基于Argo剖面浮标观测的海洋溶解氧空间-质量迭代插值方法

An Iterative Space-quality Interpolation Method for Marine Dissolved Oxygen Data Observed by Argo Floats

溶解氧是探究海洋内部各种生物地球化学循环过程以及评估海洋健康的重要参数。目前,基于Argo剖面浮标的观测方式已经成为获取海洋溶解氧信息的主要来源之一,但是这种方式获取到的海洋溶解氧资料在空间上是离散分布的。如何利用空间插值方法将离散的Argo溶解氧剖面观测数据构建成连续的空间网格数据是当前研究关注的热点,本文提出了一种面向Argo溶解氧剖面观测数据的空间-质量特征的迭代插值方法(ASOSQIM)。针对观测样点空间上分布离散、不规则的特点,ASOSQIM首先对溶解氧观测数据进行空间初始化来构建初始背景场,然后对Argo溶解氧剖面观测样点进行空间异质性分区,采用空间-质量定权的方式赋予不同质量标示数据相应的权重系数来解决参与插值的数据量及其精度问题,最后利用迭代插值的思想并集成溶解氧空间分区特征和质量标识,实现了基于Argo剖面浮标观测的溶解氧浓度空间插值。利用交叉验证法和WOA18资料对ASOSQIM进行方法验证和对比分析,结果表明:(1)ASOSQIM的插值精度相比于其他空间插值方法具有大幅度的提升,与2022年同期Argo浮标溶解氧观测结果进行对比,验证了该方法的优越性和可行性;(2)利用ASOSQIM得到的溶解氧浓度插值结果与WOA18溶解氧资料具有高度一致性,溶解氧浓度绝对误差在20μmol/kg范围内的区域占比达到85%以上,表明利用该方法得到的插值结果具有可靠性。

Dissolved oxygen is an important parameter for exploring various biogeochemical cycles in the ocean interior and assessing ocean health.At present,the observation method based on Argo floats has become one of the main sources to obtain the information of Marine dissolved oxygen,but the data obtained by this method are distributed discretely in space.How to use spatial interpolation method to construct discrete dissolved oxygen profiles of Argo floats into continuous spatial grid data is the focus of current research.Therefore,an iterative interpolation method(ASOSQIM)for spatial-quality characteristics of dissolved oxygen observation data using Argo floats is proposed in this paper.It is of great significance to integrate discrete Marine dissolved oxygen data to obtain spatially distributed continuous grid data for exploring and revealing the variation law of Marine dissolved oxygen concentration and providing reliable data support and information services for related fields.In view of the discrete and irregular spatial distribution of the observed sample points,ASOSQIM carries out a series of processing on the dissolved oxygen observation data using Argo floats,such as monthly statistics,quality recontrol and standardized interpolation.Based on the processed dissolved oxygen observation data,the spatial initialization was carried out,and the dissolved oxygen concentration on the regular grid after initialization was taken as the initial value of spatial interpolation calculation.Then,the dissolved oxygen observation data using Argo floats were divided into different spatial heterogeneity zones,and the corresponding weight coefficients were assigned to data of different quality flags by using the space-quality weighting method to solve the problem of the amount of data involved in interpolation and its accuracy.Finally,the idea of iterative interpolation was used to integrate the spatial partitioning characteristics and quality flags of dissolved oxygen.The spatial interpolation of dissolved oxygen concentration based on observation data of Argo floats is realized.The cross-validation method and WOA18 data were used to verify and compare ASOSQIM.The results show that:(1)the interpolation accuracy of ASOSQIM is significantly improved compared with other spatial interpolation methods.The advantages and feasibility of this method are verified by comparing with the dissolved oxygen observation results of Argo floats in 2022.(2)The dissolved oxygen concentration interpolation results obtained by ASOSQIM are highly consistent with the dissolved oxygen data of WOA18,and the absolute error of dissolved oxygen concentration accounts for more than 85%in the range of 20μmol/kg,indicating that the spatial interpolation results of dissolved oxygen concentration obtained by this method are reliable.