[ Objective] The multiple mean generational function （MMGF） method was applied to forecast the annual number of typhoons （TYs） over the Western North Pacific （WNP）. [Method]The method yields a number of predicto...[ Objective] The multiple mean generational function （MMGF） method was applied to forecast the annual number of typhoons （TYs） over the Western North Pacific （WNP）. [Method]The method yields a number of predictors by mean generational function based on the rolling 50- year data of TYs frequency and sunspot number, and was repeated to generate forecasts year after year by optimal subset regression. [ Result] The results showed a reasonably high predictive ability dudng period 2000 -2010, with an average root mean square （RMSE） value of 1.92 and a mean absolute error （MAE） value of 1.64. [ Conclusion] Although the MMGF method needs further validation in the practical operation, it already has strong potential for the improvement of skill at forecasting annual frequency of TYs in the WNP.展开更多
We study the high order equilibrium distributions of a counting random variable. Properties such as moments, the probability generating function, the stop--loss transform and the mean residual lifetime, are derived. E...We study the high order equilibrium distributions of a counting random variable. Properties such as moments, the probability generating function, the stop--loss transform and the mean residual lifetime, are derived. Expressions are obtained for higher order equilibrium distribution functions under mixtures and convolutions of a counting distribution. Recursive formulas for higher order equilibrium distribution functions of the (a,b,0) -family of distributions are given.展开更多
This paper proposes a robust method of parameter estimation and data classification for multiple-structural data based on the linear error in variable（EIV） model.The traditional EIV model fitting problem is analyzed...This paper proposes a robust method of parameter estimation and data classification for multiple-structural data based on the linear error in variable（EIV） model.The traditional EIV model fitting problem is analyzed and a robust growing algorithm is developed to extract the underlying linear structure of the observed data.Under the structural density assumption,the C-step technique borrowed from the Rousseeuw＇s robust MCD estimator is used to keep the algorithm robust and the mean-shift algorithm is adopted to ensure a good initialization.To eliminate the model ambiguities of the multiple-structural data,statistical hypotheses tests are used to refine the data classification and improve the accuracy of the model parameter estimation.Experiments show that the efficiency and robustness of the proposed algorithm.展开更多
In this study the structure and seasonal variations of deep mean circulation in the East/lapan Sea （E/S） were numerically simulated using a mid-resolution ocean general circulation model with two different parameter...In this study the structure and seasonal variations of deep mean circulation in the East/lapan Sea （E/S） were numerically simulated using a mid-resolution ocean general circulation model with two different parameterizations for the eddy-topography interaction （ETI）. The strong deep mean circulations observed in the EIS are well reproduced when using the ETI parameterizations. The seasonal variability in the EIS deep layer is shown by using ETI parameterization based on the potential vorticity approach, while it is not shown in the statistical dynamical parameterization. The driving mechanism of the strong deep mean currents in the E/S are discussed by investigating the effects of model grids and parameterizations. The deep mean circulation is more closely related to the baroclinic process and potential vorticity than it is to the wind driven circulation.展开更多
基金Supported by the Natural Science Fund of Education Department of Anhui Province (KJ2012Z097)
文摘[ Objective] The multiple mean generational function （MMGF） method was applied to forecast the annual number of typhoons （TYs） over the Western North Pacific （WNP）. [Method]The method yields a number of predictors by mean generational function based on the rolling 50- year data of TYs frequency and sunspot number, and was repeated to generate forecasts year after year by optimal subset regression. [ Result] The results showed a reasonably high predictive ability dudng period 2000 -2010, with an average root mean square （RMSE） value of 1.92 and a mean absolute error （MAE） value of 1.64. [ Conclusion] Although the MMGF method needs further validation in the practical operation, it already has strong potential for the improvement of skill at forecasting annual frequency of TYs in the WNP.
文摘We study the high order equilibrium distributions of a counting random variable. Properties such as moments, the probability generating function, the stop--loss transform and the mean residual lifetime, are derived. Expressions are obtained for higher order equilibrium distribution functions under mixtures and convolutions of a counting distribution. Recursive formulas for higher order equilibrium distribution functions of the (a,b,0) -family of distributions are given.
基金supported by the National High Technology Research and Development Program of China (863 Program) (2007AA04Z227)
文摘This paper proposes a robust method of parameter estimation and data classification for multiple-structural data based on the linear error in variable（EIV） model.The traditional EIV model fitting problem is analyzed and a robust growing algorithm is developed to extract the underlying linear structure of the observed data.Under the structural density assumption,the C-step technique borrowed from the Rousseeuw＇s robust MCD estimator is used to keep the algorithm robust and the mean-shift algorithm is adopted to ensure a good initialization.To eliminate the model ambiguities of the multiple-structural data,statistical hypotheses tests are used to refine the data classification and improve the accuracy of the model parameter estimation.Experiments show that the efficiency and robustness of the proposed algorithm.
基金The Research Program on Climate Change Adaptation(RECCA)of the Ministry of Education,Culture,Sports,Science and Technology(MEXT)of Japan
文摘In this study the structure and seasonal variations of deep mean circulation in the East/lapan Sea （E/S） were numerically simulated using a mid-resolution ocean general circulation model with two different parameterizations for the eddy-topography interaction （ETI）. The strong deep mean circulations observed in the EIS are well reproduced when using the ETI parameterizations. The seasonal variability in the EIS deep layer is shown by using ETI parameterization based on the potential vorticity approach, while it is not shown in the statistical dynamical parameterization. The driving mechanism of the strong deep mean currents in the E/S are discussed by investigating the effects of model grids and parameterizations. The deep mean circulation is more closely related to the baroclinic process and potential vorticity than it is to the wind driven circulation.
