In the numerical prediction of weather or climate events,the uncertainty of the initial values and/or prediction models can bring the forecast result’s uncertainty.Due to the absence of true states,studies on this pr...In the numerical prediction of weather or climate events,the uncertainty of the initial values and/or prediction models can bring the forecast result’s uncertainty.Due to the absence of true states,studies on this problem mainly focus on the three subproblems of predictability,i.e.,the lower bound of the maximum predictable time,the upper bound of the prediction error,and the lower bound of the maximum allowable initial error.Aimed at the problem of the lower bound estimation of the maximum allowable initial error,this study first illustrates the shortcoming of the existing estimation,and then presents a new estimation based on the initial observation precision and proves it theoretically.Furthermore,the new lower bound estimations of both the two-dimensional ikeda model and lorenz96 model are obtained by using the cnop(conditional nonlinear optimal perturbation)method and a pso(particle swarm optimization)algorithm,and the estimated precisions are also analyzed.Besides,the estimations yielded by the existing and new formulas are compared;the results show that the estimations produced by the existing formula are often incorrect.展开更多
Using the outputs from CMCC-CM in CMIP5 experiments,the authors identified sensitive areas for targeted observations in ENSO forecasting from the perspective of the initial error growth(IEG)method and the particle fil...Using the outputs from CMCC-CM in CMIP5 experiments,the authors identified sensitive areas for targeted observations in ENSO forecasting from the perspective of the initial error growth(IEG)method and the particle filter(PF)method.Results showed that the PF targets areas over the central-eastern equatorial Pacific,while the sensitive areas determined by the IEG method are slightly to the east of the former.Although a small part of the areas targeted by the IEG method also lie in the southeast equatorial Pacific,this does not affect the large-scale overlapping of the sensitive areas determined by these two methods in the eastern equatorial Pacific.Therefore,sensitive areas determined by the two methods are mutually supportive.When considering the uncertainty of methods for determining sensitive areas in realistic targeted observation,it is more reasonable to choose the above overlapping areas as sensitive areas for ENSO forecasting.This result provides scientific guidance for how to better determine sensitive areas for ENSO forecasting.展开更多
Support vector regression (SVR) combined with particle swarm optimization (PSO) for its parameter optimization, was proposed to establish a model to predict the thermal conductivity of polymer-based composites under d...Support vector regression (SVR) combined with particle swarm optimization (PSO) for its parameter optimization, was proposed to establish a model to predict the thermal conductivity of polymer-based composites under different mass fractions of fillers (mass fraction of polyethylene (PE) and mass fraction of polystyrene (PS)). The prediction performance of SVR was compared with those of other two theoretical models of spherical packing and flake packing. The result demonstrated that the estimated errors by leave-one-out cross validation (LOOCV) test of SVR models, such as mean absolute error (MAE) and mean absolute percentage error (MAPE), all are smaller than those achieved by the two theoretical models via applying identical samples. It is revealed that the generalization ability of SVR model is superior to those of the two theoretical models. This study suggests that SVR can be used as a powerful approach to foresee the thermal property of polymer-based composites under different mass fractions of polyethylene and polystyrene fillers.展开更多
基金supported by the National Natural Science Foundation of China(Grant No.41331174)
文摘In the numerical prediction of weather or climate events,the uncertainty of the initial values and/or prediction models can bring the forecast result’s uncertainty.Due to the absence of true states,studies on this problem mainly focus on the three subproblems of predictability,i.e.,the lower bound of the maximum predictable time,the upper bound of the prediction error,and the lower bound of the maximum allowable initial error.Aimed at the problem of the lower bound estimation of the maximum allowable initial error,this study first illustrates the shortcoming of the existing estimation,and then presents a new estimation based on the initial observation precision and proves it theoretically.Furthermore,the new lower bound estimations of both the two-dimensional ikeda model and lorenz96 model are obtained by using the cnop(conditional nonlinear optimal perturbation)method and a pso(particle swarm optimization)algorithm,and the estimated precisions are also analyzed.Besides,the estimations yielded by the existing and new formulas are compared;the results show that the estimations produced by the existing formula are often incorrect.
基金supported by the National Natural Science Foundation of China [grant numbers 41930971,41775069,and 41975076]。
文摘Using the outputs from CMCC-CM in CMIP5 experiments,the authors identified sensitive areas for targeted observations in ENSO forecasting from the perspective of the initial error growth(IEG)method and the particle filter(PF)method.Results showed that the PF targets areas over the central-eastern equatorial Pacific,while the sensitive areas determined by the IEG method are slightly to the east of the former.Although a small part of the areas targeted by the IEG method also lie in the southeast equatorial Pacific,this does not affect the large-scale overlapping of the sensitive areas determined by these two methods in the eastern equatorial Pacific.Therefore,sensitive areas determined by the two methods are mutually supportive.When considering the uncertainty of methods for determining sensitive areas in realistic targeted observation,it is more reasonable to choose the above overlapping areas as sensitive areas for ENSO forecasting.This result provides scientific guidance for how to better determine sensitive areas for ENSO forecasting.
基金supported by the Program for New Century Excellent Talents in University of China (Grant No. NCET-07-0903)the Scientific Research Foundation for the Returned Overseas Chinese Scholars of Ministry of Education, China (Grant No. 2008101-1)+2 种基金the Fundamental Research Funds for the Central Universities (Grant Nos. CDJXS10101107, CDJXS10100037)the Natural Science Foundation of Chongqing, China (Grant No. CSTC2006BB5240)the Innovative Talent Training Project of the Third Stage of "211 Project", Chongqing University (Grant No. S-09109)
文摘Support vector regression (SVR) combined with particle swarm optimization (PSO) for its parameter optimization, was proposed to establish a model to predict the thermal conductivity of polymer-based composites under different mass fractions of fillers (mass fraction of polyethylene (PE) and mass fraction of polystyrene (PS)). The prediction performance of SVR was compared with those of other two theoretical models of spherical packing and flake packing. The result demonstrated that the estimated errors by leave-one-out cross validation (LOOCV) test of SVR models, such as mean absolute error (MAE) and mean absolute percentage error (MAPE), all are smaller than those achieved by the two theoretical models via applying identical samples. It is revealed that the generalization ability of SVR model is superior to those of the two theoretical models. This study suggests that SVR can be used as a powerful approach to foresee the thermal property of polymer-based composites under different mass fractions of polyethylene and polystyrene fillers.
