计算时间成本的瓶颈.本文识别了湖泊治理面临的挑战,包括:①如何定量表征外源输入的非线性叠加和湖泊氮、磷、藻变化的非均匀性?②如何提高优化调控决策和水质目标的关联与精准性?③如何揭示湖泊生态系统的长期变化轨迹与驱动因素?最后,本文针对这些挑战提出研究展望,主要包括:①基于多源数据融合与机器学习算法以提升湖泊的短期水质预测精度;②以生物量为基础的机理模型与行为驱动的个体模型的升尺度或降尺度耦合以表达多种尺度的物质交互过程;③机器学习算法与机理模型的直接耦合或数据同化以降低模拟误差;④时空尺度各异的多介质模拟模型融合以实现精准和动态的优化调控. 相似文献
10.
11.
Evaluating the utility of the ensemble transform Kalman filter for adaptive sampling when updating a hydrodynamic model 总被引：1，自引：0，他引：1
This paper compares two Monte Carlo sequential data assimilation methods based on the Kalman filter, for estimating the effect of measurements on simulations of state error variance made by a one-dimensional hydrodynamic model. The first method used an ensemble Kalman filter (EnKF) to update state estimates, which were then used as initial conditions for further simulations. The second method used an ensemble transform Kalman filter (ETKF) to quickly estimate the effect of measurement error covariance on forecast error covariance without the need to re-run the simulation model. The ETKF gave an unbiased estimate of EnKF analysed error variance, although differences in the treatment of measurement errors meant the results were not identical. Estimates of forecast error variance could also be made, but their accuracy deteriorated as the time from measurements increased due in part to model non-linearity and the decreasing signal variance. The motivation behind the study was to assess the ability of the ETKF to target possible measurements, as part of an adaptive sampling framework, before they are assimilated by an EnKF-based forecasting model on the River Crouch, Essex, UK. The ETKF was found to be a useful tool for quickly estimating the error covariance expected after assimilating measurements into the hydrodynamic model. It, thus, provided a means of quantifying the ‘usefulness’ (in terms of error variance) of possible sampling schemes. 相似文献
12.
Using data assimilation method to calibrate a heterogeneous conductivity field conditioning on transient flow test data 总被引：1，自引：1，他引：0
Juxiu Tong Bill X. Hu Jinzhong Yang 《Stochastic Environmental Research and Risk Assessment (SERRA)》2010,24(8):1211-1223
A data assimilation method is developed to calibrate a heterogeneous hydraulic conductivity field conditioning on transient
pumping test data. The ensemble Kalman filter (EnKF) approach is used to update model parameters such as hydraulic conductivity
and model variables such as hydraulic head using available data. A synthetical two-dimensional flow case is used to assess
the capability of the EnKF method to calibrate a heterogeneous conductivity field by assimilating transient flow data from
observation wells under different hydraulic boundary conditions. The study results indicate that the EnKF method will significantly
improve the estimation of the hydraulic conductivity field by assimilating continuous hydraulic head measurements and the
hydraulic boundary condition will significantly affect the simulation results. For our cases, after a few data assimilation
steps, the assimilated conductivity field with four Neumann boundaries matches the real field well while the assimilated conductivity
field with mixed Dirichlet and Neumann boundaries does not. We found in our cases that the ensemble size should be 300 or
larger for the numerical simulation. The number and the locations of the observation wells will significantly affect the hydraulic
conductivity field calibration. 相似文献
13.
An ensemble Kalman filter (EnKF) is developed to identify a hydraulic conductivity distribution in a heterogeneous medium by assimilating solute concentration measurements of solute transport in the field with a steady‐state flow. A synthetic case with the mixed Neumann/Dirichlet boundary conditions is designed to investigate the capacity of the data assimilation methods to identify a conductivity distribution. The developed method is demonstrated in 2‐D transient solute transport with two different initial instant solute injection areas. The influences of the observation error and model error on the updated results are considered in this study. The study results indicate that the EnKF method will significantly improve the estimation of the hydraulic conductivity field by assimilating solute concentration measurements. The larger area of the initial distribution and the more observed data obtained, the better the calculation results. When the standard deviation of the observation error varies from 1% to 30% of the solute concentration measurements, the simulated results by the data assimilation method do not change much, which indicates that assimilation results are not very sensitive to the standard deviation of the observation error in this study. When the inflation factor is more than 1.0 to enlarge the model error by increasing the forecast error covariance matrix, the updated results of the hydraulic conductivity by the data assimilation method are not good at all. Copyright © 2012 John Wiley & Sons, Ltd. 相似文献
14.
Data assimilation for distributed hydrological catchment modeling via ensemble Kalman filter 总被引：4，自引：0，他引：4
Catchment scale hydrological models are critical decision support tools for water resources management and environment remediation. However, the reliability of hydrological models is inevitably affected by limited measurements and imperfect models. Data assimilation techniques combine complementary information from measurements and models to enhance the model reliability and reduce predictive uncertainties. As a sequential data assimilation technique, the ensemble Kalman filter (EnKF) has been extensively studied in the earth sciences for assimilating in-situ measurements and remote sensing data. Although the EnKF has been demonstrated in land surface data assimilations, there are no systematic studies to investigate its performance in distributed modeling with high dimensional states and parameters. In this paper, we present an assessment on the EnKF with state augmentation for combined state-parameter estimation on the basis of a physical-based hydrological model, Soil and Water Assessment Tool (SWAT). Through synthetic simulation experiments, the capability of the EnKF is demonstrated by assimilating the runoff and other measurements, and its sensitivities are analyzed with respect to the error specification, the initial realization and the ensemble size. It is found that the EnKF provides an efficient approach for obtaining a set of acceptable model parameters and satisfactory runoff, soil water content and evapotranspiration estimations. The EnKF performance could be improved after augmenting with other complementary data, such as soil water content and evapotranspiration from remote sensing retrieval. Sensitivity studies demonstrate the importance of consistent error specification and the potential with small ensemble size in the data assimilation system. 相似文献
15.
The objective of the study is to evaluate the potential of a data assimilation system for real-time flash flood forecasting over small watersheds by updating model states. To this end, the Ensemble Square-Root-Filter (EnSRF) based on the Ensemble Kalman Filter (EnKF) technique was coupled to a widely used conceptual rainfall-runoff model called HyMOD. Two small watersheds susceptible to flash flooding from America and China were selected in this study. The modeling and observational errors were considered in the framework of data assimilation, followed by an ensemble size sensitivity experiment. Once the appropriate model error and ensemble size was determined, a simulation study focused on the performance of a data assimilation system, based on the correlation between streamflow observation and model states, was conducted. The EnSRF method was implemented within HyMOD and results for flash flood forecasting were analyzed, where the calibrated streamflow simulation without state updating was treated as the benchmark or nature run. Results for twenty-four flash-flood events in total from the two watersheds indicated that the data assimilation approach effectively improved the predictions of peak flows and the hydrographs in general. This study demonstrated the benefit and efficiency of implementing data assimilation into a hydrological model to improve flash flood forecasting over small, instrumented basins with potential application to real-time alert systems. 相似文献
16.
This paper presents a coupling of an ensemble Kalman filter (EnKF) with a discontinuous Galerkin-based, two-dimensional circulation model (DG ADCIRC-2DDI) to improve the state estimation of tidal hydrodynamics including water surface elevations and depth-integrated velocities. The methodology in this paper using EnKF perturbs the modeled hydrodynamics and bottom friction parameterization in the model while assimilating data with inherent error, and demonstrates a capability to apply EnKF within DG ADCIRC-2DDI for data assimilation. Parallel code development presents a unique aspect of the approach taken and is briefly described in the paper, followed by an application to a real estuarine system, the lower St. Johns River in north Florida, for the state estimation of tidal hydrodynamics. To test the value of gauge observations for improving state estimation, a tide modeling case study is performed for the lower St. Johns River successively using one of the four available tide gauging stations in model-data comparison. The results are improved simulations of water surface elevations and depth-integrated velocities using DG ADCIRC-2DDI with EnKF, both locally where data are available and non-locally where data are not available. The methodology, in general, is extensible to other modeling and data applications, for example, the use of remote sensing data, and specifically, can be readily applied as is to study other tidal systems. 相似文献
17.
Data assimilation techniques have been proven as an effective tool to improve model forecasts by combining information about observed variables in many areas. This article examines the potential of assimilating surface soil moisture observations into a field‐scale hydrological model, the Root Zone Water Quality Model, to improve soil moisture estimation. The Ensemble Kalman Filter (EnKF), a popular data assimilation technique for nonlinear systems, was applied and compared with a simple direct insertion method. In situ soil moisture data at four different depths (5, 20, 40, and 60 cm) from two agricultural fields (AS1 and AS2) in northeastern Indiana were used for assimilation and validation purposes. Through daily update, the EnKF improved soil moisture estimation compared with the direct insertion method and model results without assimilation, having more distinct improvement at the 5 and 20 cm depths than for deeper layers (40 and 60 cm). Local vertical soil property heterogeneity in AS1 deteriorated soil moisture estimates with the EnKF. Removal of systematic bias in the forecast model was found to be critical for more successful soil moisture data assimilation studies. This study also demonstrates that a more frequent update generally contributes in enhancing the open loop simulation; however, large forecasting error can prevent more frequent update from providing better results. In addition, results indicate that various ensemble sizes make little difference in the assimilation results. An ensemble of 100 members produced results that were comparable with results obtained from larger ensembles. Copyright © 2011 John Wiley & Sons, Ltd. 相似文献
18.
Model parameters are a source of uncertainty that can easily cause systematic deviation and significantly affect the accuracy of soil moisture generation in assimilation systems. This study addresses the issue of retrieving model parameters related to soil moisture via the simultaneous estimation of states and parameters based on the Common Land Model (CoLM). The state-parameter estimation algorithms AEnKF (Augmented Ensemble Kalman Filter), DEnKF (Dual Ensemble Kalman Filter) and SODA (Simultaneous optimization and data assimilation) are entirely implemented within an EnKF framework to investigate how the three algorithms can correct model parameters and improve the accuracy of soil moisture estimation. The analysis is illustrated by assimilating the surface soil moisture levels from varying observation intervals using data from Mongolian plateau sites. Furthermore, a radiation transfer model is introduced as an observation operator to analyze the influence of brightness temperature assimilation on states and parameters that are estimated at different microwave signal frequencies. Three cases were analyzed for both soil moisture and brightness temperature assimilation, focusing on the progressive incorporation of parameter uncertainty, forcing data uncertainty and model uncertainty. It has been demonstrated that EnKF is outperformed by all other methods, as it consistently maintains a bias. State-parameter estimation algorithms can provide a more accurate estimation of soil moisture than EnKF. AEnKF is the most robust method, with the lowest RMSE values for retrieving states and parameters dealing only with parameter uncertainty, but it possesses disadvantages related to increasing sources of uncertainty and decreasing numbers of observations. SODA performs well under the complex situations in which DEnKF shows slight disadvantages in terms of statistical indicators; however, the former consumes far more memory and time than the latter. 相似文献
19.
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
20.
A series of numerical experiments for data assimilation with the Ensemble Kalman Filter (EnKF) in a shallow water model are
reported. Temperature profiles measured at a North Sea location, 55°30ˊ North and 0°55ˊ East (referred to as the CS station
of the NERC North Sea project), are assimilated in 1-D simulations. Comparison of simulations without assimilation to model
results obtained when assimilating data with the EnKF allows us to assess the filter performance in reproducing features of
the observations not accounted for by the model. The quality of the model error sampling is tested as well as the validity
of the Gaussian hypothesis underlying the analysis scheme of the EnKF. The influence of the model error parameters and the
frequency of the data assimilation are investigated and discussed. From these experiments, a set of optimal parameters for
the model error sampling are deduced and used to test the behavior of the EnKF when propagating surface information into the
water column. 相似文献