共查询到19条相似文献，搜索用时 359 毫秒
1.
《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
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本文给出了一个基于Gauss-Markov卡尔曼滤波的电离层数据同化系统的初步构建和试验结果.我们选择中国及周边地区部分涉及电离层观测的台站(包括子午工程台站、中国地壳形变网和部分IGS台站)作为观测系统进行模拟试验,背景场利用IRI模式,观测值则由NeQuick模式计算得到.我们的同化结果表明,采用Kalman滤波算法,把部分斜TEC同化到背景模式当中,能够获得较好的同化结果,说明我们设计的算法可行、所选择的各种参数比较合理,采用Gauss-Markov假设进行短期预报也取得了较合理的结果.本项研究经过进一步的改进和完善,可以用来对中国地区的电离层进行现报和短期预报,一方面满足相关空间工程应用,另一方面可以提升现有观测系统的科学意义. 相似文献
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本文利用北美、南美和大洋洲三个地区的电离层TEC数据,分析了磁偏角为零的经度线两侧中纬电离层TEC的差异.结果表明,在2001年至2010年的几乎所有季节,在磁偏角为零的经度东西两侧,北美、南美和大洋洲中纬电离层TEC都存在规则性的差异;中纬电离层TEC的这种经度差异显著地依赖地方时,对季节和太阳活动水平也有不同程度的依赖.地磁场影响下电离层与热层动力学耦合的分析表明,磁偏角的经度变化和热层风的地方时变化两者的共同作用是引起磁偏角为零的经度两侧中纬电离层TEC差异的重要原因之一. 相似文献
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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
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热层金属层位于电离层E层和F层的过渡区域,为研究105～200 km之间的中性和电离成分的相互作用过程提供了独特的示踪剂.为更好地了解热层金属层的来源和形成机制,本文基于北京延庆台站(40.42°N, 116.02°E)的高精度钠荧光共振激光雷达的数据,根据观测到的热层钠原子层的形态特征和出现规律等以及参考先前的研究报道,将该台站上空的热层钠层主要归类为四种：低热层突发钠层、天亮前热层-电离层钠层、午夜热层-电离层钠层和中纬度热层-电离层钠层.我们对最后一种热层钠层进行了仔细研究,基于2018—2020年415个观测夜共约3914 h的数据,找到了17个该事件(出现率仅4.1%,且多发于冬季).在14个完整事件中,仅约35.7%(5/14)的事件出现时间与附近地基台站观测到的电离层突发E层相似,但均早于电离层突发E层;剩下的9次事件与最近的突发E层的时间相差范围为2.5～8.6 h.因此,我们认为中纬度热层-电离层钠层与电离层突发E层相关性较弱,它应该有着其他可能的形成机制. 相似文献
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由于测量与计算的难度,对热层垂直中性风的观测还很不够,这影响了人们对热层及热层-电离层耦合的认识.本文基于全天空法布里-珀罗干涉仪(FPI)对热层风场的观测,提出了一种反演垂直中性风的方法.利用该方法,对北极黄河站全天空FPI观测数据进行了垂直中性风的反演计算,结果表明,高热层与低热层的垂直风平均幅值分别在40 m·s-1和15 m·s-1,且垂直风日变化表现出明显的时间演变特性,且与地磁ap指数的变化有一定的相关性,在地磁活动强烈时,低热层垂直风会出现高达100 m·s-1的扰动,高热层甚至会达到300 m·s-1的扰动,这些特征与其他学者的观测结果相一致.本文方法不需要假设垂直风均值为零,也不用限制FPI的观测方位,可用于垂直风的反演. 相似文献
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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
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本文将半参数平差模型引入电离层球谐函数系数的预报中,建立了半参数球谐函数模型(Semiparametric-Spherical Harmonic,Semi-SH)来预测全球电离层总电子含量.首先,通过快速傅里叶变换获得球谐函数系数的周期和振幅,将振幅高的主周期归入趋势函数,振幅低的剩余周期归入随机信号,建立了半参数模型,同时利用核估计方法拟合趋势函数,解算随机信号,并在时间域上进行外推,得到了预报时间的球谐函数系数,代入15阶电离层球谐函数模型,最后得出电离层总电子含量(Total Electron Content,TEC)的预报值.本文基于欧洲定轨中心(CODE)发布的球谐函数系数进行电离层TEC长期预报和短期预报分析,其中长期预报采用四年预报两年的模式对球谐函数系数进行预报,短期预报设计了三个算例,采用前30天预报后一天的模式,分别预报1天、滑动预报7天和滑动预报30天.实验结果表明:长期预报能够较好地反映全球电离层TEC的变化趋势和波动情况,Semi-SH模型对全球电离层TEC平均值(Mean TEC global,MTECglobal)的拟合值和预报值与MTECglobal实际值的相关系数分别为0.8743和0.8010,呈现出高度相关性.短期预报中,在太阳活动高年和太阳活动低年,Semi-SH模型在中纬度地区预报精度较CODE发布的电离层TEC 1天预报产品(CODE′S 1-Day Predicted GIM,C1PG)有较大提升,在高纬度与低纬度地区两种模型预报精度相当;Semi-SH模型在太阳活动高年和太阳活动低年30天滑动预报精度的均值均高于C1PG模型.实验结果说明了Semi-SH模型预报电离层TEC值的有效性. 相似文献
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The local ensemble transform Kalman filter (LETKF) is implemented with the Weather Research and Forecasting (WRF) model, and
real observations are assimilated to assess the newly-developed WRF-LETKF system. The WRF model is a widely-used mesoscale
numerical weather prediction model, and the LETKF is an ensemble Kalman filter (EnKF) algorithm particularly efficient in
parallel computer architecture. This study aims to provide the basis of future research on mesoscale data assimilation using
the WRF-LETKF system, an additional testbed to the existing EnKF systems with the WRF model used in the previous studies.
The particular LETKF system adopted in this study is based on the system initially developed in 2004 and has been continuously
improved through theoretical studies and wide applications to many kinds of dynamical models including realistic geophysical
models. Most recent and important improvements include an adaptive covariance inflation scheme which considers the spatial
and temporal inhomogeneity of inflation parameters. Experiments show that the LETKF successfully assimilates real observations
and that adaptive inflation is advantageous. Additional experiments with various ensemble sizes show that using more ensemble
members improves the analyses consistently. 相似文献
12.
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
13.
The ensemble Kalman filter (EnKF) has gained popularity in hydrological data assimilation problems. As a Monte Carlo based method, a sufficiently large ensemble size is usually required to guarantee the accuracy. As an alternative approach, the probabilistic collocation based Kalman filter (PCKF) employs the polynomial chaos expansion (PCE) to represent and propagate the uncertainties in parameters and states. However, PCKF suffers from the so-called “curse of dimensionality”. Its computational cost increases drastically with the increasing number of parameters and system nonlinearity. Furthermore, PCKF may fail to provide accurate estimations due to the joint updating scheme for strongly nonlinear models. Motivated by recent developments in uncertainty quantification and EnKF, we propose a restart adaptive probabilistic collocation based Kalman filter (RAPCKF) for data assimilation in unsaturated flow problems. During the implementation of RAPCKF, the important parameters are identified and active PCE basis functions are adaptively selected at each assimilation step; the “restart” scheme is utilized to eliminate the inconsistency between updated model parameters and states variables. The performance of RAPCKF is systematically tested with numerical cases of unsaturated flow models. It is shown that the adaptive approach and restart scheme can significantly improve the performance of PCKF. Moreover, RAPCKF has been demonstrated to be more efficient than EnKF with the same computational cost. 相似文献
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The application of interferometric synthetic aperture radar (InSAR) has been increasingly used to improve capabilities to model land subsidence in hydrogeologic studies. A number of investigations over the last decade show how spatially detailed time‐lapse images of ground displacements could be utilized to advance our understanding for better predictions. In this work, we use simulated land subsidences as observed measurements, mimicking InSAR data to inversely infer inelastic specific storage in a stochastic framework. The inelastic specific storage is assumed as a random variable and modeled using a geostatistical method such that the detailed variations in space could be represented and also that the uncertainties of both characterization of specific storage and prediction of land subsidence can be assessed. The ensemble Kalman filter (EnKF), a real‐time data assimilation algorithm, is used to inversely calibrate a land subsidence model by matching simulated subsidences with InSAR data. The performance of the EnKF is demonstrated in a synthetic example in which simulated surface deformations using a reference field are assumed as InSAR data for inverse modeling. The results indicate: (1) the EnKF can be used successfully to calibrate a land subsidence model with InSAR data; the estimation of inelastic specific storage is improved, and uncertainty of prediction is reduced, when all the data are accounted for; and (2) if the same ensemble is used to estimate Kalman gain, the analysis errors could cause filter divergence; thus, it is essential to include localization in the EnKF for InSAR data assimilation. 相似文献
15.
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
16.
The Kalman filter is an efficient data assimilation tool to refine an estimate of a state variable using measured data and the variable's correlations in space and/or time. The ensemble Kalman filter (EnKF) (Evensen 2004, 2009) is a Kalman filter variant that employs Monte Carlo analysis to define the correlations that help to refine the updated state. While use of EnKF in hydrology is somewhat limited, it has been successfully applied in other fields of engineering (e.g., oil reservoir modeling, weather forecasting). Here, EnKF is used to refine a simulated groundwater tetrachloroethylene (TCE) plume that underlies the Tooele Army Depot‐North (TEAD‐N) in Utah, based on observations of TCE in the aquifer. The resulting EnKF‐based assimilated plume is simulated forward in time to predict future plume migration. The correlations that underpin EnKF updating implicitly contain information about how the plume developed over time under the influence of complex site hydrology and variable source history, as they are predicated on multiple realizations of a well‐calibrated numerical groundwater flow and transport model. The EnKF methodology is compared to an ordinary kriging‐based assimilation method with respect to the accurate representation of plume concentrations in order to determine the relative efficacy of EnKF for water quality data assimilation. 相似文献
17.
Man Jun Zheng Qiang Wu Laosheng Zeng Lingzao 《Stochastic Environmental Research and Risk Assessment (SERRA)》2020,34(8):1135-1146
Stochastic Environmental Research and Risk Assessment - The ensemble Kalman filter (EnKF) has received substantial attention in hydrologic data assimilation due to its ease of implementation. In... 相似文献
18.
This paper, based on a real world case study (Limmat aquifer, Switzerland), compares inverse groundwater flow models calibrated with specified numbers of monitoring head locations. These models are updated in real time with the ensemble Kalman filter (EnKF) and the prediction improvement is assessed in relation to the amount of monitoring locations used for calibration and updating. The prediction errors of the models calibrated in transient state are smaller if the amount of monitoring locations used for the calibration is larger. For highly dynamic groundwater flow systems a transient calibration is recommended as a model calibrated in steady state can lead to worse results than a noncalibrated model with a well-chosen uniform conductivity. The model predictions can be improved further with the assimilation of new measurement data from on-line sensors with the EnKF. Within all the studied models the reduction of 1-day hydraulic head prediction error (in terms of mean absolute error [MAE]) with EnKF lies between 31% (assimilation of head data from 5 locations) and 72% (assimilation of head data from 85 locations). The largest prediction improvements are expected for models that were calibrated with only a limited amount of historical information. It is worthwhile to update the model even with few monitoring locations as it seems that the error reduction with EnKF decreases exponentially with the amount of monitoring locations used. These results prove the feasibility of data assimilation with EnKF also for a real world case and show that improved predictions of groundwater levels can be obtained. 相似文献
19.
Based on the thermosphere-Ionosphere-Electrodynamics General Circulation Model (TIEGCM), a thermospheric-ionospheric data assimilation and forecast system is developed. Using this system, we estimated the oxygen ions, neutral temperature, wind, and composition by assimilating the simulated data from Formosa Satellite 3/Constellation Observing System for Meteorology, Ionosphere, and Climate (COSMIC) occultation electron density profiles to evaluate their effects on the ionospheric forecast. An ensemble Kalman filter data assimilation scheme and combined state and parameter estimation methods are used to estimate the unobserved parameters in the model. The statistical results show that the neutral and ion compositions are more effective than the neutral temperature and wind for improving the forecast of the ionospheric electron density, whose root mean square errors in the assimilation period decreased by approximately 40%, 30%, and 10% due to the estimations of the neutral composition, oxygen ions, and neutral temperature, respectively. Due to the different physical and chemical processes that these parameters primarily affect, their e-folding times differ greatly from longer than 12 h for neutral composition to approximately 6 h for oxygen ions and 3 h for neutral temperature. The effect of estimating the neutral composition on improving the ionospheric forecast is greater than that of estimating the oxygen ions, which can be also be seen in an actual data assimilation experiment. This indicates that the neutral composition is the most important thermospheric parameter in ionospheric data assimilations and forecasts. 相似文献