Using a H∞ filter assimilation procedure to estimate root zone soil water content

Root zone soil water content impacts plant water availability, land energy and water balances. Because of unknown hydrological model error, observation errors and the statistical characteristics of the errors, the widely used Kalman filter (KF) and its extensions are challenged to retrieve the root zone soil water content using the surface soil water content. If the soil hydraulic parameters are poorly estimated, the KF and its extensions fail to accurately estimate the root zone soil water. The H‐infinity filter (HF) represents a robust version of the KF. The HF is widely used in data assimilation and is superior to the KF, especially when the performance of the model is not well understood. The objective of this study is to study the impact of uncertain soil hydraulic parameters, initial soil moisture content and observation period on the ability of HF assimilation to predict in situ soil water content. In this article, we study seven cases. The results show that the soil hydraulic parameters hold a critical role in the course of assimilation. When the soil hydraulic parameters are poorly estimated, an accurate estimation of root soil water content cannot be retrieved by the HF assimilation approach. When the estimated soil hydraulic parameters are similar to actual values, the soil water content at various depths can be accurately retrieved by the HF assimilation. The HF assimilation is not very sensitive to the initial soil water content, and the impact of the initial soil water content on the assimilation scheme can be eliminated after about 5–7 days. The observation interval is important for soil water profile distribution retrieval with the HF, and the shorter the observation interval, the shorter the time required to achieve actual soil water content. However, the retrieval results are not very accurate at a depth of 100 cm. Also it is complex to determine the weighting coefficient and the error attenuation parameter in the HF assimilation. In this article, the trial‐and‐error method was used to determine the weighting coefficient and the error attenuation parameter. After the first establishment of limited range of the parameters, ‘the best parameter set’ was selected from the range of values. For the soil conditions investigated, the HF assimilation results are better than the open‐loop results. Copyright © 2010 John Wiley & Sons, Ltd.     

BDS/GPS/GLONASS RTK定位算法研究

实现了BDS/GPS/GLONASS三系统组合RTK定位算法,介绍了BDS/GPS/GLONASS三系统组合RTK数学模型,解决了多模融合导航定位时空基准统一问题,并针对附加模糊度参数的卡尔曼滤波函数模型,提出了一种确定实时动态定位中卡尔曼滤波参数的方法。编制了BDS/GPS/GLONASS RTK定位程序,并对28 m超短基线及31 km短基线实测数据进行了解算。对比分析了BDS、GPS、GLONASS、BDS/GPS、BDS/GLONASS、GPS/GLONASS、BDS/GPS/GLONASS七种模式下的定位结果。     

南宁膨胀土持水性能的温度效应及微观机制

采用蒸汽平衡法测定了4种温度(T=5、25、40、60℃)条件下Whatman No.42滤纸的率定曲线,建立了考虑温度影响的双线性率定曲线方程,发现滤纸的持水性能随温度升高而降低,且温度对率定曲线高吸力段滤纸持水性能的影响要弱于低吸力段。在此基础上,以南宁膨胀土为研究对象,使用滤纸法测定了不同温度作用下膨胀土试样的土-水特征曲线。试验结果表明,随着温度的升高,膨胀土持水性能下降,但温度的影响幅度会随着吸力的增大而减弱,当基质吸力达到40MPa时,不同温度下试样的体积含水率并没有明显变化,即此时温度对膨胀土的持水性能变化几乎无影响。为解释上述宏观现象,选取部分试样进行了压汞试验和吸附结合水试验,并根据试验结果从土体中各相及各相界面相互耦合作用的物理机制角度阐述了南宁膨胀土持水性能的温度效应及微观机制。     

考虑动态泥膜效应的盾构开挖面稳定理论研究

泥水盾构工法被广泛应用于高压富水复杂地质环境中修筑隧道,其中开挖面稳定控制是工程成败的关键,在盾构掘进时,由于刀盘不断旋转切削地层,开挖面上的泥膜处于“形成―破坏―再形成”的动态循环过程,泥膜透水性增强,这种动态泥膜作用下开挖面的稳定如何评价有待于进一步探索。对此,对泥水盾构掘进过程中的动态泥膜进行了分类,提出了考虑盾构运动和泥浆渗滤特征的动态泥膜理论。在此基础上,采用空间离散法将泥水压力引入旋转体模型,建立了考虑动态泥膜效应的开挖面稳定力学模型,提出了泥水盾构动态掘进过程中开挖面临界泥浆压力及泥浆支护效率评估方法,研究指出：当盾构在强渗透性地层中掘进时,在尽量提高泥浆成膜率的同时,应注重泥浆黏度调节,充分发挥渗透力支护作用,同时可适当提高盾构掘进速度并降低刀盘转速,有利于开挖面稳定控制。以泥浆成膜率、等效渗透系数为依据给出了泥浆支护效率设计图,研究工作对盾构掘进支护压力控制和泥浆配置有一定的借鉴意义。     

顾及时空特性的SBAS高质量点选取算法

SBAS技术中常用的相干系数选点方法受旁瓣效应的影响,容易造成部分高质量点的漏选以及低质量点的误选。针对这一问题,本文提出了顾及时空特性的SBAS高质量点选取算法,根据干涉相位各个成分的空间特性,分离出噪声相位判别点的质量。试验表明考虑同质点的Non-Local滤波能够更加可靠地提取干涉相位中的空间相关相位,从而提高噪声相位估计的准确性。利用覆盖上海地区1992—1998年的24景JERS-1影像作为试验数据,分别用相干系数法以及本文的方法进行选点。结果表明本文算法能够有效地选出相干系数法在农田与村落交错地区漏选的高质量点,同时排除了相干系数法由于旁瓣效应影响的误选点。     

震前电离层TEC异常探测方法的研究进展

介绍了震前电离层TEC异常探测原理的研究进展和主要的异常探测方法,详细介绍了时间序列法、Kalman滤波和小波变换在电离层异常探测中的原理和应用,总结了传统方法和新方法在电离层异常探测的应用发展情况,并分析了每种方法的优势与不足。为未来中国采用电离层异常探测开展地震预测工作提出了建议。     

信息分配系数的自适应滤波在GPS/DR组合导航中的应用

在组合导航中常采用联合滤波器进行多源传感器的信息融合,其中信息分配系数的取值直接影响着联合滤波器的性能。在详细分析子滤波器状态变量估计协方差阵特点的基础上,文中提出了采用两组信息分配系数的自适应联合滤波模型,并将设计的自适应联合滤波模型应用于车载GPS/DR组合导航中。仿真实验表明,导航的位置精度提高约为40%;速度精度提高约为10%。同时也提高了组合系统的容错性和可靠性。     

各向异性组合滤波法反演陆地水储量变化

地球时变重力场模型反演陆地水储量变化已为全球气候变化研究作出巨大贡献,考虑到时变重力场模型球谐系数中存在相关性,其高阶次项具有较大的误差,需采用最优的滤波方法进行空间平滑。本文提出一种新的各向异性组合滤波方法,其基本思想是将改进的高斯滤波法与均方根(root mean square,RMS)滤波法组合,即对球谐系数的低阶次采用改进的高斯滤波法,而高阶次采用RMS滤波法。首先分析了最新的GRACE RL05系列时变重力场模型系数误差特性,基于全球水储量变化反演结果,分析比较了高斯滤波、改进的高斯滤波、RMS滤波和DDK滤波与本文提出的组合滤波法的有效性及精度,并利用模型结果进行了验证,计算结果表明,组合滤波法的中误差最小。研究结果表明,本文提出的组合法相比于先前的滤波方法,可有效地过滤高阶次的噪声,消除南北条带误差,同时减少信号泄漏,提高信噪比,保留更多有效的地球物理信号,进而提高反演精度。     

Seismic random noise attenuation and signal-preserving by multiple directional time-frequency peak filtering

Time-frequency peak filtering (TFPF) is an effective method for seismic random noise attenuation. The linearity of the signal has a significant influence on the accuracy of the TFPF method. The higher the linearity of the signal to be filtered is, the better the denoising result is. With this in mind, and taking the lateral coherence of reflected events into account, we do TFPF along the reflected events to improve the degree of linearity and enhance the continuity of these events. The key factor to realize this idea is to find the traces of the reflected events. However, the traces of the events are too hard to obtain in the complicated field seismic data. In this paper, we propose a Multiple Directional TFPF (MD–TFPF), in which the filtering is performed in certain direction components of the seismic data. These components are obtained by a directional filter bank. In each direction component, we do TFPF along these decomposed reflected events (the local direction of the events) instead of the channel direction. The final result is achieved by adding up the filtering results of all decomposition directions of seismic data. In this way, filtering along the reflected events is implemented without accurately finding the directions. The effectiveness of the proposed method is tested on synthetic and field seismic data. The experimental results demonstrate that MD–TFPF can more effectively eliminate random noise and enhance the continuity of the reflected events with better preservation than the conventional TFPF, curvelet denoising method and F–X deconvolution method.     

Assimilating Best Track Minimum Sea Level Pressure Data Together with Doppler Radar Data Using an Ensemble Kalman Filter for Hurricane Ike (2008) at a Cloud-Resolving Resolution

Extending an earlier study, the best track minimum sea level pressure (MSLP) data are assimilated for landfalling Hurricane Ike (2008) using an ensemble Kalman filter (EnKF), in addition to data from two coastal ground-based Doppler radars, at a 4-km grid spacing. Treated as a sea level pressure observation, the MSLP assimilation by the EnKF enhances the hurricane warm core structure and results in a stronger and deeper analyzed vortex than that in the GFS (Global Forecast System) analysis; it also improves the subsequent 18-h hurricane intensity and track forecasts. With a 2-h total assimilation window length, the assimilation of MSLP data interpolated to 10-min intervals results in more balanced analyses with smaller subsequent forecast error growth and better intensity and track forecasts than when the data are assimilated every 60 minutes. Radar data are always assimilated at 10-min intervals. For both intensity and track forecasts, assimilating MSLP only outperforms assimilating radar reflectivity (Z) only. For intensity forecast, assimilating MSLP at 10-min intervals outperforms radar radial wind (Vr) data (assimilated at 10-min intervals), but assimilating MSLP at 60-min intervals fails to beat Vr data. For track forecast, MSLP assimilation has a slightly (noticeably) larger positive impact than Vr(Z) data. When Vr or Z is combined with MSLP, both intensity and track forecasts are improved more than the assimilation of individual observation type. When the total assimilation window length is reduced to 1h or less, the assimilation of MSLP alone even at 10-min intervals produces poorer 18-h intensity forecasts than assimilating Vr only, indicating that many assimilation cycles are needed to establish balanced analyses when MSLP data alone are assimilated; this is due to the very limited pieces of information that MSLP data provide.