Relationships between Terrain Features and Forecasting Errors of Surface Wind Speeds in a Mesoscale Numerical Weather Prediction Model

Numerical weather prediction(NWP)models have always presented large forecasting errors of surface wind speeds over regions with complex terrain.In this study,surface wind forecasts from an operational NWP model,the SMS-WARR(Shanghai Meteorological Service-WRF ADAS Rapid Refresh System),are analyzed to quantitatively reveal the relationships between the forecasted surface wind speed errors and terrain features,with the intent of providing clues to better apply the NWP model to complex terrain regions.The terrain features are described by three parameters:the standard deviation of the model grid-scale orography,terrain height error of the model,and slope angle.The results show that the forecast bias has a unimodal distribution with a change in the standard deviation of orography.The minimum ME(the mean value of bias)is 1.2 m s^(-1) when the standard deviation is between 60 and 70 m.A positive correlation exists between bias and terrain height error,with the ME increasing by 10%−30%for every 200 m increase in terrain height error.The ME decreases by 65.6%when slope angle increases from(0.5°−1.5°)to larger than 3.5°for uphill winds but increases by 35.4%when the absolute value of slope angle increases from(0.5°−1.5°)to(2.5°−3.5°)for downhill winds.Several sensitivity experiments are carried out with a model output statistical(MOS)calibration model for surface wind speeds and ME(RMSE)has been reduced by 90%(30%)by introducing terrain parameters,demonstrating the value of this study.

Uncertainties of landslide susceptibility prediction: Influences of random errors in landslide conditioning factors and errors reduction by low pass filter method

In the existing landslide susceptibility prediction(LSP)models,the influences of random errors in landslide conditioning factors on LSP are not considered,instead the original conditioning factors are directly taken as the model inputs,which brings uncertainties to LSP results.This study aims to reveal the influence rules of the different proportional random errors in conditioning factors on the LSP un-certainties,and further explore a method which can effectively reduce the random errors in conditioning factors.The original conditioning factors are firstly used to construct original factors-based LSP models,and then different random errors of 5%,10%,15% and 20%are added to these original factors for con-structing relevant errors-based LSP models.Secondly,low-pass filter-based LSP models are constructed by eliminating the random errors using low-pass filter method.Thirdly,the Ruijin County of China with 370 landslides and 16 conditioning factors are used as study case.Three typical machine learning models,i.e.multilayer perceptron(MLP),support vector machine(SVM)and random forest(RF),are selected as LSP models.Finally,the LSP uncertainties are discussed and results show that:(1)The low-pass filter can effectively reduce the random errors in conditioning factors to decrease the LSP uncertainties.(2)With the proportions of random errors increasing from 5%to 20%,the LSP uncertainty increases continuously.(3)The original factors-based models are feasible for LSP in the absence of more accurate conditioning factors.(4)The influence degrees of two uncertainty issues,machine learning models and different proportions of random errors,on the LSP modeling are large and basically the same.(5)The Shapley values effectively explain the internal mechanism of machine learning model predicting landslide sus-ceptibility.In conclusion,greater proportion of random errors in conditioning factors results in higher LSP uncertainty,and low-pass filter can effectively reduce these random errors.

Uncertainties in landslide susceptibility prediction:Influence rule of different levels of errors in landslide spatial position

The accuracy of landslide susceptibility prediction(LSP)mainly depends on the precision of the landslide spatial position.However,the spatial position error of landslide survey is inevitable,resulting in considerable uncertainties in LSP modeling.To overcome this drawback,this study explores the influence of positional errors of landslide spatial position on LSP uncertainties,and then innovatively proposes a semi-supervised machine learning model to reduce the landslide spatial position error.This paper collected 16 environmental factors and 337 landslides with accurate spatial positions taking Shangyou County of China as an example.The 30e110 m error-based multilayer perceptron(MLP)and random forest(RF)models for LSP are established by randomly offsetting the original landslide by 30,50,70,90 and 110 m.The LSP uncertainties are analyzed by the LSP accuracy and distribution characteristics.Finally,a semi-supervised model is proposed to relieve the LSP uncertainties.Results show that:(1)The LSP accuracies of error-based RF/MLP models decrease with the increase of landslide position errors,and are lower than those of original data-based models;(2)70 m error-based models can still reflect the overall distribution characteristics of landslide susceptibility indices,thus original landslides with certain position errors are acceptable for LSP;(3)Semi-supervised machine learning model can efficiently reduce the landslide position errors and thus improve the LSP accuracies.

R-Factor Analysis of Data Based on Population Models Comprising R- and Q-Factors Leads to Biased Loading Estimates

Effects of performing an R-factor analysis of observed variables based on population models comprising R- and Q-factors were investigated. Although R-factor analysis of data based on a population model comprising R- and Q-factors is possible, this may lead to model error. Accordingly, loading estimates resulting from R-factor analysis of sample data drawn from a population based on a combination of R- and Q-factors will be biased. It was shown in a simulation study that a large amount of Q-factor variance induces an increase in the variation of R-factor loading estimates beyond the chance level. Tests of the multivariate kurtosis of observed variables are proposed as an indicator of possible Q-factor variance in observed variables as a prerequisite for R-factor analysis.

DEVELOPMENT AND ERROR ANALYSIS OF AN INSPECTING SYSTEM FOR MEASURING THE ROTATIONAL INERTIA OF BULLETS

介绍了一种用于测量弹丸转动惯量的高精度测量仪器，该仪器利用双悬丝扭摆机构、光电计时系统提取摆动周期信息，由MCS-51系列单片机组成的测量系统进行数据处理，并由LED显示器测量结果。文中阐述了仪器的工作原理， 推导了所需的计算公式，分析了产生测量误差的因素，进行了定量精度分析，给出了实验结果与测量精度。

Revise and Statistical Analysis of the Alien Press Errors in History of Chinese Journalism by Ko Kung-chen

Since Ko Kung-chen published History of Chinese Journalism with Shanghai Commercial Press in 1927,this work had been published by different publishing houses and reprinted many times amounting to 18 editions.There are many historical narrative and fact errors of alien press in this work,and up to date these errors are not revised.This essay aims to revise the omitted errors of alien press in History of Chinese Journalism systematically besides those corrected by other works or essays,and carry on the statistical analysis of the errors to probe new approach for the study of alien periodicals and press in China.

Error Analysis of English Pronunciation Acquisition by Chinese Non-English Majors

Chinese non-English majors are a large group of English learners.In the process of English pronunciation acquisition,issues such as incomplete phonological knowledge,transfer of mother tongue,and overgeneralization,lead to confusion of phonemes and stress,misunderstanding of syllable structure,and errors of assimilation,drop,and epenthesis.The accuracy of English pronunciation can only be improved by knowing both English and Chinese phonological systems,strengthening the teaching of English phonological knowledge,and adopting various phonological training activities.

Gross errors identification and correction of in-vehicle MEMS gyroscope based on time series analysis

This paper presents a novel approach to identify and correct the gross errors in the microelectromechanical system （MEMS） gyroscope used in ground vehicles by means of time series analysis. According to the characteristics of autocorrelation function （ACF） and partial autocorrelation function （PACF）, an autoregressive integrated moving average （ARIMA） model is roughly constructed. The rough model is optimized by combining with Akaike＇s information criterion （A/C）, and the parameters are estimated based on the least squares algorithm. After validation testing, the model is utilized to forecast the next output on the basis of the previous measurement. When the difference between the measurement and its prediction exceeds the defined threshold, the measurement is identified as a gross error and remedied by its prediction. A case study on the yaw rate is performed to illustrate the developed algorithm. Experimental results demonstrate that the proposed approach can effectively distinguish gross errors and make some reasonable remedies.

Time Series Analysis and Prediction of COVID-19 Pandemic Using Dynamic Harmonic Regression Models

Rapidly spreading COVID-19 virus and its variants, especially in metropolitan areas around the world, became a major health public concern. The tendency of COVID-19 pandemic and statistical modelling represents an urgent challenge in the United States for which there are few solutions. In this paper, we demonstrate combining Fourier terms for capturing seasonality with ARIMA errors and other dynamics in the data. Therefore, we have analyzed 156 weeks COVID-19 dataset on national level using Dynamic Harmonic Regression model, including simulation analysis and accuracy improvement from 2020 to 2023. Most importantly, we provide new advanced pathways which may serve as targets for developing new solutions and approaches.

A Brief Study of Second Language Learning Strategies From the Perspective of Error Analysis

Language teaching is not a one-way process.It interacts with language learning in an extremely intricate way.To improve language teaching,we need to take the process of language learning into account.This paper tries to explore and understand what strategies the second language learners consciously or subconsciously adopt during their language learning process through the analyses of the linguistic errors they commit,so as to provide some insights into language teaching practice.

Separation of Comprehensive Geometrical Errors of a 3-DOF Parallel Manipulator Based on Jacobian Matrix and Its Sensitivity Analysis with Monte-Carlo Method

Parallel kinematic machines （PKMs） have the advantages of a compact structure,high stiffness,a low moving inertia,and a high load/weight ratio.PKMs have been intensively studied since the 1980s,and are still attracting much attention.Compared with extensive researches focus on their type/dimensional synthesis,kinematic/dynamic analyses,the error modeling and separation issues in PKMs are not studied adequately,which is one of the most important obstacles in its commercial applications widely.Taking a 3-PRS parallel manipulator as an example,this paper presents a separation method of source errors for 3-DOF parallel manipulator into the compensable and non-compensable errors effectively.The kinematic analysis of 3-PRS parallel manipulator leads to its six-dimension Jacobian matrix,which can be mapped into the Jacobian matrix of actuations and constraints,and then the compensable and non-compensable errors can be separated accordingly.The compensable errors can be compensated by the kinematic calibration,while the non-compensable errors may be adjusted by the manufacturing and assembling process.Followed by the influence of the latter,i.e.,the non-compensable errors,on the pose error of the moving platform through the sensitivity analysis with the aid of the Monte-Carlo method,meanwhile,the configurations of the manipulator are sought as the pose errors of the moving platform approaching their maximum.The compensable and non-compensable errors in limited-DOF parallel manipulators can be separated effectively by means of the Jacobian matrix of actuations and constraints,providing designers with an informative guideline to taking proper measures for enhancing the pose accuracy via component tolerancing and/or kinematic calibration,which can lay the foundation for the error distinguishment and compensation.

Consequences of Insufficient Selectivity in Quantitative and Qualitative Chemical Analysis

A problem in chemical analysis in connection with measurements of a substance normally occurring in a sample, or identification of a substance which should not exist in a sample, is insufficient selectivity. In this article, we analyze this problem and propose remedies. We use a real doping case to illustrate how chemical noise causes a serious selectivity problem, probably causing a false positive outcome.

Error Analysis of the English Renderings of Public and Scenic Signs in Zunyi

在研究遵义市600多幅具有英文翻译的公示语基础上．本论文根据“错误理论”从语言和文化方层次分析了翻译错误的分布．并找出7种导致翻译错误的主要原因，同时也提出了解决这些翻译错误的建议措施。

An analysis on the error structure and mechanism of soil moisture and ocean salinity remotely sensed sea surface salinity products

For the application of soil moisture and ocean salinity（SMOS） remotely sensed sea surface salinity（SSS） products,SMOS SSS global maps and error characteristics have been investigated based on quality control information.The results show that the errors of SMOS SSS products are distributed zonally,i.e.,relatively small in the tropical oceans,but much greater in the southern oceans in the Southern Hemisphere（negative bias） and along the southern,northern and some other oceanic margins（positive or negative bias）.The physical elements responsible for these errors include wind,temperature,and coastal terrain and so on.Errors in the southern oceans are due to the bias in an SSS retrieval algorithm caused by the coexisting high wind speed and low temperature; errors along the oceanic margins are due to the bias in a brightness temperature（TB） reconstruction caused by the high contrast between L-band emissivities from ice or land and from ocean; in addition,some other systematic errors are due to the bias in TB observation caused by a radio frequency interference and a radiometer receivers drift,etc.The findings will contribute to the scientific correction and appropriate application of the SMOS SSS products.

Analysis and modeling of error of spiral bevel gear grinder based on multi-body system theory

Six-axis numerical control spiral bevel gear grinder was taken as the object, multi-body system theory and Denavit-Hartenberg homogeneous transformed matrix （HTM） were utilized to establish the grinder synthesis error model, and the validity of model was confirmed by the experiment. Additionally, in grinding wheel tool point coordinate system, the errors of six degrees of freedom were simulated when the grinding wheel revolving around C-axis, moving along X-axis and Y-axis. The influence of these six errors on teeth space, helix angle, pitch, teeth profile was discussed. The simulation results show that the angle error is in the range from -0.148 4 tad to -0.241 9 rad when grinding wheel moving along X, Y-axis; the translation error is in the range from 0.866 0 μm to 3.605 3μm when grinding wheel moving along X-axis. These angle and translation errors have a great influence on the helix angle, pitch, teeth thickness and tooth socket.

PROBABILISTIC ANALYSIS AND MONTE CARLO SIMULATION OF THE KINEMATIC ERROR IN A SPATIAL LINKAGE

The stochastic models of the usual joints are first established through intro-ducing the concepts of“clearance characteristic element”and“clearance space”.After de-riving the probability density function of the joint clearance and making the probabilisticanalysis of the resulted kinematic errors,the sampling formulas of the independent varia-bles of the joint clearances are further deduced.Through Monte Carlo simulation,the sta-tistical characteristics and frequency histograms of the kinematic errors are then analysedon computer.

Analysis of Spatial and Temporal Variation Characteristics of the bvalue in the Hetao Seismic Belt

Focusing on the b-value as the research target and under the theoretical framework that the b-value is determined by stress state and medium properties, the variation characteristics of the b-value in the Hetao seismic belt are analyzed. Earthquakes with ML≥1. 5,which have occurred in the Hetao seismic belt since 1970 are selected to conduct the quantitative detection of the non-uniform temporal change of Mcusing the EMR method. Based on the actual situation of seismic activity,the lower limit magnitude is set as ML2. 0 to calculate the b-value. The temporal variation of the b-value is calculated and scanned using the least square method. The results show that there is a good corresponding relationship between the temporal variation of the b-value,strong earthquake activity,network distribution and aftershock deletion. We also calculate and scan the spatial variation of the b-value by using maximum likelihood. The results show that the spatial difference is possibly caused by stress state and crustal medium properties. The tectonic dependence of the b-value is obvious. In addition,the sufficient earthquakes samples in each magnitude interval are still a key step to improve the calculation accuracy of the b-value.

Linearization of the Liouville Equation, Multiple Splits of the Chandler Frequency, Markowitz Wobbles, and Error Analysis

The rotation of the physical Earth is far more complex than the rotation of a biaxial or slightly triaxial rigid body can represent. The linearization of the Liouville equation via the Munk and MacDonal perturbation scheme has oversimplified polar excitation physics. A more conventional linearization of the Liouville equation as the generalized equation of motion for free rotation of the physical Earth reveals: 1) The reference frame is most essential, which needs to be unique and physically located in the Earth;2) Physical angular momentum perturbation arises from motion and mass redistribution to appear as relative angular momentum in a rotating Earth, which excites polar motion and length of day variations;3) At polar excitation, the direction of the rotation axis in space does not change besides nutation and precession around the invariant angular momentum axis, while the principal axes shift responding only to mass redistribution;4) Two inertia changes appear simultaneously at polar excitation;one is due to mass redistribution, and the other arises from the axial near-symmetry of the perturbed Earth;5) The Earth at polar excitation becomes slightly triaxial and axially near-symmetrical even it was originally biaxial;6) At polar excitation, the rotation of a non-rigid Earth becomes unstable;7) The instantaneous figure axis or mean excitation axis around which the rotation axis physically wobbles is not a principal axis;8) In addition to amplitude excitation, the Chandler wobble possesses also multiple frequency-splits and is slow damping;9) Secular polar drift is after the products of inertia and always associated with the Chandler wobble;both belong to polar motion;10) The Earth will reach its stable rotation only after its rotation axis, major principal axis, and instantaneous figure axis or mean excitation axis are all completely aligned with each other to arrive at the minimum energy configuration of the system;11) The observation of the multiple splits of the Chandler frequency is further examined by means of exact-bandwidth filtering and spectral analysis, which confirms the theoretical prediction of the linearized Liouville equation. After the removal of the Gibbs phenomenon from the polar motion spectra, Markowitz wobbles are also observed;12) Error analysis of the ILS data demonstrates that the incoherent noises from the Wars in 1920-1945 are separable from polar motion and removable, so the ILS data are still reliable and useful for the study of the continuation of polar motion.

The Synthetic Effect of Magnetorheological Finishing Technology:Analysis,Modeling,and Control

The controllable key factors in magnetorheological finishing device were studied to determine their influence on efficiency of magnetorheological finishing(MRF)and surface of MRF,as well as interaction between efficiency and surface.Based on theoretical and experimental research,the law of material removal was explored and a new process variable based material removal model(PVMR)was proposed.The experimental findings demonstrate that PVMR reveals the law of the material removal with introduction of three concepts and makes a material removal function z(y i)where the magnetorheological finishing process parameters are considered since they are easy to control and adjust.So the material function of this model is quadratic curve function which is readily suitable for stability and online control magnetorheological finishing.

The simulation and error analysis of raindrop size distribution obtained by micro rain radar

Observation of raindrop size distribution( DSD) with a vertically pointing micro rain radar( MRR) has important significance in the precipitation measurement field. The Mie scattering of large particle,vertical wind and air turbulence have great influences on the retrieval of DSD measured by MRR. This paper simulates the process of how three factors affect the inversion of DSD and the calculation of other precipitation parameters,then makes the errors analysis. Because the wavelength of MRR is 12.5mm,M ie theory is more suitable for the precipitation particle than Rayleigh approximation,w hich may cause the underestimation of number density of small droplets and the overestimation of that in middle field. The vertical wind results in inaccurate estimation of particle terminal velocity,so the diameter is measured with some errors by empirical relationship,w hich affects the calculating accuracy of radar reflectivity and rain rate. Air turbulence can broaden the pow er spectral density,of which the impact on the inversion of DSD are concentrated in small droplets field. Then the measured data from MRR is analyzed and the results prove the impacts of those factors. Finally,according to the analysis and application limits,the prospect of the future research trend of particle size distribution is conducted.