In this paper,the role of constant optimal forcing(COF) in correcting forecast models was numerically studied using the well-known Lorenz 63 model.The results show that when we only consider model error caused by para...In this paper,the role of constant optimal forcing(COF) in correcting forecast models was numerically studied using the well-known Lorenz 63 model.The results show that when we only consider model error caused by parameter error,which also changes with the development of state variables in a numerical model,the impact of such model error on forecast uncertainties can be offset by superimposing COF on the tendency equations in the numerical model.The COF can also offset the impact of model error caused by stochastic processes.In reality,the forecast results of numerical models are simultaneously influenced by parameter uncertainty and stochastic process as well as their interactions.Our results indicate that COF is also able to significantly offset the impact of such hybrid model error on forecast results.In summary,although the variation in the model error due to physical process is time-dependent,the superimposition of COF on the numerical model is an effective approach to reducing the influence of model error on forecast results.Therefore,the COF method may be an effective approach to correcting numerical models and thus improving the forecast capability of models.展开更多
A novel method has been designed and exploited to determine the thermal junction potential difference(TJPD) between two acids or alkalies of the same composition but with different temperature. The absolute value of m...A novel method has been designed and exploited to determine the thermal junction potential difference(TJPD) between two acids or alkalies of the same composition but with different temperature. The absolute value of measured TJPD between two strong acids(or alkalies) maintained at different temperatures increases with increasing of the temperature difference between the two electrolytes over the range from 0 to 40 °C. In strong acids, the hot end always has the lower potential while in strong alkalies, the cold end has the lower potential. This is because the ions of fast diffusion rate contribute most to the TJPD. Our results demonstrate the importance of the correction for TJPD in deriving the kinetic parameters when studying the temperature effect on reaction kinetics.展开更多
Semiparametric regression models and estimating covariance functions are very useful for longitudinal study. To heed the positive-definiteness constraint, we adopt the modified Cholesky decomposition approach to decom...Semiparametric regression models and estimating covariance functions are very useful for longitudinal study. To heed the positive-definiteness constraint, we adopt the modified Cholesky decomposition approach to decompose the covariance structure. Then the covariance structure is fitted by a semiparametric model by imposing parametric within-subject correlation while allowing the nonparametric variation function. We estimate regression functions by using the local linear technique and propose generalized estimating equations for the mean and correlation parameter. Kernel estimators are developed for the estimation of the nonparametric variation function. Asymptotic normality of the the resulting estimators is established. Finally, the simulation study and the real data analysis are used to illustrate the proposed approach.展开更多
基金sponsored by the National Basic Research Program of China(Grant No.2012CB955202)the Knowledge Innovation Program of the Chinese Academy of Sciences(Grant No.KZCX2-YW-QN203)the National Natural Science Foundation of China(Grant No.41176013)
文摘In this paper,the role of constant optimal forcing(COF) in correcting forecast models was numerically studied using the well-known Lorenz 63 model.The results show that when we only consider model error caused by parameter error,which also changes with the development of state variables in a numerical model,the impact of such model error on forecast uncertainties can be offset by superimposing COF on the tendency equations in the numerical model.The COF can also offset the impact of model error caused by stochastic processes.In reality,the forecast results of numerical models are simultaneously influenced by parameter uncertainty and stochastic process as well as their interactions.Our results indicate that COF is also able to significantly offset the impact of such hybrid model error on forecast results.In summary,although the variation in the model error due to physical process is time-dependent,the superimposition of COF on the numerical model is an effective approach to reducing the influence of model error on forecast results.Therefore,the COF method may be an effective approach to correcting numerical models and thus improving the forecast capability of models.
基金supported by the National Basic Research Program of China (2015CB932301)National Natural Science Foundation of China (21273215, 91545124)
文摘A novel method has been designed and exploited to determine the thermal junction potential difference(TJPD) between two acids or alkalies of the same composition but with different temperature. The absolute value of measured TJPD between two strong acids(or alkalies) maintained at different temperatures increases with increasing of the temperature difference between the two electrolytes over the range from 0 to 40 °C. In strong acids, the hot end always has the lower potential while in strong alkalies, the cold end has the lower potential. This is because the ions of fast diffusion rate contribute most to the TJPD. Our results demonstrate the importance of the correction for TJPD in deriving the kinetic parameters when studying the temperature effect on reaction kinetics.
基金supported by National Natural Science Foundation of China (GrantNos.10931002,10911120386)
文摘Semiparametric regression models and estimating covariance functions are very useful for longitudinal study. To heed the positive-definiteness constraint, we adopt the modified Cholesky decomposition approach to decompose the covariance structure. Then the covariance structure is fitted by a semiparametric model by imposing parametric within-subject correlation while allowing the nonparametric variation function. We estimate regression functions by using the local linear technique and propose generalized estimating equations for the mean and correlation parameter. Kernel estimators are developed for the estimation of the nonparametric variation function. Asymptotic normality of the the resulting estimators is established. Finally, the simulation study and the real data analysis are used to illustrate the proposed approach.
