本文总结了近年来条件非线性最优扰动方法的应用研究的主要进展.主要包括四个方面:(1)将条件非线性最优扰动(CNOP)方法拓展到既考虑初始扰动又考虑模式参数扰动,形成了拓展的CNOP方法.拓展的CNOP方法不仅能够应用于研究分别由初始误差和模式参数误差导致的可预报性问题,而且能够用于探讨初始误差和模式参数误差同时存在的情形；(2)将拓展的CNOP方法分别应用于ENSO和黑潮可预报性研究,考察了初始误差和模式参数误差对其可预报性的影响,揭示了初始误差在导致ENSO和黑潮大弯曲路径预报不确定性中的重要作用；(3)考察了阻塞事件发生的最优前期征兆(OPR)以及导致其预报不确定性的最优增长初始误差(OGR),揭示了OPR和OGR空间模态及其演变机制的相似性及其局地性特征；(4)研究了台风预报的目标观测问题,用CNOP方法确定了台风预报的目标观测敏感区,通过观测系统模拟试验(OSSEs)和/或观测系统试验(OSEs),表明了CNOP敏感区在改进台风预报中的有效性.具体地,台风OGR的主要误差分布在某一特定区域,空间分布具有明显的局地性特征,在台风OGR的局地性区域增加观测,有效改进了台风的预报技巧,该区域代表了台风预报的初值敏感区.事实上,上述El Ni(n)o事件、黑潮路径变异以及阻塞事件的OGR的空间分布也具有明显的局地性特征,这些事件的OGR刻画的局地性区域可能也代表了初值敏感区.%This paper summarizes the applications of conditional nonlinear optimal perturbation (CNOP) in recent predictability studies. These include four main contributions. First, the CNOP approach was extended to consider not only initial perturbations but also model parametric perturbations. The extended CNOP approach can study the predictability problems induced by either initial errors or model errors as well as those induced by both initial errors and model errors. Second, the extended CNOP approach was applied to the predictability studies of ENSO events and Kuroshio path anomalies. The effect of initial errors and model parametric errors on the predictability of these events was demonstrated and it was shown that the initial errors play a dominant role in the predictability of ENSO and the Kuroshio path anomalies. The CNOP approach was also applied to investigate the optimal precursors (OPRs) of the onset of blocking events and optimally growing initial errors (OGRs). The results demonstrated that OPRs and OGRs are often concentrated at a localized region; furthermore, their patterns are very similar. Finally, the CNOP approach was used to study adaptive observations of typhoons. With the CNOP, the sensitive areas of some typhoon cases were determined and with the data from the Observing-Systems Simulation Experiments (OSSEs) and/or Observing-Systems Experiments (OSEs) the validity of the sensitive area was demonstrated. Specifically, the OGRs of typhoon cases often concentrate in a particular region. Increasing the number of the observations in this region may significantly improve the forecasting skill for typhoons. The region identified by OGRs may represent the sensitive area of typhoon forecasting. The OGRs of El Nino events, Kuroshio path anomalies, and blocking events are also localized in a particular region. Based on the approach of the typhoon adaptive observation, the sensitive areas associated with these events may be identified as the localized regions of the OGRs.
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