论文标题
Argo海洋温度和盐度轮廓的3D双变量空间建模
3D Bivariate Spatial Modelling of Argo Ocean Temperature and Salinity Profiles
论文作者
论文摘要
随着海洋吸收大量太阳能，全球海洋中包含的变量可以检测并揭示变暖气候的影响。因此，有关海洋变量联合空间分布的信息对于理解气候至关重要。在本文中，我们使用ARGO计划收集的数据研究了海洋温度和盐度之间的空间依赖结构，并为覆盖海洋内部表面的数据构建了双变量空间模型。我们开发了在3维（3D）空间（经度$ \ times $ latitude $ \ times $ depth）中定义的灵活类的多元非组织协方差模型，允许方差和相关性随海洋深度而变化。这些模型描述了两个变量的关节空间分布，同时结合了海洋的基本垂直结构。我们将此框架应用于来自Argo Floats的温度和盐度数据。为了管理大量ARGO数据所带来的计算挑战，我们将Vecchia近似应用于可能性函数。我们证明，所提出的双变量协方差能够描述原始过程以及它们的一阶和二阶差异之间复杂的垂直交叉稳定结构，而现有的双变量模型（包括双变量Matérn），不太符合经验交叉互动结构。
Variables contained within the global oceans can detect and reveal the effects of the warming climate, as the oceans absorb huge amounts of solar energy. Hence, information regarding the joint spatial distribution of ocean variables is critical for understanding the climate. In this paper, we investigate the spatial dependence structure between ocean temperature and salinity using data harvested from the Argo program and construct a bivariate spatial model for the data that cover the surface to the ocean's interior. We develop a flexible class of multivariate nonstationary covariance models defined in 3-dimensional (3D) space (longitude $\times$ latitude $\times$ depth) that allow the variances and correlation to vary with ocean depth. These models describe the joint spatial distribution of the two variables while incorporating the underlying vertical structure of the ocean. We apply this framework to temperature and salinity data from Argo floats. To manage the computational challenges posed by the large volume of the Argo data, we apply the Vecchia approximation to the likelihood functions. We demonstrate that the proposed bivariate covariance is able to describe the complex vertical cross-covariance structure between the original processes as well as their first and second-order differenciations, while existing bivariate models, including bivariate Matérn, poorly fit the empirical cross-covariance structure.
