基于梯度依赖OI的全球多参数Argo数据集的构建与验证-海洋预报

基于梯度依赖OI的全球多参数Argo数据集的构建与验证

单位：1. 上海海洋大学海洋科学学院, 上海 201306;
2. 自然资源部海洋生态监测与修复技术重点实验室, 上海 201306;
3. 自然资源部第二海洋研究所, 浙江杭州 310012;
4. 卫星海洋环境动力学国家重点实验室, 浙江杭州 310012

关键词：梯度依赖最优插值 Argo 多参数网格化数据集客观分析

分类号：P715.2

出版年·卷·期（页码）：2023·40·第二期（77-88）

摘要：

利用Argo资料，基于梯度依赖最优插值客观分析系统，重构2004—2020年空间分辨率为1°×1°的全球多参数Argo网格化数据集，并通过置信区间估计、实测数据检验、与其他数据集对比等方式，对该数据集进行一系列的验证。结果表明：重构的Argo数据集在95%的统计概率下，90%以上的温度、盐度重构结果可信，且与实测数据的温度、盐度最大偏差不超过±1.0℃、±0.02。该数据集所反映的大尺度信号与现有数据集一致，并且可以保留较多中小尺度信号，分析结果与实际观测更接近。

Based on the gradient-dependent optimal interpolation objective analysis system, only, a global multiparameter Argo gridded dataset with a spatial resolution of 1°×1° from 2004 to 2020 is constructed using the Argo observation in this paper. A series of validations are made for this dataset including confidence interval estimation, observation inspection and comparison with other datasets. The results show that more than 90% of the reconstructed temperature and salinity are reliable under the statistical probability of 95% with the maximum bias from observations are less than ±1.0℃ and ±0.02, respectively. The large-scale signals reflected in this dataset are consistent with the existing datasets, and more small and medium-scale signals can be retained. The analysis results are closer to the observations.

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