Recursive residuals and model diagnostics for normal and non-normal state space models

Model diagnostics for normal and non-normal state space models are based on recursive residuals which are defined from the one-step ahead predictive distribution. Routine calculation of these residuals is discussed in detail. Various diagnostic tools are suggested to check, for example, for wrong observation distributions and for autocorrelation. The paper also discusses such topics as model diagnostics for discrete time series and model discrimination via Bayes factors. The case studies cover environmental applications such as analysing a time series of the number of daily rainfall occurrences and a time series of daily sulfur dioxide emissions.     

非理想环境下加速度计信号的混合滤波

简要分析了舰艇在非理想环境下航行时,常用的加速度计数据处理方法,介绍了一种新型基于线性和非线性的混合滤波器。通过MATLAB仿真,结果表明这种混合滤波器对加速度计信号具有较好的滤波效果。     

A COMPARATIVE STUDY OF LINEAR AND NONLINEAR TIME SERIES MODELS FOR WATER QUALITY1

ABSTRACT: Surface water quality data are routinely collected in river basins by state or federal agencies. The observed quality of river water generally reflects the overall quality of the ecosystem of the river basin. Advanced statistical methods are often needed to extract valuable information from the vast amount of data for developing management strategies. Among the measured water quality constituents, total phosphorus is most often the limiting nutrient in freshwater aquatic systems. Relatively low concentrations of phosphorus in surface waters may create eutrophication problems. Phosphorus is a non-conservative constituent. Its time series generally exhibits nonlinear behavior. Linear models are shown to be inadequate. This paper presents a nonlinear state-dependent model for the phosphorous data collected at DeSoto, Kansas. The nonlinear model gives significant reductions in error variance and forecasting error as compared to the best linear autoregressive model identified.     

简化活性污泥数学模型的优化

为了从控制运行的角度促进简化数学模型的应用，必须借助其它优化手段进一步提高预测精度。本文主要介绍了应用于简化数学模型的扩展卡尔曼滤波、神经网络和误差反馈系统三种优化方法以及优化后的验证结果。从验证结果来看，优化的ROM模型与ASMl模型的定性变化行为相似，基于神经网络的复合模型对PO4^3-和NOx^-的预测结果非常准确，误差反馈的优化系统模拟性能良好。     

抗差自适应Kalman滤波在动态变形监测中的应用研究

抗差自适应Kalman滤波算法中,抗差等价权矩阵和自适应因子的计算,要求观测信息具有多余观测量且准确可靠,但在动态变形监测应用中,通常滤波观测值仅为三维坐标且存在较强噪声和粗差的影响.为此,先对该算法中的自适应因子和抗差等价权矩阵的计算进行研究和改进,然后计算了某高速公路边坡的GPS动态监测数据.结果表明,抗差自适应K...     

A meteorologically adjusted ensemble Kalman filter approach for inversing daily emissions: A case study in the Pearl River Delta, China

The conventional Ensemble Kalman filter (EnKF), which is now widely used to calibrate emission inventories and to improve air quality simulations, is susceptible to simulation errors of meteorological inputs, making accurate updates of high temporal-resolution emission inventories challenging. In this study, we developed a novel meteorologically adjusted inversion method (MAEInv) based on the EnKF to improve daily emission estimations. The new method combines sensitivity analysis and bias correction to alleviate the inversion biases caused by errors of meteorological inputs. For demonstration, we used the MAEInv to inverse daily carbon monoxide (CO) emissions in the Pearl River Delta (PRD) region, China. In the case study, 60% of the total CO simulation biases were associated with sensitive meteorological inputs, which would lead to the overestimation of daily variations of posterior emissions. Using the new inversion method, daily variations of emissions shrank dramatically, with the percentage change decreased by 30%. Also, the total amount of posterior CO emissions estimated by the MAEInv decreased by 14%, indicating that posterior CO emissions might be overestimated using the conventional EnKF. Model evaluations using independent observations revealed that daily CO emissions estimated by MAEInv better reproduce the magnitude and temporal patterns of ambient CO concentration, with a higher correlation coefficient (R, +37.0%) and lower normalized mean bias (NMB, -17.9%). Since errors of meteorological inputs are major sources of simulation biases for both low-reactive and reactive pollutants, the MAEInv is also applicable to improve the daily emission inversions of reactive pollutants.     

Soil Moisture Data Assimilation Using Support Vector Machines and Ensemble Kalman Filter1

Abstract: A hybrid data assimilation (DA) methodology that combines two state‐of‐the‐art techniques, support vector machines (SVMs) and ensemble Kalman filter (EnKF), is applied for soil moisture DA in this work. The SVM methodology provides a statistically sound and robust approach to solving the inverse problem, and thus to building statistical models. EnKF is an extension of the Kalman Filter (KF), a well‐known tool in prediction updating. In the present research, ground measurements were used to build a SVM‐type soil moisture predictor. Subsequent observations and their statistics were assimilated to update predictions from the SVM model by coupling it with EnKF. In this way, both model predictions and ground data, as well as their statistics, are fused thus minimizing the prediction error and making the predictions and observations statistically consistent. The results are shown for two approaches; one in which update is done at every time step and the other which assumes that data is only available at alternate time steps (in window of 10 time steps) and hence update is performed at those occasions. The SVM‐EnKF coupling is shown to improve soil moisture forecasts in an example using data from the Soil Climate Analysis Network site at Ames, Iowa.     

基于多速率卡尔曼滤波方法的位移和加速度数据融合

为了尽可能地利用加速度传感器和位移传感器中的冗余信息、提高振动实测数据的精度,将结构同一位置的加速度和位移观测值进行数据融合处理,提出了一种基于维纳过程加速度模型的多速率卡尔曼滤波数据融合方法。首先给出了基于维纳过程的多速率卡尔曼滤波数融合模型;然后在加速度和位移观测值的基础上,通过多速率卡尔曼滤波数据融合得到位移、速度以及加速度的的最优估计,并对融合结果进行卡尔曼平滑处理,进一步提高了状态估计准确性;最后数值分析了不同噪声水平和采样频率比对融合算法的影响,而且与相关文献的结果作了对比分析。结果表明,本文方法合理有效且具有一定的噪声鲁棒性。     

基于集合卡尔曼滤波的区域臭氧资料同化试验

基于集合卡尔曼滤波方法和嵌套网格空气质量模式系统建立了一个区域空气质量资料同化系统(RAQDAS),并利用该系统开展了京津冀地区2008年北京奥运会期间的地面臭氧观测资料同化试验,分析了同化系统订正臭氧初始场对24 h臭氧预报的影响.试验结果表明采用50个集合样本的集合卡尔曼滤波同化不仅改进了观测站点的臭氧预报,也提高了观测以外区域的臭氧预报技巧,使得臭氧预报的均方根误差平均下降了15％,并且当集合样本数减小到20时也可达到相近的预报改进效果.为了解决滤波发散问题,分别采用了放大集合离散度和扰动模式误差源两种方法.其中放大集合离散度能避免滤波发散,但没有提高臭氧预报技巧,反而导致预报误差的增加;扰动模式误差源不仅解决了滤波发散问题,也使同化导致的臭氧预报均方根误差下降比例从15％进一步提高到20％.