Seasonal prediction of summer rainfall over the Yangtze River valley（YRV） is valuable for agricultural and industrial production and freshwater resource management in China, but remains a major challenge. Earlier mu...Seasonal prediction of summer rainfall over the Yangtze River valley（YRV） is valuable for agricultural and industrial production and freshwater resource management in China, but remains a major challenge. Earlier multi-model ensemble（MME） prediction schemes for summer rainfall over China focus on single-value prediction, which cannot provide the necessary uncertainty information, while commonly-used ensemble schemes for probability density function（PDF） prediction are not adapted to YRV summer rainfall prediction. In the present study, an MME PDF prediction scheme is proposed based on the ENSEMBLES hindcasts. It is similar to the earlier Bayesian ensemble prediction scheme, but with optimization of ensemble members and a revision of the variance modeling of the likelihood function. The optimized ensemble members are regressed YRV summer rainfall with factors selected from model outputs of synchronous 500-h Pa geopotential height as predictors. The revised variance modeling of the likelihood function is a simple linear regression with ensemble spread as the predictor. The cross-validation skill of 1960–2002 YRV summer rainfall prediction shows that the new scheme produces a skillful PDF prediction, and is much better-calibrated, sharper, and more accurate than the earlier Bayesian ensemble and raw ensemble.展开更多
This study utilizes daily Asian Precipitation–Highly-Resolved Observational Data Integration Towards Evaluation（APHRODITE）gridded rainfall and the U.S.National Centers for Environmental PredictionDepartment of Ener...This study utilizes daily Asian Precipitation–Highly-Resolved Observational Data Integration Towards Evaluation（APHRODITE）gridded rainfall and the U.S.National Centers for Environmental PredictionDepartment of Energy reanalysis II products to examine the intraseasonal oscillations（ISOs）of rainfall over Eastern China during each summer of 1996,2002,and 2006.These three cases represent three typical spatial patterns of intraseasonal rainfall anomalies over Eastern China,with the strongest intraseasonal rainfall occurring over the middle and lower Yangtze Basin,southern Yangtze Basin,and Southeast China,respectively.The intraseasonal rainfall anomalies over Eastern China are dominated by both 30–60-and 10–20-day ISOs in each summer and are further modulated by the boreal summer ISOs（BSISOs）over the entire Asian summer monsoon region.The objective of this study is thus to apply the Bayesian wavelet-banding（WB）scheme to predicting intraseasonal rainfall over Eastern China.Several key factors associated with BSISOs are selected as predictors to experimentally develop a 15-day-lead statistical forecast.The forecast results show promise for the intraseasonal rainfall anomalies over Eastern China.Correlations generally greater than or equal to 0.6 are noted between the observed and predicted ISOs of rainfall over the major intraseasonal activity centers during each of the three summers.Such a high forecasting skill on intraseasonal timescales over various areas in Eastern China demonstrates the general usefulness of the WB scheme.展开更多
This article considers estimation of the unknown parameters for the compound Rayleigh distribution (CRD) based on a new life test plan called a progressive first failure-censored plan introduced by Wu and Kus (2009). ...This article considers estimation of the unknown parameters for the compound Rayleigh distribution (CRD) based on a new life test plan called a progressive first failure-censored plan introduced by Wu and Kus (2009). We consider the maximum likelihood and Bayesian inference of the unknown parameters of the model, as well as the reliability and hazard rate functions. This was done using the conjugate prior for the shape parameter, and discrete prior for the scale parameter. The Bayes estimators hav been obtained relative to both symmetric (squared error) and asymmetric (LINEX and general entropy (GE)) loss functions. It has been seen that the symmetric and asymmetric Bayes estimators are obtained in closed forms. Also, based on this new censoring scheme, approximate confidence intervals for the parameters of CRD are developed. A practical example using real data set was used for illustration. Finally, to assess the performance of the proposed estimators, some numerical results using Monte Carlo simulation study were reported.展开更多
In this paper, based on a new type of censoring scheme called an adaptive type-II progressive censoring scheme introduce by Ng et al. [1], Naval Research Logistics is considered. Based on this type of censoring the ma...In this paper, based on a new type of censoring scheme called an adaptive type-II progressive censoring scheme introduce by Ng et al. [1], Naval Research Logistics is considered. Based on this type of censoring the maximum likelihood estimation (MLE), Bayes estimation, and parametric bootstrap method are used for estimating the unknown parameters. Also, we propose to apply Markov chain Monte Carlo (MCMC) technique to carry out a Bayesian estimation procedure and in turn calculate the credible intervals. Point estimation and confidence intervals based on maximum likelihood and bootstrap method are also proposed. The approximate Bayes estimators obtained under the assumptions of non-informative priors, are compared with the maximum likelihood estimators. Numerical examples using real data set are presented to illustrate the methods of inference developed here. Finally, the maximum likelihood, bootstrap and the different Bayes estimates are compared via a Monte Carlo simulation study.展开更多
基金co-supported by the National Natural Science Foundation (Grant Nos. 41005052 and 41375086)the Strategic Priority Research Program of the Chinese Academy of Sciences (Grant No. XDA05110201)the National Basic Research Program of China (Grant No. 2010CB950403)
文摘Seasonal prediction of summer rainfall over the Yangtze River valley（YRV） is valuable for agricultural and industrial production and freshwater resource management in China, but remains a major challenge. Earlier multi-model ensemble（MME） prediction schemes for summer rainfall over China focus on single-value prediction, which cannot provide the necessary uncertainty information, while commonly-used ensemble schemes for probability density function（PDF） prediction are not adapted to YRV summer rainfall prediction. In the present study, an MME PDF prediction scheme is proposed based on the ENSEMBLES hindcasts. It is similar to the earlier Bayesian ensemble prediction scheme, but with optimization of ensemble members and a revision of the variance modeling of the likelihood function. The optimized ensemble members are regressed YRV summer rainfall with factors selected from model outputs of synchronous 500-h Pa geopotential height as predictors. The revised variance modeling of the likelihood function is a simple linear regression with ensemble spread as the predictor. The cross-validation skill of 1960–2002 YRV summer rainfall prediction shows that the new scheme produces a skillful PDF prediction, and is much better-calibrated, sharper, and more accurate than the earlier Bayesian ensemble and raw ensemble.
基金jointly supported by the National Basic Research Program of China[grant numbers 2014CB953902,2012CB417203,and 2012CB955202]the Priority Research Program of the Chinese Academy of Sciences[grant number XDA11010402]+2 种基金the National Natural Science Foundation of China[grant numbers 4117505941375087and 91537103]
文摘This study utilizes daily Asian Precipitation–Highly-Resolved Observational Data Integration Towards Evaluation（APHRODITE）gridded rainfall and the U.S.National Centers for Environmental PredictionDepartment of Energy reanalysis II products to examine the intraseasonal oscillations（ISOs）of rainfall over Eastern China during each summer of 1996,2002,and 2006.These three cases represent three typical spatial patterns of intraseasonal rainfall anomalies over Eastern China,with the strongest intraseasonal rainfall occurring over the middle and lower Yangtze Basin,southern Yangtze Basin,and Southeast China,respectively.The intraseasonal rainfall anomalies over Eastern China are dominated by both 30–60-and 10–20-day ISOs in each summer and are further modulated by the boreal summer ISOs（BSISOs）over the entire Asian summer monsoon region.The objective of this study is thus to apply the Bayesian wavelet-banding（WB）scheme to predicting intraseasonal rainfall over Eastern China.Several key factors associated with BSISOs are selected as predictors to experimentally develop a 15-day-lead statistical forecast.The forecast results show promise for the intraseasonal rainfall anomalies over Eastern China.Correlations generally greater than or equal to 0.6 are noted between the observed and predicted ISOs of rainfall over the major intraseasonal activity centers during each of the three summers.Such a high forecasting skill on intraseasonal timescales over various areas in Eastern China demonstrates the general usefulness of the WB scheme.
文摘This article considers estimation of the unknown parameters for the compound Rayleigh distribution (CRD) based on a new life test plan called a progressive first failure-censored plan introduced by Wu and Kus (2009). We consider the maximum likelihood and Bayesian inference of the unknown parameters of the model, as well as the reliability and hazard rate functions. This was done using the conjugate prior for the shape parameter, and discrete prior for the scale parameter. The Bayes estimators hav been obtained relative to both symmetric (squared error) and asymmetric (LINEX and general entropy (GE)) loss functions. It has been seen that the symmetric and asymmetric Bayes estimators are obtained in closed forms. Also, based on this new censoring scheme, approximate confidence intervals for the parameters of CRD are developed. A practical example using real data set was used for illustration. Finally, to assess the performance of the proposed estimators, some numerical results using Monte Carlo simulation study were reported.
文摘In this paper, based on a new type of censoring scheme called an adaptive type-II progressive censoring scheme introduce by Ng et al. [1], Naval Research Logistics is considered. Based on this type of censoring the maximum likelihood estimation (MLE), Bayes estimation, and parametric bootstrap method are used for estimating the unknown parameters. Also, we propose to apply Markov chain Monte Carlo (MCMC) technique to carry out a Bayesian estimation procedure and in turn calculate the credible intervals. Point estimation and confidence intervals based on maximum likelihood and bootstrap method are also proposed. The approximate Bayes estimators obtained under the assumptions of non-informative priors, are compared with the maximum likelihood estimators. Numerical examples using real data set are presented to illustrate the methods of inference developed here. Finally, the maximum likelihood, bootstrap and the different Bayes estimates are compared via a Monte Carlo simulation study.
