Identifying the sensitive areas in targeted observation for predicting the Kuroshio large meander path in a regional ocean model
Identifying the sensitive areas in targeted observation for predicting the Kuroshio large meander path in a regional ocean model
复制标题
区域海洋模型中黑潮大曲流路径预测定向观测中识别敏感区域
DOI：
10.1007/s13131-021-1838-7
复制
发表时间：
2022
影响因子：
1.4
通讯作者：
Mu Mu
中科院分区：
文献类型：
--
作者：
Liu Xia;Wang Qiang;Mu Mu
利用区域海洋模拟系统（ROMS），采用条件非线性最优扰动方法，对黑潮大弯曲路径预报中目标观测的敏感区域进行了研究。为了识别敏感区域，首先计算LM预测中非线性演化最大的最佳初始误差（OIE）;产生的OIE主要集中在LM上游区域上方2500 m处，其空间结构与最佳触发扰动有一定的相似性。根据这一空间结构，成功地识别出了位于九州东南部（29°-32 ° N，131°-134 ° E）的敏感区。一系列的敏感性实验表明，初始误差的位置和空间结构对LM预报有重要影响，验证了敏感区的有效性。然后，通过观测系统仿真实验，对敏感区域的目标观测效果进行了评估。在识别出的敏感区域实施有针对性的观测，有效地降低了预报误差，显著提高了LM事件的预报水平。这为海洋观测提供了科学指导，提高了LM事件的预报水平。
With the Regional Ocean Modeling System (ROMS), this paper investigates the sensitive areas in targeted observation for predicting the Kuroshio large meander (LM) path using the conditional nonlinear optimal perturbation approach. To identify the sensitive areas, the optimal initial errors (OIEs) featuring the largest nonlinear evolution in the LM prediction are first calculated; the resulting OIEs are localized mainly in the upper 2 500 m over the LM upstream region, and their spatial structure has certain similarities with that of the optimal triggering perturbation. Based on this spatial structure, the sensitive areas are successfully identified, located southeast of Kyushu in the region (29°–32°N, 131°–134°E). A series of sensitivity experiments indicate that both the positions and the spatial structure of initial errors have important effects on the LM prediction, verifying the validity of the sensitive areas. Then, the effect of targeted observation in the sensitive areas is evaluated through observing system simulation experiments. When targeted observation is implemented in the identified sensitive areas, the prediction errors are effectively reduced, and the prediction skill of the LM event is improved significantly. This provides scientific guidance for ocean observations related to enhancing the prediction skill of the LM event.