The Ensemble Transform（ET） method has been shown to be useful in providing guidance for adaptive observation deployment.It predicts forecast error variance reduction for each possible deployment using its correspond...The Ensemble Transform（ET） method has been shown to be useful in providing guidance for adaptive observation deployment.It predicts forecast error variance reduction for each possible deployment using its corresponding transformation matrix in an ensemble subspace.In this paper,a new ET-based sensitivity（ETS） method,which calculates the gradient of forecast error variance reduction in terms of analysis error variance reduction,is proposed to specify regions for possible adaptive observations.ETS is a first order approximation of the ET;it requires just one calculation of a transformation matrix,increasing computational efficiency（60%-80%reduction in computational cost）.An explicit mathematical formulation of the ETS gradient is derived and described.Both the ET and ETS methods are applied to the Hurricane Irene（2011） case and a heavy rainfall case for comparison.The numerical results imply that the sensitive areas estimated by the ETS and ET are similar.However,ETS is much more efficient,particularly when the resolution is higher and the number of ensemble members is larger.展开更多
We consider the estimation of nonparametric regression models with predictors being measured with a mixture of Berkson and classical errors. In practice, the Berkson error arises when the variable X of interest is uno...We consider the estimation of nonparametric regression models with predictors being measured with a mixture of Berkson and classical errors. In practice, the Berkson error arises when the variable X of interest is unobservable and only a proxy of X can be measured while the inaccuracy related to the observation of the proxy causes an error of classical type. In this paper, we propose two nonparametric estimators of the regression function in the presence of either or both types of errors. We prove the asymptotic normality of our estimators and derive their rates of convergence. The finite-sample properties of the estimators are investigated through simulation studies.展开更多
High-resolution global non-hydrostatic gridded dynamic models have drawn significant attention in recent years in conjunction with the rising demand for improving weather forecasting and climate predictions.By far it ...High-resolution global non-hydrostatic gridded dynamic models have drawn significant attention in recent years in conjunction with the rising demand for improving weather forecasting and climate predictions.By far it is still challenging to build a high-resolution gridded global model,which is required to meet numerical accuracy,dispersion relation,conservation,and computation requirements.Among these requirements,this review focuses on one significant topic—the numerical accuracy over the entire non-uniform spherical grids.The paper discusses all the topic-related challenges by comparing the schemes adopted in well-known finite-volume-based operational or research dynamical cores.It provides an overview of how these challenges are met in a summary table.The analysis and validation in this review are based on the shallow-water equation system.The conclusions can be applied to more complicated models.These challenges should be critical research topics in the future development of finite-volume global models.展开更多
基金jointly sponsored by the Key Project of the Chinese National Programs for Fundamental Research and Development (“973 Program”, Grant No. 2013CB430106)the Key Project of the Chinese National Science & Technology Pillar Program during the Twelfth Five-year Plan Period (Grant No. 2012BAC22B01)
文摘The Ensemble Transform（ET） method has been shown to be useful in providing guidance for adaptive observation deployment.It predicts forecast error variance reduction for each possible deployment using its corresponding transformation matrix in an ensemble subspace.In this paper,a new ET-based sensitivity（ETS） method,which calculates the gradient of forecast error variance reduction in terms of analysis error variance reduction,is proposed to specify regions for possible adaptive observations.ETS is a first order approximation of the ET;it requires just one calculation of a transformation matrix,increasing computational efficiency（60%-80%reduction in computational cost）.An explicit mathematical formulation of the ETS gradient is derived and described.Both the ET and ETS methods are applied to the Hurricane Irene（2011） case and a heavy rainfall case for comparison.The numerical results imply that the sensitive areas estimated by the ETS and ET are similar.However,ETS is much more efficient,particularly when the resolution is higher and the number of ensemble members is larger.
文摘We consider the estimation of nonparametric regression models with predictors being measured with a mixture of Berkson and classical errors. In practice, the Berkson error arises when the variable X of interest is unobservable and only a proxy of X can be measured while the inaccuracy related to the observation of the proxy causes an error of classical type. In this paper, we propose two nonparametric estimators of the regression function in the presence of either or both types of errors. We prove the asymptotic normality of our estimators and derive their rates of convergence. The finite-sample properties of the estimators are investigated through simulation studies.
基金Supported by the National Key Research and Development Program of China(2017YFC1502201)Basic Scientific Research and Operation Fund of Chinese Academy of Meteorological Sciences(2017Z017)。
文摘High-resolution global non-hydrostatic gridded dynamic models have drawn significant attention in recent years in conjunction with the rising demand for improving weather forecasting and climate predictions.By far it is still challenging to build a high-resolution gridded global model,which is required to meet numerical accuracy,dispersion relation,conservation,and computation requirements.Among these requirements,this review focuses on one significant topic—the numerical accuracy over the entire non-uniform spherical grids.The paper discusses all the topic-related challenges by comparing the schemes adopted in well-known finite-volume-based operational or research dynamical cores.It provides an overview of how these challenges are met in a summary table.The analysis and validation in this review are based on the shallow-water equation system.The conclusions can be applied to more complicated models.These challenges should be critical research topics in the future development of finite-volume global models.
