Assessing the Performance of a Dynamical Downscaling Simulation Driven by a Bias-Corrected CMIP6 Dataset for Asian Climate

In this study,we aim to assess dynamical downscaling simulations by utilizing a novel bias-corrected global climate model(GCM)data to drive a regional climate model(RCM)over the Asia-western North Pacific region.Three simulations were conducted with a 25-km grid spacing for the period 1980–2014.The first simulation(WRF_ERA5)was driven by the European Centre for Medium-Range Weather Forecasts Reanalysis 5(ERA5)dataset and served as the validation dataset.The original GCM dataset(MPI-ESM1-2-HR model)was used to drive the second simulation(WRF_GCM),while the third simulation(WRF_GCMbc)was driven by the bias-corrected GCM dataset.The bias-corrected GCM data has an ERA5-based mean and interannual variance and long-term trends derived from the ensemble mean of 18 CMIP6 models.Results demonstrate that the WRF_GCMbc significantly reduced the root-mean-square errors(RMSEs)of the climatological mean of downscaled variables,including temperature,precipitation,snow,wind,relative humidity,and planetary boundary layer height by 50%–90%compared to the WRF_GCM.Similarly,the RMSEs of interannual-tointerdecadal variances of downscaled variables were reduced by 30%–60%.Furthermore,the WRF_GCMbc better captured the annual cycle of the monsoon circulation and intraseasonal and day-to-day variabilities.The leading empirical orthogonal function(EOF)shows a monopole precipitation mode in the WRF_GCM.In contrast,the WRF_GCMbc successfully reproduced the observed tri-pole mode of summer precipitation over eastern China.This improvement could be attributed to a better-simulated location of the western North Pacific subtropical high in the WRF_GCMbc after GCM bias correction.

面向大型数据集的局部敏感哈希K−means算法

大型数据集高效处理策略是煤矿安全监测智能化、采掘智能化等煤矿智能化建设的关键支撑。针对K−means算法面对大型数据集时聚类高效性及准确性不足的问题,提出了一种基于局部敏感哈希(LSH)的高效K−means聚类算法。基于LSH对抽样过程进行优化,提出了数据组构建算法LSH−G,将大型数据集合理划分为子数据组,并对数据集中的噪声点进行有效删除;基于LSH−G算法优化密度偏差抽样(DBS)算法中的子数据组划分过程,提出了数据组抽样算法LSH−GD,使样本集能更真实地反映原始数据集的分布规律;在此基础上,通过K−means算法对生成的样本集进行聚类,实现较低时间复杂度情况下从大型数据集中高效挖掘有效数据。实验结果表明:由10个AND操作与8个OR操作组成的级联组合为最优级联组合,得到的类中心误差平方和(SSEC)最小;在人工数据集上,与基于多层随机抽样(M−SRS)的K−means算法、基于DBS的K−means算法及基于网格密度偏差抽样(G−DBS)的K−means算法相比,基于LSH−GD的K−means算法在聚类准确性方面的平均提升幅度分别为56.63%、54.59%及25.34%,在聚类高效性方面的平均提升幅度分别为27.26%、16.81%及7.07%;在UCI标准数据集上,基于LSH−GD的K−means聚类算法获得的SSEC与CPU消耗时间(CPU−C)均为最优。

Long-term Stability and Oceanic Mean State Simulated by the Coupled Model FGOALS-s2

We describe the long-term stability and mean climatology of oceanic circulations simulated by version 2 of the Flexible Global Ocean-Atmosphere-Land System model （FGOALS-s2）. Driven by pre-industrial forcing, the integration of FGOALS-s2 was found to have remained stable, with no obvious climate drift over 600 model years. The linear trends of sea SST and sea surface salinity （SSS） were -0.04℃ （100 yr）-1 and 0.01 psu （100 yr）-1, respectively. The simulations of oceanic temperatures, wind-driven circulation and thermohaline circulation in FGOALS-s2 were found to be comparable with observations, and have been substantially improved over previous FGOALS-s versions （1.0 and 1.1）. However, significant SST biases （exceeding 3℃） were found around strong western boundary currents, in the East China Sea, the Sea of Japan and the Barents Sea. Along the eastern coasts in the Pacific and Atlantic Ocean, a warm bias （〉3℃） was mainly due to overestimation of net surface shortwave radiation and weak oceanic upwelling. The difference of SST biases in the North Atlantic and Pacific was partly due to the errors of meridional heat transport. For SSS, biases exceeding 1.5 psu were located in the Arctic Ocean and around the Gulf Stream. In the tropics, freshwater biases dominated and were mainly caused by the excess of precipitation. Regarding the vertical dimension, the maximal biases of temperature and salinity were located north of 65°N at depths of greater than 600 m, and their values exceeded 4℃ and 2 psu, respectively.

Optimal Generalized Biased Estimator in Linear Regression Model

The paper introduces a new biased estimator namely Generalized Optimal Estimator (GOE) in a multiple linear regression when there exists multicollinearity among predictor variables. Stochastic properties of proposed estimator were derived, and the proposed estimator was compared with other existing biased estimators based on sample information in the the Scalar Mean Square Error (SMSE) criterion by using a Monte Carlo simulation study and two numerical illustrations.

Performance of Existing Biased Estimators and the Respective Predictors in a Misspecified Linear Regression Model

In this paper, the performance of existing biased estimators (Ridge Estimator (RE), Almost Unbiased Ridge Estimator (AURE), Liu Estimator (LE), Almost Unbiased Liu Estimator (AULE), Principal Component Regression Estimator (PCRE), r-k class estimator and r-d class estimator) and the respective predictors were considered in a misspecified linear regression model when there exists multicollinearity among explanatory variables. A generalized form was used to compare these estimators and predictors in the mean square error sense. Further, theoretical findings were established using mean square error matrix and scalar mean square error. Finally, a numerical example and a Monte Carlo simulation study were done to illustrate the theoretical findings. The simulation study revealed that LE and RE outperform the other estimators when weak multicollinearity exists, and RE, r-k class and r-d class estimators outperform the other estimators when moderated and high multicollinearity exist for certain values of shrinkage parameters, respectively. The predictors based on the LE and RE are always superior to the other predictors for certain values of shrinkage parameters.

New Efficient Estimators of Population Mean Using Non-Traditional Measures of Dispersion

One of the aims in survey sampling is to search for the estimators with highest efficiency. In the present paper, three improved estimators of population mean have been proposed using some non-traditional measures of dispersion of auxiliary variable such as Gini’s mean difference, Downton’s method and probability weighted moments early given by Abid [1] with a special population parameter of auxiliary variable. The large sample properties that are biased and mean squared errors of the proposed estimators have been derived up to the first order of approximation. A theoretical comparison of the proposed estimators has been made with the other existing estimators of population mean using auxiliary information. The conditions under which the proposed estimators perform better than the other existing estimators of population mean have been given. A numerical study is also carried out to see the performances of the proposed and existing estimators of population mean and verify the conditions under which proposed estimators are better than other estimators. It has been shown that the proposed estimators perform better than the existing estimators as they are having lesser mean squared error.

Exponential Ratio Type Estimators of Population Mean under Non-Response

This paper proposes some exponential ratio type estimators of population mean under the situations when certain observations for some sampling units are missing. These missing observations may be for either auxiliary variable or study variable. The biases and mean square errors of the proposed estimators have been derived, up to the first order of approximation. The proposed estimators are compared theoretically with that of the existing ratio type estimators defined by [1]. It has been found that the proposed exponential ratio type estimators perform better than the mean per unit estimator even for the low positive correlation between study variable and auxiliary variable. Moreover, we obtained the conditions for which our proposed estimators are better than the corresponding ratio type estimators of [1]. To verify the theoretical results obtained, a simulation study is carried out finally.

Biased Diffusive Search Triggered by ATP Binding During Kinesin's Stepping

在这份报纸，我们在场有一条思考边界和一条吸收边界， kinesin 马达的自由的头的散开的搜索能在偏向它的向前有约束力的地点的一个不对称现象 conformational 潜力。在大量条件下面，用第一经过的时间分析，我们执行数字模拟到 Langevin 方程，并且在负担力量上获得前面的步的居住时间的依赖。并且我们由 Laplace 变换方法在居住时间计算表示。数字、分析的结果证明居住时间指数地取决于负担力量，它为试验性的数据提供简单物理解释。我们的结果建议在颈连接器的那个 ATP 有约束力符合构造的变化在 kinesin 的 mechanochemical 周期期间在单向性的步骤起一个重要作用。

An Efficient Class of Estimators for the Finite Population Mean in Ranked Set Sampling

In this paper, we propose a class of estimators for estimating the finite population mean of the study variable under Ranked Set Sampling (RSS) when population mean of the auxiliary variable is known. The bias and Mean Squared Error (MSE) of the proposed class of estimators are obtained to first degree of approximation. It is identified that the proposed class of estimators is more efficient as compared to [1] estimator and several other estimators. A simulation study is carried out to judge the performances of the estimators.

长度偏差完全数据下均值剩余寿命的矩类估计

长度偏差数据在寿命研究中是广泛存在的,但是在长度偏差完全样本下对均值剩余寿命的研究却相对较少。作为生存分析中评估个体生存情况的重要指标之一,均值剩余寿命也越来越受到统计研究者的关注。为了在长度偏差完全数据下建立均值剩余寿命函数的非参数估计,采用逆概率加权方法,以矩估计为基础,得到了均值剩余寿命的两种估计量,并证明了两个估计量在适当条件下分别依分布收敛到均值为零的正态变量。为了评估两种点估计在有限样本下的优劣,还进行了一系列的数值模拟试验,并将模拟结果与已有方法进行了比较。数值结果表明了两种估计方法的合理性。

有偏估计与LS估计的比较与选择

从假设检验的角度研究测量平差Gauss Markov模型中有偏估计与LS估计的选择问题。首先在均方误差准则下对目前应用最广泛的2种有偏估计———岭估计和主成分估计与LS估计进行了比较研究,得到了岭估计、主成分估计优于LS估计的条件;然后运用统计方法对这些条件的成立进行了假设检验;最后通过数值实验说明,在一定显著性水平下当原假设被接受时,说明没有理由不相信采用有偏估计来代替LS估计的合理性,可认为采用有偏估计将对LS估计做出比较有效的改进,当原假设被拒绝时,说明对采用有偏估计的优越性产生了怀疑,此时建议仍采用LS估计。

聚集数据的线性模型参数的有偏估计

对于聚集数据的线性模型,提出了二种有偏估计,在均方误差(MSE)意义下,讨论了它们的优良性质,并将这二种估计进行了比较.本文提出的估计,优于有关文献中的结果.

回归系数的一种有偏估计

为了改进最小二乘估计,克服其在设计阵呈病态时表现出的不可靠性,本文将最小二乘估计、Stein型估计和组合主成分估计的方法联合起来,提出了多元线性模型回归系数的一种新的有偏估计.并且证明了:此种估计在均方误差意义下是可容许估计;在参数区域的某个椭球内,这种新的估计优于最小二乘估计、主成分估计和组合主成分估计.

岭型主成分估计的优良性质

用线性回归模型的一种有偏岭型主成分估计,证明岭型主成分估计在M SE和GM SE准则下优于最小二乘估计,而且比主成分估计更有效,在协方差阵准则下优于最小二乘估计。并且进一步得到了在均方误差意义下岭型主成分估计是可容许估计。

多元线性模型参数的有偏估计

文章对于多元线性模型Yn×q^(Xn×pBp×q,Vq×q In×n)提出了参数B的一种有偏估计^βh,^hβ在线性模型典则形式下,对应的有偏估计为^αh=(IqΛ+hI)-1(IqΛ+I)^α,此^αh有很好的统计性质;并阐明它在均方误差准则下这种估计相对于最小二乘估计和岭估计的优良性问题,从而推广了已有的有关结果。

典型有偏估计方法均方误差极小值一致性分析

针对高斯-马尔科夫定理只能保证最小二乘估计在线性无偏估计类中具有最小方差,和目前有偏估计对最小二乘估计的改善程度研究甚少的现状,提出研究典型有偏估计方法的均方误差极小值的一致性。结果表明:尽管不同有偏估计方法具有不同的结构形式,但是这些有偏估计方法对最小二乘估计方差的改善程度是相同的,即均方误差的极小值总是相等的。实例分析结果也表明:典型有偏估计方法的均方误差极小值是一致的。

机动目标状态估计的最小均方误差界

基于多项式模型的各种自适应滤波算法被广泛应用于机动目标跟踪领域,但尚没有统一的评估标准来衡量这些跟踪算法的优劣。由于存在确定的时变未知输入,机动目标的状态估计实际为有偏估计。基于状态估计均方误差最小的准则,推导了多项式模型滤波的最小均方误差界计算方法,获得了使状态估计均方误差最小的过程噪声方差变化规律。该方法给出了各种基于多项式模型的机动目标跟踪算法的估计均方误差下限,也为机动目标跟踪中最优过程噪声方差的设定提供了依据。仿真结果验证了算法的有效性。

从复合词的“异序”论汉语的类型学特征

汉语复合词出现异序现象是汉语词汇从单音词向双音词发展的必然产物,是汉语的韵律要求和共同的语素义为之造就了支撑条件,是丰富的方言提供了生存的土壤。汉语构词上语序AB式和BA式并存的事实证明,汉语不仅有"修饰语+中心语"的偏正式合成词,也同时具有"中心语+修饰语"的正偏式合成词,后者并非受亲属语言或古越语的影响。

线性回归模型系数有偏估计研究

针对引起线性回归模型LS估计性能变坏的根本原因,提出了回归系数的广义c-K估计,将众多经典的有偏估计结合在一起,对有偏估计的改进进行了研究,分别证明了最小化均方误差和数量化矩阵K均可对Stein估计进行改进,给出了参数的最优值,为病态线性回归模型系数有偏估计的改进提供了有效途径。