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A New Method of significance testing for Correlation-Coefficient Fields and Its Application

Advances in Atmospheric Sciences 2022年第3期39卷 529-535页

作者： Xiaojuan SUN Siyan LI Julian XL WANG Panxing WANG Dong GUO Key Laboratory of Meteorological Disaster Ministry of Education/Collaborative Innovation Center on Forecast and Evaluation of Meteorological DisastersNanjing University of Information Science and TechnologyNanjing 210044China Nanjing Xindan Institute of Meteorological Science and Technology Nanjing 210044China Air Resources Laboratory National Oceanic and Atmospheric AdministrationSilver SpringMaryland20901USA

Correlation-coefficient fields are widely used in short-term climate prediction research. The most frequently used significance test method for the correlation-coefficient field was proposed by Livezey, in which the number of significantcorrelation lattice(station) points on the correlation coherence map is used as the statistic. However, the method is based on two assumptions:(1) the spatial distribution of the lattice(station) points is uniform;and(2) there is no correlation between the physical quantities in the correlation-coefficient field. However, in reality, the above two assumptions are not valid.Therefore, we designed a more reasonable method for significance testing of the correlation-coefficient field. Specifically, a new statistic, the significant-correlation area, is introduced to eliminate the inhomogeneity of the grid(station)-point distribution, and an empirical Monte Carlo method is employed to eliminate the spatial correlation of the matrix.Subsequently, the new significance test was used for simultaneous correlation-coefficient fields between intensities of the atmospheric activity center in the Northern Hemisphere and temperature/precipitation in China. The results show that the new method is more reasonable than the Livezey method.

关键词： correlation-coefficient field significant-correlation area empirical Monte Carlo method significance test

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An Intercomparison of Rules for testing the significance of Coupled Modes of Singular Value Decomposition Analysis

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Advances in Atmospheric Sciences 2007年第2期24卷 199-212页

作者：李芳曾庆存 Institute of Atmospheric Physics Chinese Academy of Sciences

This paper clarifies the essence of the significance test of singular value decomposition analysis （SVD）, and investigates four rules for testing the significance of coupled modes of SVD, including parallel analysis, nonparametric bootstrap, random-phase test, and a new rule named modified parallel analysis. A numerical experiment is conducted to quantitatively compare the performance of the four rules in judging whether a coupled mode of SVD is significant as parameters such as the sample size, the number of grid points, and the signal-to-noise ratio vary. The results show that the four rules perform better with lower ratio of the number of grid points to sample size. Modified parallel analysis and nonparametric bootstrap perform best to abandon the spurious coupled modes, but the latter is better than the former to retain the significant coupled modes when the sample size is not much larger than the number of grid points. Parallel analysis and random-phase test are robust to abandon the spurious coupled modes only when either （1） the observations at the grid points are spatially uncorrelated, or （2） the coupled signal is very strong for parallel analysis and is not weak for random-phase test. The reasons affecting the accuracy of the test rules are discussed.

关键词： singular value decomposition analysis significance test

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Seasonal variability of the Kuroshio Current at the PN Section in the East China Sea based on in-situ observation from 1987 to 2010

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Acta Oceanologica Sinica 2015年第5期34卷 12-21页

作者： WEI Yanzhou PEI Yuhua ZHANG Rong-Hua Key Laboratory of Ocean Circulation and Wave Institute of OceanologyChinese Academy of Sciences University of the Chinese Academy of Sciences State Key Laboratory of Satellite Ocean Environment Dynamics Second Institute of OceanographyState Oceanic Administration College of Physical and Environmental Oceanography Ocean University of China

As the spatio-temporal variability of the Kuroshio is highly influenced by mesoscale eddies, representing its seasonal variability characteristics requires sufficiently long term observations to reduce the uncertainties. Geostrophic velocity data estimated from hydrographic observation from 1987 to 2010 and the shipboard ADCP velocity data from 1993 to 2008 at the PN Section in the central East China Sea are collected to view the seasonal variability objectively. From both types of observation, it is found that the seasonal climatology mean of the Kuroshio Current exhibits significant difference in three areas, which are located at the Kuroshio Current core and its two flanks in a shallow layer less than 300 m, with the weakest northeast current at the core in autumn, the strongest counter current on the right flank in spring, and the strongest northeast current on the left flank in autumn, respectively. The seasonal variance of the Kuroshio Current also exhibits significant difference on the off- shore side of the Kuroshio, with larger variance in spring and summer while smaller variance in autumn and winter. For the current parallel to the PN Section, the ratio of the seasonal variability component to the intraseasonal variability component is relatively smaller than that for the current perpendicular to the PN Section. Further analyses indicate that the seasonal variability at the PN Section is tightly linked to the upstream and downstream current variability.

关键词： Kuroshio Current seasonal variability East China Sea significance test

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Construction of Multiple Linear Regression Prediction Model of PRETCO-A Scores and Its Positive Backwash Effect on Teaching

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国际计算机前沿大会会议论文集 2021年第2期 491-502页

作者： Haiyun Han Yuanhui Li Sanya Aviation&Tourism College Sanya Hainan 572000China

PRETCO-A is a standardized English proficiency test set up to evaluate the English application ability of the students in higher vocational college.In order to improve the passing rate,a multiple linear regression prediction model is constructed in this paper.A significance test was first performed on the regression model and the regression coefficient to verify a high correlation among the variables.The confirmed model was then put into application to predict the students’scores and identify the students who may fail the exam,leading to targeted tutoring assistance given to those students in advance.Finally,60 students with predicted scores lower than 60 points were selected as research samples,and randomly divided into the control group and the experimental group,30 students in each group.Finally,the experimental group students were given 40 teaching hours of precision assistance and targeted training,while the control group did not engage in any teaching intervention.The experimental results indicate that the pass rate of experimental group is 20%higher than the control group,which means the backwash effect of the test prediction is positive.The prediction model is proved to be scientific and reliable for teaching.

关键词： PRETCO-A Multiple linear regression Prediction model significance test Backwash effect Targeted teaching assistance

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