Influence of correlation scale errors on aquifer hydraulic conductivity inversion precision

In order to investigate the influence of correlation scale error on the inversion precision of the hydraulic conductivity of the aquifer,the successive linear estimator(SLE)was used to invert the hydraulic conductivity field of a heterogeneous aquifer based on synthetic experiments.By increasing the numbers of observation wells and pumping tests,we analyzed the difference between the estimated and true values of hydraulic conductivity with different correlation scale errors.The relationships between the observation well number and the error in inversion results,and between the pumping test number and the error in inversion results were investigated.The results show that,if the amount of observed head data is insufficient,there will be errors in inversion results with changing correlation scale.Due to the existence of correlation scale error,the improvement of inversion precision gradually slows down with the increase of the amount of observed head data,which indicates that too much observed head data causes data redundancy.Therefore,for the synthetic experiments described in this paper,the observation well number should be less than 41,the pumping test number should be less than 17,and a more suitable method should be selected according to the precision requirements of specific situations in practical engineering.

ON THE OPTIMAL BACKGROUND ERROR COVARIANCES: DIFFERENT SCALE ERRORS' CONTRIBUTION

The large-scale and small-scale errors could affect background error covariances for a regional numerical model with the specified grid resolution.Based on the different background error covariances influenced by different scale errors,this study tries to construct a so-called"optimal background error covariances"to consider the interactions among different scale errors.For this purpose,a linear combination of the forecast differences influenced by information of errors at different scales is used to construct the new forecast differences for estimating optimal background error covariances.By adjusting the relative weight of the forecast differences influenced by information of smaller-scale errors,the relative influence of different scale errors on optimal background error covariances can be changed.For a heavy rainfall case,the corresponding optimal background error covariances can be estimated through choosing proper weighting factor for forecast differences influenced by information of smaller-scale errors.The data assimilation and forecast with these optimal covariances show that,the corresponding analyses and forecasts can lead to superior quality,compared with those using covariances that just introduce influences of larger-or smallerscale errors.Due to the interactions among different scale errors included in optimal background error covariances,relevant analysis increments can properly describe weather systems(processes)at different scales,such as dynamic lifting,thermodynamic instability and advection of moisture at large scale,high-level and low-level jet at synoptic scale,and convective systems at mesoscale and small scale,as well as their interactions.As a result,the corresponding forecasts can be improved.

INFLUENCE OF DIFFERENT-SCALE ERRORS INTERACTIONS ON ANALYSIS AND FORECAST OF REGIONAL NWP MODEL

In the previous study, the influences of introducing larger- and smaller-scale errors on the background error covariances estimated at the given scales were investigated, respectively. This study used the covariances obtained in the previous study in the data assimilation and model forecast system based on three-dimensional variational method and the Weather Research and Forecasting model. In this study, analyses and forecasts from this system with different covariances for a period of one month were compared, and the causes for differing results were presented. The variations of analysis increments with different-scale errors are consistent with those of variances and correlations of background errors that were reported in the previous paper. In particular, the introduction of smaller-scale errors leads to greater amplitudes in analysis increments for medium-scale wind at the heights of both high- and low-level jets. Temperature and humidity analysis increments are greater at the corresponding scales at the middle- and upper-levels. These analysis increments could improve the intensity of the jet-convection system that includes jets at different levels and the coupling between them that is associated with latent heat release. These changes in analyses will contribute to more accurate wind and temperature forecasts in the corresponding areas. When smaller-scale errors are included, humidity analysis increments are significantly enhanced at large scales and lower levels, to moisten southern analyses. Thus, dry bias can be corrected, which will improve humidity forecasts. Moreover, the inclusion of larger-(smaller-) scale errors will be beneficial for the accuracy of forecasts of heavy(light) precipitation at large(small) scales because of the amplification(diminution) of the intensity and area in precipitation forecasts.

Estimation of random errors for lidar based on noise scale factor

Estimation of random errors, which are due to shot noise of photomultiplier tube(PMT) or avalanche photodiode(APD) detectors, is very necessary in lidar observation. Due to the Poisson distribution of incident electrons, there still exists a proportional relationship between standard deviation and square root of its mean value. Based on this relationship,noise scale factor(NSF) is introduced into the estimation, which only needs a single data sample. This method overcomes the distractions of atmospheric fluctuations during calculation of random errors. The results show that this method is feasible and reliable.

GNSS/MEMS IMU车载组合导航中IMU比例因子误差的影响分析

从系统状态模型出发,分析比例因子误差对组合导航精度和计算量的影响,同时基于车载运动的特点分析比例因子误差的观测性,提出一种仅保留航向陀螺仪和水平加速度计比例因子误差的降维状态模型。实验结果表明,当比例因子误差大于6×10^(-3)时,增广比例因子误差有助于提高导航精度,但计算量增加约170%;降维模型能够达到高维模型的导航精度,与不增广比例因子误差相比,计算量仅增加约70%。

随机复合材料结构非线性热-力耦合模拟的统计高阶多尺度方法

对于具有复杂随机细观构造的复合材料结构的非线性热-力耦合问题的随机多尺度建模和计算仍是一个具有挑战性的问题。本文发展了一个新的统计高阶多尺度方法,克服了随机多尺度问题直接模拟时巨大的计算量,实现了具有随机复合材料结构非线性热-力耦合问题的数值模拟。借助统计多尺度渐近分析和泰勒级数方法,本文严格推导了可以精确分析随机复合材料结构宏-细观尺度非线性热-力耦合响应的统计高阶多尺度计算模型。然后,通过局部误差分析证明了统计高阶多尺度计算模型中高阶校正项在保持计算模型局部能量和动量守恒的重要意义。进一步,建立了可以高效模拟随机复合材料结构非线性热-力耦合行为的具有离线和在线两阶段的时空多尺度算法。最后,通过数值实验验证了统计高阶多尺度方法的计算高效率和高精度。

基于SINS速度信息的里程计参数快速标定方法

针对里程计标度因数误差和安装误差对捷联惯导/里程计组合导航精度的显著影响,提出一种基于短时捷联惯导系统(strapdown inertial navigation system,SINS)速度信息的里程计参数标定方法.通过建立航位推算误差模型,构建惯性测量单元(inertial measurement unit,IMU)坐标系内惯导系统输出速度与里程计输出之间的关系式,得到里程计参数的计算公式;利用最小二乘法对里程计标度因数和安装误差进行标定.该方法只利用了捷联惯导信息,在1 min内就可实现里程计参数的初次标定,不需要相关参数误差值为小量的假设,并可以忽略杆臂效应对标定效果的影响.试验结果表明:当车辆行驶30 min后,利用该方法标定后的水平航位推算精度比传统标定方法定位精度高92.3%.

电子秤称量误差检定及补偿方法

本文概述了电子秤称量误差的检定方法,并分别从电子秤的使用环境、使用方法、校准与维护管理等方面分析了产生称量误差的主要因素,并提出相关补充方法。

基于无人机摄影测量的大比例尺地形图测制关键技术

为了进一步梳理大比例尺地形图中应用无人机摄影测量的关键技术,介绍了无人机摄影测量中的误差控制、空三及影像匹配,并重点介绍了大比例尺地形图测设过程中的像控点布设、影像质量检查以及数据检查等关键技术,并对该技术的价值体现进行了分析。研究结果表明:人为操作误差以及仪器设备自限性误差可以通过相应的修正公式予以修正,合理的像控点布设、影像质量检查及数据处理可以实现大比例尺地形图测制的高精度及高效率。

收发未精确同步条件下非相关多运动辐射源TOAs/FOAs协同定位方法

在联合到达时间/到达频率的无线定位体制中,除了TOAs/FOAs(Time-Of-Arrivals/Frequency-Of-Arrivals)估计误差与传感器位置/速度先验观测误差以外,收发两端的传感器时钟同步误差也是影响定位精度的重要因素.为了抑制时钟同步误差和各类观测误差的影响,本文针对非相关多运动辐射源定位场景,提出一种基于加权多维标度分析的TOAs/FOAs多辐射源协同定位方法.文中首先通过构造两组标量积矩阵推导定位关系式,然后基于一阶误差分析方法得到该关系式中的误差渐近统计特性,并进而构建联合定位与时钟同步误差校正的优化准则.针对此优化模型,本文提出一种基于正交投影矩阵数学性质的参数解耦合优化算法,可实现对多运动辐射源位置/速度参数与同步误差参数的分步估计,显著降低了参与优化迭代的变量维数.此外,文中还在收发未精确同步条件下推导TOAs/FOAs多辐射源协同定位模型的克拉美罗界,定量证明多辐射源协同定位可以带来性能增益,并且利用一阶误差分析以及正交投影矩阵数学性质证明新方法的渐近统计最优性.仿真实验结果验证所提出的协同定位方法的优越性.

MULTI-SCALE GAUSSIAN PROCESSES MODEL

A novel model named Multi-scale Gaussian Processes (MGP) is proposed. Motivated by the ideas of multi-scale representations in the wavelet theory, in the new model, a Gaussian process is represented at a scale by a linear basis that is composed of a scale function and its different translations. Finally the distribution of the targets of the given samples can be obtained at different scales. Compared with the standard Gaussian Processes (GP) model, the MGP model can control its complexity conveniently just by adjusting the scale pa-rameter. So it can trade-off the generalization ability and the empirical risk rapidly. Experiments verify the fea-sibility of the MGP model, and exhibit that its performance is superior to the GP model if appropriate scales are chosen.

Mesoscale Predictability of Mei-yu Heavy Rainfall

Recently reported results indicate that small amplitude and small scale initial errors grow rapidly and subsequently contaminate short-term deterministic mesoscale forecasts. This rapid error growth is dependent on not only moist convection but also the flow regime. In this study, the mesoscale predictability and error growth of mei-yu heavy rainfall is investigated by simulating a particular precipitation event along the mei-yu front on 4- 6 July 2003 in eastern China. Due to the multi-scale character of the mei-yu front and scale interactions, the error growth of mei-yu heavy rainfall forecasts is markedly different from that in middle-latitude moist baroclinic systems. The optimal growth of the errors has a relatively wide spectrum, though it gradually migrates with time from small scale to mesoscale. During the whole period of this heavy rainfall event, the error growth has three different stages, which similar to the evolution of 6-hour accumulated precipitation. Multi-step error growth manifests as an increase of the amplitude of errors, the horizontal scale of the errors, or both. The vertical profile of forecast errors in the developing convective instability and the moist physics convective system indicates two peaks, which correspond with inside the mei-yu front, and related to moist The error growth for the mei-yu heavy rainfall is concentrated convective instability and scale interaction.

基于尺度分析的CMA-GFS全球能量评估

全球模式能量循环和能量转换规律可准确反映模式动力和物理过程相互作用的物理机制,是诊断大气环流特征的重要方法。基于混合时空域能量循环框架,采用尺度分析方法,利用2022年中国气象局全球数值预报系统(CMA Global Forecast System,CMA-GFS)全球预报产品及欧洲中期天气预报中心第5代再分析资料(ECMWF reanalysis version 5,ERA5),考察CMA-GFS不同尺度下的能量蓄能及转换特征,以此诊断模式的误差来源。结果表明:CMA-GFS可有效预报大气能量循环基本特征,但其对斜压性的高估导致平均环流有效位能偏强,且具有随预报时效逐渐增长的趋势。定常和瞬变涡动能量分别受行星尺度和天气及以下尺度分量主导。涡动有效位能误差由模式斜压性决定,其中CMA-GFS的定常涡动有效位能偏高而瞬变涡动有效位能偏低。定常和瞬变涡动动能均存在系统性低估,负误差主要集中在副热带急流和极夜急流中心附近,偏强的正压输送使更多能量向平均环流转换,涡动能量偏弱。CMA-GFS的4种涡动能量在冬季预报偏低,而在夏季偏高或略偏低,严重削弱了季节变化影响。

改进多尺度结构化融合的红外与可见光图像融合

为解决弱光环境下的红外与可见光图像的融合结果存在对比度低、细节纹理不足、融合耗时长的问题,提出了一种改进多尺度结构化融合的方法。在图像融合前,采用动态范围压缩算法对弱光下的可见光图像进行增强,再通过多尺度结构化分解将增强后的可见光图像和红外图像分解成低频和高频信息;在融合过程中,提出一种基于均方根误差系数的方法对低频信息进行融合,提出一种基于信息熵自适应调整权重的策略对初步融合的高频信息进行二次优化融合,再通过多尺度结构化分解的逆变换重构出融合图像;最后,提出一种基于灰度分类的区域像素增强算法来提高融合后图像的对比度。将提出方法与9种常用的融合方法进行了定性和定量的分析比较,在TNO和CVC-14数据集上的实验结果表明,该方法在平均梯度、交叉熵、边缘强度、标准差和空间频率指标上取得了更好的客观评价结果,整体视觉效果也要优于对比方法。本方法的融合结果具有丰富的细节纹理、较高的清晰度和对比度,且融合耗时短。

Comparative Study of Response Surface Designs with Errors-in-Variables Model

This paper investigates the scaled prediction variances in the errors-in-variables model and compares the performance with those in classic model of response surface designs for three factors.The ordinary least squares estimators of regression coefficients are derived from a second-order response surface model with errors in variables.Three performance criteria are proposed.The first is the difference between the empirical mean of maximum value of scaled prediction variance with errors and the maximum value of scaled prediction variance without errors.The second is the mean squared deviation from the mean of simulated maximum scaled prediction variance with errors.The last performance measure is the mean squared scaled prediction variance change with and without errors.In the simulations,1 000 random samples were performed following three factors with 20 experimental runs for central composite designs and 15 for Box-Behnken design.The independent variables are coded variables in these designs.Comparative results show that for the low level errors in variables,central composite face-centered design is optimal;otherwise,Box-Behnken design has a relatively better performance.

Anovelprofile error model to calculate contact error

A new profile model based on multi-scale asperities is developed and contact error is cal- culated. After stratified sampling, the model can get the distribution law of entity points on each cross section. Asperity radius of curvature is estimated by the relationship between circle radius and the section interval. Contact error is related to surface form error. A model equation of contact error plane is calculated through a method based on static equilibrium theory. Three contact asperities which determine the contact error plane on the rough surface are studied. The simulation results show that contact error can be accurately calculated according to the profile error model.

径流预报误差的混合t Location-Scale分布模型及应用

基于混合t Location-Scale分布良好的自适应性,利用k均值聚类法挖掘数据隐含结构、良好的聚类效果特点,建立了不同预见期径流预报误差混合t Location-Scale分布模型。将模型应用于雅砻江流域官地水库,对预见期分别为6、12、18 h和24 h的区间径流预报误差进行了分析与建模,结果发现混合t Location-Scale分布模型弥补了单一分布难以描述径流预报误差特征多样性的局限,能更准确地描述不同预见期径流预报误差的统计特征,误差分布与实测径流预报误差的变化规律一致,可为水库水电站的径流预报和调度运行提供更加准确、可靠的来水数据。

Effect of Surface Roughness in Micro-nano Scale on Slotted Waveguide Arrays in Ku-band

Modeling of the roughness in micro-nano scale and its influence have not been fully investigated, however the roughness will cause amplitude and phase errors of the radiating slot, and decrease the precision and efficiency of the SWA in Ku-band. Firstly, the roughness is simulated using the electromechanical coupled（EC） model. The relationship between roughness and the antenna＇s radiation properties is obtained. For verification, an antenna proto- type is manufactured and tested, and the simulation method is introduced. According to the prototype, a contrasting experiment dealing with the flatness of the radiating plane is conducted to test the simulation method. The advantage of the EC model is validated by comparisons of the EC model and two classical roughness models （sine wave and fractal function）, which shows that the EC model gives a more accurate description model for roughness, the maxi- mum error is 13%. The existence of roughness strongly broadens the beamwidth and raises the side-lobe level of SWA, which is 1.2 times greater than the ideal antenna. In addition, effect of the EC model＇s evaluation indices is investigated, the most affected scale of the roughness is found, which is 1/10 of the working wavelength. The proposed research provides the instruction for antenna designing and manufacturing.

CMA全球集合预报系统误差增长及预报性能的尺度依赖特征诊断分析

利用CMA全球集合预报(CMA-GEPS)业务系统2020年6月1日至2021年5月31日一整年的500 hPa位势高度场(H_(500))预报数据,诊断评估了CMA-GEPS在北半球地区误差增长及预报性能的尺度依赖特征。使用谱滤波方法实现H_(500)不同尺度(包括行星尺度、天气尺度与次天气尺度)分量的分离。从集合平均均方根误差(简称集合平均误差)-离散度关系来看,在预报前期(108 h之前),CMA-GEPS集合平均误差小于集合离散度,存在过度发散的问题,主要是由天气尺度分量离散度过大导致;在预报后期(108 h之后),CMA-GEPS集合平均误差大于集合离散度,离散度偏小,是由行星尺度与天气尺度分量离散度不足共同引起。采用Dalcher等1987年修订的误差增长模型对H_(500)集合平均预报误差增长特征进行诊断分析,发现CMA-GEPS误差增长过程合理,初始误差在次天气尺度上增长最快,行星尺度上增长最慢;就绝对(相对)误差而言,模式误差对预报误差的影响随空间尺度的增大而增大(减小)。此外,将使用1989至2018年共计30 a的ERA-Interim再分析逐日数据得到的气候态分布作为参考预报,通过连续分级概率预报技巧评分(Continuously Ranked Probability Skill Score,CRPSS)检验了CMA-GEPS H_(500)及其不同尺度分量的概率预报技巧。结果表明,行星尺度分量概率预报技巧最高,次天气尺度分量最小,未经滤波的H_(500)预报技巧位于行星尺度与天气尺度分量预报技巧之间。上述诊断结果可为CMA-GEPS改进方向提供一定的客观依据。

考虑风电功率预测误差分时补偿的电热联合系统多时间尺度调度

由于风电具有不确定性,风电功率预测误差难以避免。热力系统具备储放热能力,通过调整热功率平移风电功率,可以降低风电功率预测误差的影响。但在风电并入电热联合系统时,风电变化频繁,热力系统具有较大惯性和延迟,难以与风电功率同步;同时,热力系统不同区域对电力系统指令的响应速度也不同。因此,为了解决利用热力系统补偿风电功率预测误差时响应时间不同步的问题,提出了一种考虑风电功率预测误差分时补偿的电热联合系统多时间尺度调度策略。首先,分析概率区间误差评估和实时预测误差评估的评估周期。然后,研究热力系统中供热区域和管网的时间特性。最后,在日前阶段利用响应速度较慢的供热区域补偿评估周期较长的概率区间误差,在实时阶段利用响应速度较快的管网补偿评估周期较短的实时预测误差,建立了多时间尺度调度模型。算例分析表明,所提策略实现了不同调度周期、不同预测误差评估周期、不同热力系统区域响应速度在时间上的匹配,减少了风电功率预测误差的影响,提高了系统的风电消纳能力。