剖面浮标与表层漂流浮标的统计海洋学
Statistical Oceanography of Profiling Floats and Surface Drifters
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中文总结 AI 辅助
本文聚焦全球海洋观测系统中剖面浮标与表层漂流浮标数据的统计处理，综述近年时空模型，并详述欧拉与拉格朗日两种参考系的适用性，以应对物理海洋学中的统计挑战。
中文摘要 AI 辅助
全球海洋观测系统对于理解海洋变率和气候变化至关重要。海洋广阔无垠，各种仪器和系统在时间和空间上对海洋的采样往往稀疏且不规则。统计模型，尤其是时空模型，有助于从稀疏的海洋观测中实现推断、预报和决策。本文聚焦于全球海洋观测系统中两类原位观测数据的统计处理：来自剖面浮标和表层漂流浮标的数据，重点关注Argo计划和全球漂流浮标计划。我们描述了近年来针对这些数据开发的时空模型，以呈现现代物理海洋学中所面临的统计挑战。在建模此类数据时，我们将详细讨论两种参考系：欧拉参考系和拉格朗日参考系，前者更适用于剖面浮标，后者更适用于表层漂流浮标。
英文摘要
The Global Ocean Observing System is key to understanding oceanic variability and climate change. The ocean is vast and often sparsely and irregularly sampled in time and space by various instruments and systems. Statistical models, especially spatio-temporal ones, are useful for enabling inferences, forecasts and decisions from sparse oceanic observations. This article focuses on the statistical treatment of two types of in situ observations in the Global Ocean Observing System: from profiling floats and surface drifters, focusing on the Argo and Global Drifter Programs. We describe the spatio-temporal models that have been developed in recent years for these data, to give a picture of the statistical challenges faced in modern physical oceanography. We will discuss in detail two types of reference frames when modeling such data: Eulerian and Lagrangian, the former of which is more appropriate for profiling floats and the latter for surface drifters.
发表机构
- Carnegie Mellon University（卡内基梅隆大学）
- École Polytechnique Fédérale de Lausanne（洛桑联邦理工学院）
- Imperial College London（帝国理工学院）
机构由 AI 辅助整理，请以论文原文为准。
