An improved four-dimensional variation source term inversion model with observation error regularization

Aiming at the Four-Dimensional Variation source term inversion algorithm proposed earlier,the observation error regularization factor is introduced to improve the prediction accuracy of the diffusion model,and an improved Four-Dimensional Variation source term inversion algorithm with observation error regularization(OER-4DVAR STI model)is formed.Firstly,by constructing the inversion process and basic model of OER-4DVAR STI model,its basic principle and logical structure are studied.Secondly,the observation error regularization factor estimation method based on Bayesian optimization is proposed,and the error factor is separated and optimized by two parameters:error statistical time and deviation degree.Finally,the scientific,feasible and advanced nature of the OER-4DVAR STI model are verified by numerical simulation and tracer test data.The experimental results show that OER-4DVAR STI model can better reverse calculate the hazard source term information under the conditions of high atmospheric stability and flat underlying surface.Compared with the previous inversion algorithm,the source intensity estimation accuracy of OER-4DVAR STI model is improved by about 46.97%,and the source location estimation accuracy is improved by about 26.72%.

ENSO Predictions in an Intermediate Coupled Model Influenced by Removing Initial Condition Errors in Sensitive Areas： A Target Observation Perspective

Previous studies indicate that ENSO predictions are particularly sensitive to the initial conditions in some key areas（socalled ＂sensitive areas＂）. And yet, few studies have quantified improvements in prediction skill in the context of an optimal observing system. In this study, the impact on prediction skill is explored using an intermediate coupled model in which errors in initial conditions formed to make ENSO predictions are removed in certain areas. Based on ideal observing system simulation experiments, the importance of various observational networks on improvement of El Ni n？o prediction skill is examined. The results indicate that the initial states in the central and eastern equatorial Pacific are important to improve El Ni n？o prediction skill effectively. When removing the initial condition errors in the central equatorial Pacific, ENSO prediction errors can be reduced by 25%. Furthermore, combinations of various subregions are considered to demonstrate the efficiency on ENSO prediction skill. Particularly, seasonally varying observational networks are suggested to improve the prediction skill more effectively. For example, in addition to observing in the central equatorial Pacific and its north throughout the year,increasing observations in the eastern equatorial Pacific during April to October is crucially important, which can improve the prediction accuracy by 62%. These results also demonstrate the effectiveness of the conditional nonlinear optimal perturbation approach on detecting sensitive areas for target observations.

Assimilating temperature and salinity profiles using Ensemble Kalman Filter with an adaptive observation error and T-S constraint

Temperature （T） and salinity （S） profiles from conductivity-temperature-depth data collected during the Northern South China Sea Open Cruise from August 16 to September 13, 2008 are assimilated using Ensemble Kalman Filter （EnKF）. An adaptive observational error strategy is used to prevent filter from diverging. In the meantime, aiming at the limited improvement in some sites caused by the T and S biases in the model, a T-S constraint scheme is adopted to improve the assimilation performance, where T and S are separately updated at these locations. Validation is performed by comparing assimilated outputs with independent in situ data （satellite remote sensing sea level anomaly （SLA）, the OSCAR velocity product and shipboard ADCP）. The results show that the new EnKF assimilation scheme can significantly reduce the root mean square error （RMSE） of oceanic T and S compared with the control run and traditional EnKF. The system can also improve the simulation of circulations and SLA.

Pattern-Moving-Based Parameter Identification of Output Error Models with Multi-Threshold Quantized Observations

This paper addresses a modified auxiliary model stochastic gradient recursive parameter identification algorithm(M-AM-SGRPIA)for a class of single input single output(SISO)linear output error models with multi-threshold quantized observations.It proves the convergence of the designed algorithm.A pattern-moving-based system dynamics description method with hybrid metrics is proposed for a kind of practical single input multiple output(SIMO)or SISO nonlinear systems,and a SISO linear output error model with multi-threshold quantized observations is adopted to approximate the unknown system.The system input design is accomplished using the measurement technology of random repeatability test,and the probabilistic characteristic of the explicit metric value is employed to estimate the implicit metric value of the pattern class variable.A modified auxiliary model stochastic gradient recursive algorithm(M-AM-SGRA)is designed to identify the model parameters,and the contraction mapping principle proves its convergence.Two numerical examples are given to demonstrate the feasibility and effectiveness of the achieved identification algorithm.

Wind Speed and Altitude Dependent AMDAR Observational Error and Its Impacts on Data Assimilation and Forecasting

Aircraft Meteorological Data Relay(AMDAR)observations have been widely used in numerical weather prediction(NWP)because of its high spatiotemporal resolution.The observational error of AMDAR is influenced by aircraft flight altitude and atmospheric condition.In this study,the wind speed and altitude dependent observational error of AMDAR is estimated.The statistical results show that the temperature and the observational error in wind speeds slightly decrease as altitude increases,and the observational error in wind speed increases as wind speed increases.Pseudo single AMDAR observation assimilation tests demonstrate that the wind speed and altitude dependent observational error can provide more reasonable analysis increment.Furthermore,to assess the performance of wind speed and altitude dependent observational error on data assimilation and forecasting,two-month 3-hourly cycling data assimilation and forecast experiments based on the Weather Research and Forecasting Model(WRF)and its Data Assimilation system(WRFDA)are performed for the period during 1 September-31 October,2017.The results of the two-month 3-hourly cycling experiments indicate that new observational error improves analysis and forecast of wind field and geo-potential height,and has slight improvements on temperature.The Fractions Skill Score(FSS)of the 6-h accumulated precipitation shows that new wind speed and altitude dependent observational error leads to better precipitation forecast skill than the default observational error in the WRFDA does.

Estimating the Correlated Observation-Error Characteristics of the Chinese FengYun Microwave Temperature Sounder and Microwave Humidity Sounder

In operational data assimilation systems, observation-error covariance matrices are commonly assumed to be diagonal.However, inter-channel and spatial observation-error correlations are inevitable for satellite radiances. The observation errors of the Microwave Temperature Sounder(MWTS) and Microwave Humidity Sounder(MWHS) onboard the FengYun-3A(FY-3A) and FY-3B satellites are empirically assigned and considered to be uncorrelated when they are assimilated into the WRF model's Community Variational Data Assimilation System(WRFDA). To assimilate MWTS and MWHS measurements optimally, a good characterization of their observation errors is necessary. In this study, background and analysis residuals were used to diagnose the correlated observation-error characteristics of the MWTS and MWHS. It was found that the error standard deviations of the MWTS and MWHS were less than the values used in the WRFDA. MWTS had small inter-channel errors, while MWHS had significant inter-channel errors. The horizontal correlation length scales of MWTS and MWHS were about 120 and 60 km, respectively. A comparison between the diagnosis for instruments onboard the two satellites showed that the observation-error characteristics of the MWTS or MWHS were different when they were onboard different satellites. In addition, it was found that the error statistics were dependent on latitude and scan positions.The forecast experiments showed that using a modified thinning scheme based on diagnosed statistics can improve forecast accuracy.

The Initial Errors in the Tropical Indian Ocean that Can Induce a Significant “Spring Predictability Barrier” for La Nina Events and Their Implication for Targeted Observations

Initial errors in the tropical Indian Ocean(IO-related initial errors) that are most likely to yield the Spring Prediction Barrier(SPB) for La Ni?a forecasts are explored by using the CESM model.These initial errors can be classified into two types.Type-1 initial error consists of positive sea temperature errors in the western Indian Ocean and negative sea temperature errors in the eastern Indian Ocean,while the spatial structure of Type-2 initial error is nearly opposite.Both kinds of IO-related initial errors induce positive prediction errors of sea temperature in the Pacific Ocean,leading to underprediction of La Nina events.Type-1 initial error in the tropical Indian Ocean mainly influences the SSTA in the tropical Pacific Ocean via atmospheric bridge,leading to the development of localized sea temperature errors in the eastern Pacific Ocean.However,for Type-2 initial error,its positive sea temperature errors in the eastern Indian Ocean can induce downwelling error and influence La Ni?a predictions through an oceanic channel called Indonesian Throughflow.Based on the location of largest SPB-related initial errors,the sensitive area in the tropical Indian Ocean for La Nina predictions is identified.Furthermore,sensitivity experiments show that applying targeted observations in this sensitive area is very useful in decreasing prediction errors of La Nina.Therefore,adopting a targeted observation strategy in the tropical Indian Ocean is a promising approach toward increasing ENSO prediction skill.

A Modified Regression Estimator for Single Phase Sampling in the Presence of Observational Errors

In this paper, a regression method of estimation has been used to derive the mean estimate of the survey variable using simple random sampling without replacement in the presence of observational errors. Two covariates were used and a case where the observational errors were in both the survey variable and the covariates was considered. The inclusion of observational errors was due to the fact that data collected through surveys are often not free from errors that occur during observation. These errors can occur due to over-reporting, under-reporting, memory failure by the respondents or use of imprecise tools of data collection. The expression of mean squared error (MSE) based on the obtained estimator has been derived to the first degree of approximation. The results of a simulation study show that the derived modified regression mean estimator under observational errors is more efficient than the mean per unit estimator and some other existing estimators. The proposed estimator can therefore be used in estimating a finite population mean, while considering observational errors that may occur during a study.

Stochastic convergence analysis of cubature Kalman filter with intermittent observations

The stochastic convergence of the cubature Kalmanfilter with intermittent observations （CKFI） for general nonlinearstochastic systems is investigated. The Bernoulli distributed ran-dom variable is employed to describe the phenomenon of intermit-tent observations. According to the cubature sample principle, theestimation error and the error covariance matrix （ECM） of CKFIare derived by Taylor series expansion, respectively. Afterwards, itis theoretically proved that the ECM will be bounded if the obser-vation arrival probability exceeds a critical minimum observationarrival probability. Meanwhile, under proper assumption corre-sponding with real engineering situations, the stochastic stabilityof the estimation error can be guaranteed when the initial estima-tion error and the stochastic noise terms are sufficiently small. Thetheoretical conclusions are verified by numerical simulations fortwo illustrative examples; also by evaluating the tracking perfor-mance of the optical-electric target tracking system implementedby CKFI and unscented Kalman filter with intermittent observa-tions （UKFI） separately, it is demonstrated that the proposed CKFIslightly outperforms the UKFI with respect to tracking accuracy aswell as real time performance.

Using self-location to calibrate the errors of observer positions for source localization

The uncertainty of observers＇ positions can lead to significantly degrading in source localization accuracy. This pa-per proposes a method of using self-location for calibrating the positions of observer stations in source localization to reduce the errors of the observer positions and improve the accuracy of the source localization. The relative distance measurements of the two coordinative observers are used for the linear minimum mean square error （LMMSE） estimator. The results of computer si-mulations prove the feasibility and effectiveness of the proposed method. With the general estimation errors of observers＇ positions, the MSE of the source localization with self-location calibration, which is significantly lower than that without self-location calibra-tion, is approximating to the Cramer-Rao lower bound （CRLB）.

ASYMPTOTIC NORMALITY OF PARAMETERSESTIMATION IN EV MODEL WITH REPLICATEDOBSERVATIONS

This paper based on the essay [1], studies in case that replicated observations are available in some experimental points., the parameters estimation of one dimensional linear errors-in-variables (EV) models. Asymptotic normality is established.

On the sensitive areas for targeted observations in ENSO forecasting

Using the outputs from CMCC-CM in CMIP5 experiments,the authors identified sensitive areas for targeted observations in ENSO forecasting from the perspective of the initial error growth(IEG)method and the particle filter(PF)method.Results showed that the PF targets areas over the central-eastern equatorial Pacific,while the sensitive areas determined by the IEG method are slightly to the east of the former.Although a small part of the areas targeted by the IEG method also lie in the southeast equatorial Pacific,this does not affect the large-scale overlapping of the sensitive areas determined by these two methods in the eastern equatorial Pacific.Therefore,sensitive areas determined by the two methods are mutually supportive.When considering the uncertainty of methods for determining sensitive areas in realistic targeted observation,it is more reasonable to choose the above overlapping areas as sensitive areas for ENSO forecasting.This result provides scientific guidance for how to better determine sensitive areas for ENSO forecasting.

Accuracy of Measuring Camera Position by Marker Observation

A lower bound to errors of measuring object position is constructed as a function of parameters of a monocular computer vision system (CVS) as well as of observation conditions and a shape of an observed marker. This bound justifies the specification of the CVS parameters and allows us to formulate constraints for an object trajectory based on required measurement accuracy. For making the measurement, the boundaries of marker image are used.

Quality analysis of crustal tilt and strain observations in China's earthquakes in 2014

This work analyzes the quality of crustal tilt and strain observations during 2014, which were acquired from 269 sets of ground tiltmeters and 212 sets of strainmeters. In terms of data quality, the water tube tiltmeters presented the highest rate of excellent quality,approximately 91%, and the pendulum tiltmeters and ground strainmeters yielded rates of81% and 78%, respectively. This means that a total of 380 sets of instruments produced high-quality observational data suitable for scientific investigations and analyses.

带有状态/输入量化的无人艇航向跟踪控制

[目的]针对水面无人艇(USV)的海上通信受限的问题,提出一种带有状态/输入量化的USV航向跟踪控制方法。[方法]基于反步法设计系统控制律,结合动态面技术降低虚拟控制律的计算量膨胀问题。对于控制系统中存在的不确定项及外界干扰,利用扩张状态观测器(ESO)进行估计。采用均匀量化器分别对控制系统中的状态变量和控制输入进行量化,且量化后的状态反馈信息仅用于跟踪控制。利用量化状态递归设计基于ESO的USV航向控制器,证明闭环控制系统中量化变量和非量化变量间误差的有界性。[结果]基于李雅普诺夫稳定性理论,提出了量化误差考量及闭环系统稳定性判定方法,严格证明了所设计的带有状态量化和输入量化的USV航向跟踪控制系统的稳定性,仿真实验验证了该控制策略的有效性。[结论]结果表明,所提方法可为USV航向跟踪控制提供借鉴。

基于观测方程重构滤波算法的锂离子电池荷电状态估计

滤波算法中观测方程的准确性在电池状态评估中起着决定性作用。然而,该文通过试验发现,由于温度、工作电流和荷电状态(SOC)的影响,即使使用精度较高的电池模型,扩展卡尔曼滤波(EKF)算法中观测方程的输出值与实际电压之间仍会存在较大误差,即产生了较大的新息。该文提出一种基于观测方程重组的增强型扩展卡尔曼滤波(E-EKF)算法。该算法的核心思想是利用具有温度、SOC和电流自适应能力的误差修正策略对观测方程进行重组,实现算法中新息的降低,进而提高SOC估计的准确性。使用两种不同温度下的典型工况试验对E-EKF算法的性能进行了验证。试验结果表明,该算法能够适应不同的温度和工况,并具有较高的SOC估计精度。

矿井煤岩界面节点式雷达快速动态探测系统及实验研究

煤岩界面识别技术是煤矿智能化开采的关键技术之一。基于高频雷达波探测技术可实现煤岩界面的随采高精度探测,但仍存在矿井超大采高(≥6 m)片帮垮落带来设备的安全风险及采高突变(采高≤2 m)时空间限制设备通过的问题。在前期工作基础上提出了一种矿井煤岩界面节点式雷达快速动态探测系统并进行了煤岩界面探测实验研究,主要内容包括:①阐述矿井节点式雷达观测系统原理,根据矿井工作面实际环境设计煤岩界面识别观测系统方案及雷达传感单元安装方式;②研究并提出节点式采集控制系统和信息交互传输设计方案,实现数据动态采集控制及存储;③针对节点式采集方式及煤岩界面雷达反射回波特征,研究提出了节点探测数据增强处理方法、煤岩界面识别算法,可有效的实现煤岩界面智能识别与追踪、煤层厚度及空间坐标解算。为验证该方法的可行性,采用多个中心频率为1.5 GHz的探地雷达传感单元进行物理模型验证实验,并对节点式数据采集和连续数据采集结果进行了对比分析,实验结果表明:节点式采集方法与连续采集方法均可有效识别出煤岩界面,与连续采集方法相比,本文提出的节点式探测方法可实现数据的快速动态重复性采集,单次采集时长控制在10 s以内,煤层厚度探测结果平均误差为1.07 cm,最大误差为1.47 cm,平均误差百分比为7.64%。本方法为矿井智能化开采中煤岩界面的动态高精度探测提供技术支撑。

PMSM正切趋近律无位置传感器角度补偿方法研究

在负载转矩突变的动态过程中,基于PI调节器的角度补偿方法作用时PMSM超螺旋滑模观测器(STSMO)转子电角度估算误差波动剧烈,因此依据角度估算误差的定义,建立了转子电角度补偿算法的数学模型。根据滑模控制和趋近律理论,提出了基于正切趋近律的变步长闭环角度补偿方法,选择角度估算误差的半角正切值作为角度调节步长,并通过前馈解耦得到的角度估算误差正余弦信号计算该步长,实现了对无位置传感器控制系统动态性能和抗扰动能力的改善。根据归一化灵敏度的定义,分析调节步长随角度估算误差变化的灵敏度,提出了基于归一化补偿灵敏度的系统动态性能分析方法,衡量两种补偿算法作用下系统的动态性能。计算和仿真结果表明,正切趋近律补偿方法具有更高的归一化补偿灵敏度,在负载转矩突变等角度估算误差变化剧烈的工况下能够实现更好的补偿效果,抑制估算角度误差的波动。实验结果表明,相比传统的PI补偿方法,正切趋近律补偿方法能够将突加额定转矩动态过程中角度估算误差的波动幅度降低61.9%,动态过程持续时间缩短23%,有效提升了系统的动态性能和抗扰动能力。

基于未知输入观测器的电液执行器容错控制研究

电液执行器(EHA)中的传感器或执行器故障,会导致系统不稳定,存在安全隐患。为了解决这些问题,提出并研发了一种用于EHA的容错控制补偿技术。建立其数学模型,利用PID控制器进行位置跟踪控制,通过重构的未知输入观测器实现对干扰和传感器故障的诊断和误差补偿;利用容错控制补偿技术,可以提高EHA运行的可靠性和稳定性,确保控制器的稳定性及其跟踪性能。通过数值模拟和实验,证明了所研发的容错控制补偿技术的有效性。

基于CCSFF-FPLL的PMSM位置估算误差抑制

在基于滑模观测器的无位置传感器控制系统中,低通滤波器的使用会使反电动势产生相位延迟,在加减速工况下锁相环位置估计的动态性能较差,二者都会造成较大的位置估算误差。针对这两个问题,该文提出了一种基于CCSFF-FPLL的转子位置估算方法。该方法采用复系数同步频率滤波器(CCSFF)对反电动势进行滤波,利用其在中心频率处没有相位延迟和幅值衰减的特性来实现反电动势的准确提取;同时在传统锁相环的基础上增加了转速前馈的路径,设计了一种前馈锁相环(FPLL)来提取反电动势中的位置信息。仿真与实验结果表明,该文所提的方法能够有效抑制转子位置的估算误差,提高估算精度。