秋季移动大棚无公害黄瓜生产技术

文章从品种选择、播前准备、播种育苗、田间管理及病虫害防治等方面介绍了秋季移动大棚无公害黄瓜生产技术.     

Applications of nonlinear optimization methods to quantifying the predictability of a numerical model for El Nino-Southern Oscillation

The nonlinear optimization methods are applied to quantify the predictability of a numerical model for El Nino-Southern Oscillation (ENSO). We establish a lower bound of maximum predictability time for the model ENSO events (i.e. ENSO events in the numerical model), an upper bound of maximum prediction error, and a lower bound of maximum allowable initial error, all of which potentially quantify the predictability of model ENSO. Numerical results reveal the phenomenon of “spring predictability barrier” (SPB) for ENSO event and support the previous views on SPB. Additionally, we also explore the differences between the linear evolution of prediction error and its nonlinear counterpart. The results demonstrate the limitation of linear estimation of prediction error. All these above results suggest that the nonlinear optimization method is one of the useful tools of quantifying the predictability of the numerical model for ENSO.     

Conditional nonlinear optimal perturbation and its applications to the studies of weather and climate predictability

Conditional nonlinear optimal perturbation （CNOP） is the initial perturbation that has the largest nonlinear evolution at prediction time for initial perturbations satisfying certain physical constraint condition. It does not only represent the optimal precursor of certain weather or climate event, but also stand for the initial error which has largest effect on the prediction uncertainties at the prediction time. In sensitivity and stability analyses of fluid motion, CNOP also describes the most unstable （or most sensitive） mode. CNOP has been used to estimate the upper bound of the prediction error. These physical characteristics of CNOP are examined by applying respectively them to ENSO predictability studies and ocean＇s thermohaline circulation （THC） sensitivity analysis. In ENSO predictability studies, CNOP, rather than linear singular vector （LSV）, represents the initial patterns that evolve into ENSO events most potentially, i.e. the optimal precursors for ENSO events. When initial perturbation is considered to be the initial error of ENSO, CNOP plays the role of the initial error that has largest effect on the prediction of ENSO. CNOP also derives the upper bound of prediction error of ENSO events. In the THC sensitivity and stability studies, by calculating the CNOP （most unstable perturbation） of THC, it is found that there is an asymmetric nonlinear response of ocean＇s THC to the finite amplitude perturbations. Finally, attention is paid to the feasibility of CNOP in more complicated model. It is shown that in a model with higher dimensions, CNOP can be computed successfully. The corresponding optimization algorithm is also shown to be efficient.     

A new approach to studying ENSO predictability： Conditional nonlinear optimal perturbation

A new approach, the conditional nonlinear optimal perturbation (CNOP) is introduced to study the predictability of El Nifio-Southern Oscillation (ENSO) using a theoretical coupled ocean-atmosphere model. The differences between CNOP and linear singular vector (LSV) are demonstrated. The results suggest that the nonlinear model and CNOP are superior in determining error growth for studying predictability of the ENSO. In particular, the CNOP approach is used to explore the nature of the ‘spring predictability barrier‘ in ENSO prediction.