共查询到19条相似文献，搜索用时 125 毫秒
1.
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
2.
富营养化模型是进行湖泊水环境质量预测和管理的重要工具,然而模型客观存在的误差一直是应用者关心的重要问题.数据同化作为连接观测数据与数值模型的重要方法,可以有效提高模型的准确性.集合卡尔曼滤波(En KF)是众多数据同化算法中应用最为广泛的一种,可进行非线性系统的数据同化,并能有效降低数据同化的计算量.本研究以太湖作为具体实例,选择Delft3D-BLOOM作为富营养化模型,在数值实验确定En KF集合数为100、观测误差方差为1%、模拟误差方差为10%的基础上分别进行模型状态变量同化以及状态变量与关键参数同步同化.结果显示,仅同化状态变量时,模型预测精度有所增加;同时同化状态变量和关键参数时,可显著提升模型在湖泊水环境质量预测中的精度.该研究为应用集合卡尔曼滤波以提高复杂的湖库富营养化模型模拟精度提供了有效的方法. 相似文献
3.
若不考虑特定的数据同化方法,数据同化通常可被分解为先验信息、观测算子、观测误差协方差和背景误差协方差等组成部分.本文基于经典的Lorenz模式,研究了数据同化各组成部分对初始条件误差和预报误差的影响,以期为设计不同尺度天气系统的数据同化方法提供理论基础.研究结果表明,预报误差经历三个典型阶段:0～5天为预报误差的缓慢增长期; 5～15天为预报误差的快速增长期,其中确定性预报和集合预报的误差增长速率具有显著差异; 15天后为预报误差的饱和期.数据同化可通过提供更加准确的初始条件,进而提升可预报性.相比于静态背景误差协方差(B),流依赖的背景误差协方差(Pf)可提供更精确的初始条件,因此当瞬时观测或频繁的时间平均观测被同化时,循环同化效果优于离线同化;但当时间平均观测频率低时则结果相反,这是因为循环同化在模式缺乏预报技巧时无法构造具有信息的先验估计,且流依赖的Pf相比于静态的B不能有效地从含信息量低的观测中提取出观测信息.瞬时观测相比于时间平均观测包含更多的信息,因此在时间频率低的观测系统中,瞬时观测应优先被考虑.此外,集合预报优于确定性预报,且... 相似文献
4.
由于GRACE卫星数据解算的时变重力场模型中高阶位系数存在误差,这些误差在重力异常图中表现为南北向的条带噪声,在应用GRACE时变重力场数据时必须进行滤波.本文在空间域引入了一种有效消除GRACE时变重力场条带噪声的平滑先验信息方法,并将其与目前常用的高斯滤波和去相关误差等滤波方法分别应用于合成的质量变化趋势数字模型,检测不同滤波方法消除条带噪声的能力及其对真实信号的影响.滤波结果显示,与目前常用的高斯滤波和去相关误差滤波器相比,本文滤波方法在有效移除条带噪声的同时,具有有效信号幅度衰减小、有效信号形变小以及保存了更多的短波长细节信息等优势;此外,统计结果显示,本文滤波结果在信号最大值、最小值以及残差均方根等方面均与模拟真实信号最为接近.相比300km高斯平滑和组合滤波结果,有效信号振幅的极小值和极大值分别提高了约18%和6%,残差均方根分别降低了25%和33%.说明本文滤波方法移除GRACE相关误差的同时,在保留有效信号方面具有明显的优势. 相似文献
5.
短期气候预测中如何将气候模式和统计方法的预测结果科学、客观的集成起来,一直是非常重要的问题.本文针对动力模式和统计方法预测结果相结合的问题,引入资料同化中信息融合的思想,采用最优内插同化方法,实现了动力模式和统计季节降水预测结果的融合.检验表明,对1982-2015年我国夏季降水百分率的回报,融合预测结果与观测的平均空间相关系数可达0.44,分别较统计预测和CFSv2模式统计降尺度订正的技巧提高了0.1左右,而均方根误差较两者可以降低5%~20%.可见,该方法可以进一步提升对我国夏季降水的预测技巧,具有显著的业务应用价值. 相似文献
6.
《中国科学:地球科学》2018,(11)
文章研究了基于理论模型TIEGCM的电离层热层同化预报系统,通过同化模拟的COSMIC掩星观测电子密度廓线,利用集合卡尔曼滤波方法和联合状态参数估计理论,优化估计了离子成分、中性大气温度、风场以及中性成分等背景参数.电子密度的误差统计结果显示,氧离子和中性成分对电子密度预报改善效果最佳,温度和风场较差.在同化期间离子成分、中性成分和温度的优化在12h内分别使电子密度的误差下降约30%、40%和10%.由于作用于不同的物理化学过程,中性成分、温度和离子成分影响的弛豫时间分别约为12h以上、6h和3h.同化9h以后中性成分的优化对电子密度预报的改善效果好于离子成分,这表明中性成分在电离层预报中是最重要的热层背景参数.实测数据同化结果显示,离子成分和中性成分均可使电子密度预报偏差减小约20%,并且中性成分的弛豫时间更长. 相似文献
7.
Tikhonov正则化反问题思想应用于变分同化时,通常引入的单正则化参数并不能同时满足不同观测资料的误差特性.针对传统四维变分同化(4D-Var)中不同观测资料分别引入不同正则化参数,提出基于多正则化参数约束的4D-Var(Tikh-4D-Var);同时,鉴于实际维数巨大同化系统中多正则化参数难于计算问题,基于同化系统后验估计信息,引入一种新的多正则化参数选择方法,相比于传统正则化参数选择方法,该方法计算量较小.基于WRF3.3.1 4D-Var同化系统,利用2010年Chaba台风个例开展bogus资料同化台风初始化应用试验,结果表明:结合引入的多正则化参数选择方法,Tikh-4D-Var方法相比于传统4D-Var方法更快达到收敛标准,迭代次数更少;同时,相比于传统4D-Var方法,Tikh-4D-Var方法呈现出更优的同化和预报效果,使得72 h路径和强度预报误差减小的同时,进一步改善了台风的内部结构信息;多正则化参数在一定程度上可反应同化系统中观测资料误差方差给定的准确性. 相似文献
8.
针对延伸期尺度的可预报分量,借鉴了CNOP相关算法,形成了在数值模式中提取可预报分量的实用方法和预报技术.从模式预报误差增长的角度将模式变量分为可预报分量和不可预报的混沌分量,将可预报分量定义为在预报时段内误差增长较慢的分量.基于现有的国家气候中心月动力延伸预报业务模式,建立了针对可预报分量的数值模式.同时结合历史资料有用信息,对数值模式的可预报分量,在历史资料的可预报分量中寻找相似场,降低了相似判断过程中变量的维数,进一步对可预报分量的预报误差进行订正.对混沌分量利用历史资料,通过集合预报方法得出其期望值和方差.数值试验结果表明,该方法能有效提高10～30天延伸期数值模式大气环流场的预报技巧,具有良好的业务应用前景. 相似文献
9.
《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
10.
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
11.
An effective method based on dynamic sampling for data assimilation in a global wave model 总被引：3，自引：2，他引：1
The ensemble Kalman filter (EnKF) performs well because that the covariance of background error is varying along time. It provides a dynamic estimate of background error and represents the reasonable statistic characters of background error. However, high computational cost due to model ensemble in EnKF is employed. In this study, two methods referred as static and dynamic sampling methods are proposed to obtain a good performance and reduce the computation cost. Ensemble adjustment Kalman filter (EAKF) method is used in a global surface wave model to examine the performance of EnKF. The 24-h interval difference of simulated significant wave height (SWH) within 1 year is used to compose the static samples for ensemble errors, and these errors are used to construct the ensemble states at each time the observations are available. And then, the same method of updating the model states in the EAKF is applied for the ensemble states constructed by a static sampling method. The dynamic sampling method employs a similar method to construct the ensemble states, but the period of the simulated SWH is changing with time. Here, 7 days before and after the observation time is used as this period. To examine the performance of three schemes, EAKF, static, or dynamic sampling method, observations from satellite Jason-2 in 2014 are assimilated into a global wave model, and observations from satellite Saral are used for validation. The results indicate that the EAKF performs best, while the static sampling method is relatively worse. The dynamic sampling method improves an assimilation effect dramatically compared to the static sampling method, and its overall performance is closed to the EAKF. In low latitudes, the dynamic sampling method has a slight advantage over the EAKF. In the dynamic or static sampling methods, only one wave model is required to run and their computational cost is reduced sharply. According to the performance of these three methods, the dynamic sampling method can treated as an effective alternative of EnKF, which could reduce the computational cost and provide a good performance of data assimilation. 相似文献
12.
This study has applied evolutionary algorithm to address the data assimilation problem in a distributed hydrological model. The evolutionary data assimilation (EDA) method uses multi-objective evolutionary strategy to continuously evolve ensemble of model states and parameter sets where it adaptively determines the model error and the penalty function for different assimilation time steps. The assimilation was determined by applying the penalty function to merge background information (i.e., model forecast) with perturbed observation data. The assimilation was based on updated estimates of the model state and its parameterizations, and was complemented by a continuous evolution of competitive solutions.The EDA was illustrated in an integrated assimilation approach to estimate model state using soil moisture, which in turn was incorporated into the soil and water assessment tool (SWAT) to assimilate streamflow. Soil moisture was independently assimilated to allow estimation of its model error, where the estimated model state was integrated into SWAT to determine background streamflow information before they are merged with perturbed observation data. Application of the EDA in Spencer Creek watershed in southern Ontario, Canada generates a time series of soil moisture and streamflow. Evaluation of soil moisture and streamflow assimilation results demonstrates the capability of the EDA to simultaneously estimate model state and parameterizations for real-time forecasting operations. The results show improvement in both streamflow and soil moisture estimates when compared to open-loop simulation, and a close matching between the background and the assimilation illustrates the forecasting performance of the EDA approach. 相似文献
13.
The paper presents a novel approach to the setup of a Kalman filter by using an automatic calibration framework for estimation of the covariance matrices. The calibration consists of two sequential steps: (1) Automatic calibration of a set of covariance parameters to optimize the performance of the system and (2) adjustment of the model and observation variance to provide an uncertainty analysis relying on the data instead of ad-hoc covariance values. The method is applied to a twin-test experiment with a groundwater model and a colored noise Kalman filter. The filter is implemented in an ensemble framework. It is demonstrated that lattice sampling is preferable to the usual Monte Carlo simulation because its ability to preserve the theoretical mean reduces the size of the ensemble needed. The resulting Kalman filter proves to be efficient in correcting dynamic error and bias over the whole domain studied. The uncertainty analysis provides a reliable estimate of the error in the neighborhood of assimilation points but the simplicity of the covariance models leads to underestimation of the errors far from assimilation points. 相似文献
14.
Coastal management and maritime safety strongly rely on accurate representations of the sea state. Both dynamical models and observations provide abundant pieces of information. However, none of them provides the complete picture. The assimilation of observations into models is one way to improve our knowledge of the ocean state. Its application in coastal models remains challenging because of the wide range of temporal and spatial variabilities of the processes involved. This study investigates the assimilation of temperature profiles with the ensemble Kalman filter in 3-D North Sea simulations. The model error is represented by the standard deviation of an ensemble of model states. Parameters’ values for the ensemble generation are first computed from the misfit between the data and the model results without assimilation. Then, two square root algorithms are applied to assimilate the data. The impact of data assimilation on the simulated temperature is assessed. Results show that the ensemble Kalman filter is adequate for improving temperature forecasts in coastal areas, under adequate model error specification. 相似文献
15.
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
16.
Argo data assimilation in ocean general circulation model of Northwest Pacific Ocean 总被引：2，自引：0，他引：2
Xunqiang Yin Fangli Qiao Yongzeng Yang Changshui Xia Xianyao Chen 《Ocean Dynamics》2012,62(7):1059-1071
The Argo temperature and salinity profiles in 2005–2009 are assimilated into a coastal ocean general circulation model of the Northwest Pacific Ocean using the ensemble adjustment Kalman filter (EAKF). Three numerical tests, including the control run (CTL) (without data assimilation, which serves as the reference experiment), ensemble free run (EnFR) (without data assimilation), and EAKF experiment (with Argo data assimilation using EAKF), are carried out to examine the performance of this system. Using the restarts of different years as the initial conditions of the ensemble integrations, the ensemble spreads from EnFR and EAKF are all kept at a finite value after a sharp decreasing in the first few months because of the sensitive of the model to the initial conditions, and the reducing of the ensemble spread due to Argo data assimilation is not much. The ensemble samples obtained in this way can well represent the probabilities of the real ocean states, and no ensemble inflation is necessary for this EAKF experiment. Different experiment results are compared with satellite sea surface temperature (SST) data and the Global Temperature-Salinity Profile Program (GTSPP) data. The comparison of SST shows that modeled SST errors are reduced after data assimilation; the error reduction percentage after assimilating the Argo profiles is about 10?% on average. The comparison against the GTSPP profiles, which are independent of the Argo profiles, shows improvements in both temperature and salinity. The comparison results indicated a great error reduction in all vertical layers relative to CTL and the ensemble mean of EnFR; the maximum value for temperature and salinity reaches to 85?% and 80?%, respectively. The standard deviations of sea surface height are employed to examine the simulation ability, and it is shown that the mesoscale variability is improved after Argo data assimilation, especially in the Kuroshio extension area and along the section of 10°N. All these results suggest that this system is potentially useful for improving the simulation ability of oceanic numerical models. 相似文献
17.
Local extreme rain usually resulted in disasters such as flash floods and landslides. Upon today, it is still one of the most difficult tasks for operational weather forecast centers to predict those events accurately. In this paper, we simulate an extreme precipitation event with ensemble Kalman filter (EnKF) assimilation of Doppler radial-velocity observations, and analyze the uncertainties of the assimilation. The results demonstrate that, without assimilation radar data, neither a single initialization of deterministic forecast nor an ensemble forecast with adding perturbations or multiple physical parameterizations can predict the location of strong precipitation. However, forecast was significantly improved with assimilation of radar data, especially the location of the precipitation. The direct cause of the improvement is the buildup of a deep mesoscale convection system with EnKF assimilation of radar data. Under a large scale background favorable for mesoscale convection, efficient perturbations of upstream mid-low level meridional wind and moisture are key factors for the assimilation and forecast. Uncertainty still exists for the forecast of this case due to its limited predictability. Both the difference of large scale initial fields and the difference of analysis obtained from EnKF assimilation due to small amplitude of initial perturbations could have critical influences to the event's prediction. Forecast could be improved through more cycles of EnKF assimilation. Sensitivity tests also support that more accurate forecasts are expected through improving numerical models and observations. 相似文献
18.
Bias aware Kalman filters: Comparison and improvements 总被引：1，自引：0，他引：1
This paper reviews two different approaches that have been proposed to tackle the problems of model bias with the Kalman filter: the use of a colored noise model and the implementation of a separate bias filter. Both filters are implemented with and without feedback of the bias into the model state. The colored noise filter formulation is extended to correct both time correlated and uncorrelated model error components. A more stable version of the separate filter without feedback is presented. The filters are implemented in an ensemble framework using Latin hypercube sampling. The techniques are illustrated on a simple one-dimensional groundwater problem. The results show that the presented filters outperform the standard Kalman filter and that the implementations with bias feedback work in more general conditions than the implementations without feedback. 相似文献
19.
The proper orthogonal decomposition (POD) method is used to construct a set of basis functions for spanning the ensemble of
data in a certain least squares optimal sense. Compared with the singular value decomposition (SVD), the POD basis functions
can capture more energy in the forecast ensemble space and can represent its spatial structure and temporal evolution more
effectively. After the analysis variables are expressed by a truncated expansion of the POD basis vectors in the ensemble
space, the control variables appear explicitly in the cost function, so that the adjoint model, which is used to derive the
gradient of the cost function with respect to the control variables, is no longer needed. The application of this new technique
significantly simplifies the data assimilation process. Several assimilation experiments show that this POD-based explicit
four-dimensional variational data assimilation method performs much better than the usual ensemble Kalman filter method on
both enhancing the assimilation precision and reducing the computation cost. It is also better than the SVD-based explicit
four-dimensional assimilation method, especially when the forecast model is not perfect and the forecast error comes from
both the noise of the initial filed and the uncertainty of the forecast model.
Supported by the National Natural Science Foundation of China (Grant No. 40705035), National High Technology Research and
Development Program of China (Grant No. 2007AA12Z144), Knowledge Innovation Project of Chinese Academy of Sciences (Grant
Nos. KZCX2-YW-217 and KZCX2-YW-126-2), and National Basic Research Program of China (Grant No. 2005CB321704) 相似文献