For the EOF decomposition continuation phase space, the least square method is applied under the condition of orthogonal basis to find coefficients of all quadratic nonlinear terms of a state evo- lution equation such...For the EOF decomposition continuation phase space, the least square method is applied under the condition of orthogonal basis to find coefficients of all quadratic nonlinear terms of a state evo- lution equation such that a dynamic system that indicates the evolution features of a weather/cli- mate system in a limited area can be formulated. The scheme is compared with that for phase space continuation by time series drift. Results show that the dynamic system established in terms of the present method is likely to give more precise and realistic description of evolution of the weather/ climate system.展开更多
This paper concerns the reconstruction of a dynamic system based on phase space continuation of monthly meantemperature iD time series and the assumption that the equation for the time-varying evolution of phase space...This paper concerns the reconstruction of a dynamic system based on phase space continuation of monthly meantemperature iD time series and the assumption that the equation for the time-varying evolution of phase space statevariables contains linear and nonlinear quadratic terms. followed by the fitting of the dataset subjected to continuation so as to get, by the least square method. the coefficients of the terms, of which those of greater variance contribution are retained for use. Results show that the obtained low-order system may be used to describe nonlinear properties of the short range climate variation shown by monthly mean temperature series.展开更多
This paper investigates the nonlinear prediction of monthly rainfall time series which consists of phase space continuation of one-dimensional sequence, followed by least-square determination of the coefficients for t...This paper investigates the nonlinear prediction of monthly rainfall time series which consists of phase space continuation of one-dimensional sequence, followed by least-square determination of the coefficients for the terms ofthe time-lag differential equation model and then fitting of the prognostic expression is made to 1951-1980 monthlyrainfall datasets from Changsha station. Results show that the model is likely to describe the nonlinearity of the allnual cycle of precipitation on a monthly basis and to provide a basis for flood prevention and drought combating forthe wet season.展开更多
基金This work is sponsored by the National Natural Science Foundation of Chinathe Natural Science Foundation of Jiangsu Province
文摘For the EOF decomposition continuation phase space, the least square method is applied under the condition of orthogonal basis to find coefficients of all quadratic nonlinear terms of a state evo- lution equation such that a dynamic system that indicates the evolution features of a weather/cli- mate system in a limited area can be formulated. The scheme is compared with that for phase space continuation by time series drift. Results show that the dynamic system established in terms of the present method is likely to give more precise and realistic description of evolution of the weather/ climate system.
文摘This paper concerns the reconstruction of a dynamic system based on phase space continuation of monthly meantemperature iD time series and the assumption that the equation for the time-varying evolution of phase space statevariables contains linear and nonlinear quadratic terms. followed by the fitting of the dataset subjected to continuation so as to get, by the least square method. the coefficients of the terms, of which those of greater variance contribution are retained for use. Results show that the obtained low-order system may be used to describe nonlinear properties of the short range climate variation shown by monthly mean temperature series.
文摘This paper investigates the nonlinear prediction of monthly rainfall time series which consists of phase space continuation of one-dimensional sequence, followed by least-square determination of the coefficients for the terms ofthe time-lag differential equation model and then fitting of the prognostic expression is made to 1951-1980 monthlyrainfall datasets from Changsha station. Results show that the model is likely to describe the nonlinearity of the allnual cycle of precipitation on a monthly basis and to provide a basis for flood prevention and drought combating forthe wet season.
