中文摘要：

基于南船座数据的最佳的插值目的分析，改进被做到广泛地在数据吸收和客观分析系统使用的一个背景错误协变性矩阵的实验公式。明确地，关联的一个评价可伸缩那能有效地改善南船座客观分析的精确性被开发了。这个方法能自动地适应一个变量的坡度变化并且被叫作坡度依赖者关联规模方法。它南船座客观分析上的效果与 Gaussian 脉搏和光谱分析理论上被验证。一个维的模拟实验的结果证明坡度依赖者关联规模能改进客观分析系统的适应性，为充分吸收在有更大的海洋学的坡度的区域的观察的短波信息的分析计划使它可能。新计划在太平洋被用于南船座数据目的分析系统。结果显然被改进。

英文摘要：

Based on the optimal interpolation objective analysis of the Argo data, improvements are made to the em- pirical formula of a background error covariance matrix widely used in data assimilation and objective anal- ysis systems. Specifically, an estimation of correlation scales that can improve effectively the accuracy of Ar- go objective analysis has been developed. This method can automatically adapt to the gradient change of a variable and is referred to as ＂gradient-dependent correlation scale method＂. Its effect on the Argo objective analysis is verified theoretically with Gaussian pulse and spectrum analysis. The results of one-dimensional simulation experiment show that the gradient-dependent correlation scales can improve the adaptability of the objective analysis system, making it possible for the analysis scheme to fully absorb the shortwave information of observation in areas with larger oceanographic gradients. The new scheme is applied to the Argo data obiective analysis system in the Pacific Ocean. The results are obviously improved.