共查询到18条相似文献，搜索用时 203 毫秒
1.
数据同化是在基于物理机制的背景模型上,融合时空不规则分布的观测数据的一种现报方法.同化能够有效弥补数据的时空局限和模型的精度偏差,使二者相互匹配从而获得更加合理可信的模拟效果.本研究利用电离层数据同化方法,针对中国及周边区域（15°N-55°N,70°E-140°E）构建了电离层总电子含量（TEC）同化现报系统.系统使用国际参考电离层（IRI）作为背景场,利用中国科学院空间环境监测网和国际GNSS服务组织（IGS）的部分地基GNSS台站数据作为观测值,并采用三维变分与Gauss-Markov卡尔曼滤波相结合的算法进行背景场和观测值的数据同化,生成覆盖中国及周边区域的电离层TEC和GPS单频接收机延迟误差的格点化准实时现报地图,并在中国科学院空间环境预报中心（http：//sepc.ac.cn/TEC_chn.php）网上发布,每15 min进行更新.该系统是我国基于同化算法的电离层现报系统之一,已用于中国及周边区域的电离层环境实时监测,可为卫星导航、雷达成像、短波通信等科学研究和工程应用提供相对及时、准确、有效的电离层TEC和误差修正信息. 相似文献
2.
本文采用高效集合卡尔曼滤波(EnKF)算法和背景场热层电离层理论模式NCAR-TIEGCM,开发了热层电离层数据同化系统.基于全球空地基GNSS电离层斜TEC观测、CHAMP和TIMED/GUVI热层参量观测构型设计了系列观测系统模拟实验,对热层参量进行估计.实验结果表明,(1)通过集合卡尔曼滤波算法同化电离层TEC观测能够较好地优化热层参量.(2)中性质量密度优化效果在整个同化阶段均有提升,提升百分比能达到40%.(3)积分氧氮比在同化阶段也能得到较好的优化,但在电子密度水平梯度变化剧烈区域效果较差.最后本文对中性质量密度进行了预报评估,结果表明,由于中性成分优化,在地磁平静条件下其预报时间尺度可长达24h. 相似文献
3.
地磁扰动是空间天气中的重要现象,对地基技术系统具有重要的影响.准确预报地磁扰动可以有效避免重大灾害发生.本文基于Weimer电势和磁势模型发展了高纬地区地磁扰动的模拟方法,并与地面台站观测数据进行了比较.地表磁场扰动主要受电离层电流系统的影响,利用Weimer模式计算出电离层等效电流分布后,基于毕奥-萨伐尔定律推导了地磁扰动三分量与电流的关系,最终计算出地磁扰动量.模型的输入参数为太阳风速度、太阳风密度、行星际磁场和磁偶极倾角.模型计算结果与不同纬度和经度的地磁台站观测结果对比表明本文的计算方法能有效地模拟地磁暴期间地磁扰动特征.本文结果对今后发展高纬地区地磁场预报模型奠定了重要基础. 相似文献
4.
《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
5.
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
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.
对国内外电离层参数短期预报方法进行了综述,重点介绍了几种作者最新研究的电离层foF2参数短期预报方法.包括基于混沌时间序列分析的电离层foF2参数提前15 min(分钟)准实时预测方法、基于人工神经网络技术的提前1 h(小时)现报方法、提前1～3 d(天)的神经网络预测方法、相似日短期预报方法以及综合预报模型方法.利用中国垂测站多年的观测数据对各种算法的预测精度进行了评估,并与国内外相关算法进行了定性或定量比较,各种预报方法都在前人的预报精度基础之上有了一定的提高.其中提前15 min(分钟)预测方法平均相对误差小于4%,平均绝对误差小于0.2 MHz,可以用于实时性和精度要求较高的短波系统;提前1小时预报方法在太阳活动高年平均预测相对误差小于6%,均方根误差小于0.6 MHz,太阳活动低年平均预测相对误差小于10%,均方根误差小于0.5 MHz,平均相对误差比前人研究的自相关方法提高3个百分点左右;对于提前1～3 d(天)短期预报,综合预报模型方法充分利用了神经网络方法、自相关方法以及相似日方法的优点,获得了高于任何一种单一方法的精度,对于中国9个垂测站(海口、广州、重庆、拉萨、兰州、北京、乌鲁木齐、长春、满洲里)在不同太阳活动性条件下的历史数据进行了精度测试,提前1天和提前3天预测的平均相对误差分别小于10%和小于15%,达到了国内先进水平.此外,该方法还可以综合更多预报方法,具有进一步提高预报精度的潜力.文中提出的针对不同尺度进行电离层参数预测的方法具有一定的理论基础,且精度高、易实现,对从事电离层短期预报算法研究及相关专业的学者具有一定的参考价值. 相似文献
8.
《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
9.
作为最重要的电离层参量之一,电离层电子浓度总含量(TEC)可以通过当前广泛使用的全球定位系统(GPS)的信标进行观测.我们在我国北起漠河、经北京和武汉、南到三亚四个观测站建立了GPS接收站,经单站数据处理后将原始的单站GPS TEC观测数据上载到北京数据处理中心;采用我们发展的经验基函数模式算法,用实测数据估算格点TEC并提供给用户,同时生成覆盖中国疆域的TEC地图并在因特网上实时发布.这一电离层TEC现报系统是我国首个类似的技术系统,在观测站布局和TEC地图算法上有所创新.该系统已用于实时监测我国电离层环境,并可为我国卫星定位导航和测控等技术系统的电波修正提供实测电离层数据. 相似文献
10.
电离层掩星数据反演的传统方法是Abel反演法及其改进方法,而实际电离层的非球对称给电离层电子密度反演带来误差.本文研究了在气象领域广泛应用的变分同化方法在掩星数据反演电离层电子密度廓线的可行性,利用IRI和Nequiek模型模拟掩星真值场和背景场进行数值模拟反演,并与Abel反演法反演结果进行对比.结果表明,变分同化反演方法能够有效综合模式和观测数据,使得反演结果精度较高;与Abel反演法相比,反演的电子密度廓线F2层峰值浓度误差在10％以下,而Abel反演法在20％～30％之间;而且变分同化反演法对误差扰动有较好的过滤性,因此实用性较强. 相似文献
11.
Dual state-parameter estimation of root zone soil moisture by optimal parameter estimation and extended Kalman filter data assimilation 总被引：1，自引：0，他引：1
Haishen Lü Zhongbo Yu Yonghua ZhuSam Drake Zhenchun HaoEdward A. Sudicky 《Advances in water resources》2011,34(3):395-406
With well-determined hydraulic parameters in a hydrologic model, a traditional data assimilation method (such as the Kalman filter and its extensions) can be used to retrieve root zone soil moisture under uncertain initial state variables (e.g., initial soil moisture content) and good simulated results can be achieved. However, when the key soil hydraulic parameters are incorrect, the error is non-Gaussian, as the Kalman filter will produce a persistent bias in its predictions. In this paper, we propose a method coupling optimal parameters and extended Kalman filter data assimilation (OP-EKF) by combining optimal parameter estimation, the extended Kalman filter (EKF) assimilation method, a particle swarm optimization (PSO) algorithm, and Richards’ equation. We examine the accuracy of estimating root zone soil moisture through the optimal parameters and extended Kalman filter data assimilation method by using observed in situ data at the Meiling experimental station, China. Results indicate that merely using EKF for assimilating surface soil moisture content to obtain soil moisture content in the root zone will produce a persistent bias between simulated and observed values. Using the OP-EKF assimilation method, estimates were clearly improved. If the soil profile is heterogeneous, soil moisture retrieval is accurate in the 0-50 cm soil profile and is inaccurate at 100 cm depth. Results indicate that the method is useful for retrieving root zone soil moisture over large areas and long timescales even when available soil moisture data are limited to the surface layer, and soil moisture content are uncertain and soil hydraulic parameters are incorrect. 相似文献
12.
Marc Leisenring Hamid Moradkhani 《Stochastic Environmental Research and Risk Assessment (SERRA)》2011,25(2):253-270
Using the U.S. National Weather Service’s SNOW-17 model, this study compares common sequential data assimilation methods,
the ensemble Kalman filter (EnKF), the ensemble square root filter (EnSRF), and four variants of the particle filter (PF),
to predict seasonal snow water equivalent (SWE) within a small watershed near Lake Tahoe, California. In addition to SWE estimation,
the various data assimilation methods are used to estimate five of the most sensitive parameters of SNOW-17 by allowing them
to evolve with the dynamical system. Unlike Kalman filters, particle filters do not require Gaussian assumptions for the posterior
distribution of the state variables. However, the likelihood function used to scale particle weights is often assumed to be
Gaussian. This study evaluates the use of an empirical cumulative distribution function (ECDF) based on the Kaplan–Meier survival
probability method to compute particle weights. These weights are then used in different particle filter resampling schemes.
Detailed analyses are conducted for synthetic and real data assimilation and an assessment of the procedures is made. The
results suggest that the particle filter, especially the empirical likelihood variant, is superior to the ensemble Kalman
filter based methods for predicting model states, as well as model parameters. 相似文献
13.
The Ensemble Kalman Filter (EnKF) is well known and widely used in land data assimilation for its high precision and simple operation. The land surface models used as the forecast operator in a land data assimilation system are usually designed to consider the model subgrid-heterogeneity and soil water thawing and freezing. To neglect their effects could lead to some errors in soil moisture assimilation. The dual EnKF method is employed in soil moisture data assimilation to build a soil moisture data as- similation framework based on the NCAR Community Land Model version 2.0 (CLM 2.0) in considera- tion of the effects of the model subgrid-heterogeneity and soil water thawing and freezing: Liquid volumetric soil moisture content in a given fraction is assimilated through the state filter process, while solid volumetric soil moisture content in the same fraction and solid/liquid volumetric soil moisture in the other fractions are optimized by the parameter filter. Preliminary experiments show that this dual EnKF-based assimilation framework can assimilate soil moisture more effectively and precisely than the usual EnKF-based assimilation framework without considering the model subgrid-scale heteroge- neity and soil water thawing and freezing. With the improvement of soil moisture simulation, the soil temperature-simulated precision can be also improved to some extent. 相似文献
14.
Annette Eicker Maike Schumacher Jürgen Kusche Petra Döll Hannes Müller Schmied 《Surveys in Geophysics》2014,35(6):1285-1309
We introduce a new ensemble-based Kalman filter approach to assimilate GRACE satellite gravity data into the WaterGAP Global Hydrology Model. The approach (1) enables the use of the spatial resolution provided by GRACE by including the satellite observations as a gridded data product, (2) accounts for the complex spatial GRACE error correlation pattern by rigorous error propagation from the monthly GRACE solutions, and (3) allows us to integrate model parameter calibration and data assimilation within a unified framework. We investigate the formal contribution of GRACE observations to the Kalman filter update by analysis of the Kalman gain matrix. We then present first model runs, calibrated via data assimilation, for two different experiments: the first one assimilates GRACE basin averages of total water storage and the second one introduces gridded GRACE data at \(5^\circ\) resolution into the assimilation. We finally validate the assimilated model by running it in free mode (i.e., without adding any further GRACE information) for a period of 3 years following the assimilation phase and comparing the results to the GRACE observations available for this period. 相似文献
15.
Real-time assimilation of streamflow observations into a hydrological routing model: effects of model structures and updating methods 总被引：1，自引：1，他引：0
Maurizio Mazzoleni Haksu Lee Yuqiong Liu Dong-Jun Seo Alessandro Amaranto 《水文科学杂志》2018,63(3):386-407
This paper comparatively assesses the performance of five data assimilation techniques for three-parameter Muskingum routing with a spatially lumped or distributed model structure. The assimilation techniques used include direct insertion (DI), nudging scheme (NS), Kalman filter (KF), ensemble Kalman filter (EnKF) and asynchronous ensemble Kalman filter (AEnKF), which are applied to river reaches in Texas and Louisiana, USA. For both lumped and distributed routing, results from KF, EnKF and AEnKF are sensitive to the error specification. As expected, DI outperformed the other models in the case of lumped modelling, while in distributed routing, KF approaches, particularly AEnKF and EnKF, performed better than DI or nudging, reflecting the benefit of updating distributed states through error covariance modelling in KF approaches. The results of this work would be useful in setting up data assimilation systems that employ increasingly abundant real-time observations using distributed hydrological routing models. 相似文献
16.
17.
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
18.
《Advances in water resources》2001,24(6):631-650
This paper investigates the ability to retrieve the true soil moisture and temperature profiles by assimilating near-surface soil moisture and surface temperature data into a soil moisture and heat transfer model. The direct insertion and Kalman filter assimilation schemes have been used most frequently in assimilation studies, but no comparisons of these schemes have been made. This study investigates which of these approaches is able to retrieve the soil moisture and temperature profiles the fastest, over what depth soil moisture observations are required, and the effect of update interval on profile retrieval. These questions are addressed by a desktop study using synthetic data. The study shows that the Kalman filter assimilation scheme is superior to the direct insertion assimilation scheme, with retrieval of the soil moisture profile being achieved in 12 h as compared to 8 days or more, depending on observation depth, for hourly observations. It was also found that profile retrieval could not be realised for direct insertion of the surface node alone, and that observation depth does not have a significant effect on profile retrieval time for the Kalman filter. The observation interval was found to be unimportant for profile retrieval with the Kalman filter when the forcing data is accurate, whilst for direct insertion the continuous Dirichlet boundary condition was required for an increasingly longer period of time. It was also found that the Kalman filter assimilation scheme was less susceptible to unstable updates if volumetric soil moisture was modelled as the dependent state rather than matric head, because the volumetric soil moisture state is more linear in the forecasting model. 相似文献