中文摘要：

集合初始扰动的好坏对集合预报效果有着至关重要的影响.为了产生能够捕捉到初始场不确定性的初始扰动,在不考虑模式误差的情况下,用Lorenz-96模式探讨了条件非线性最优扰动（conditional nonlinear optimal perturbation,CNOP）在集合预报中的应用,并与传统的增长繁殖模方法（breeding of growing mode,BGM）进行了比较.发现用CNOPs代替2L范数较大的繁殖向量（breeding vectors,BVs）的集合预报技巧明显高于BGM方法.在分析误差是快速增长型扰动的条件下,新方法对预报结果的改善幅度随预报时间的延长而增大,特别是在中期预报范围（6～14 d）内,改善效果更加明显.按照通常距平相关系数（ACC）不小于0.6则视为有效预报的标准,新方法和BGM方法得到的集合预报相对于参照预报而言,都能够将有效预报时间延长4 d左右.

英文摘要：

Numerical forecasting can estimate the future state of the atmosphere or oceans. Because the atmosphere and oceans are complex and nonlinear systems, very small errors in the initial field can be amplified nonlinearly during integration of the numerical model, which may ultimately result in large errors. The true initial field cannot be obtained, and it is not possible to construct a perfect model that simulates the development of the atmospheric or oceanic state. Therefore, initial uncertainties and system instabilities result in large forecast errors. However, ensemble forecasting can be used to estimate the forecasting uncertainty. The ensemble initial perturbation has a crucial impact on the ensemble forecasting result. To obtain the initial perturbation that can capture initial field uncertainties and system instabilities, conditional nonlinear optimal perturbation（CNOP） is applied to generate initial perturbations for ensemble forecasting. CNOP is the initial perturbation that satisfies certain physical constraints and causes the largest forecast error at forecast time. More specifically, it is a generalization of the singular vector（SV） in nonlinear field. Using the Lorenz-96 model, we generate initial perturbations by combining CNOPs and breeding vectors（BVs）, and then compare it with the traditional breeding growing modes（BGM） method. The spectral projected gradient 2（SPG2） optimal algorithm is used to compute CNOPs based on basic states, and we sort the BVs according to the corresponding L2 norm magnitude. We design two ensemble samples for the forecast experiments： sample 1（S1） and sample 2（S2）. Ensemble initial perturbations all consist of BVs in S1. CNOPs and BVs are combined to form ensemble initial perturbations in S2; we use CNOPs to replace the several larger BVs in S1 according to the BVs＇ L2 norm magnitude order, and keep the remaining BVs unchanged. Both S1 and S2 are consist of 17 ensemble members, including one control forecast. Under the perfect model assumpt