论文标题
气候观察网络中的桥接差距：通过数据驱动的随机模型对拉格朗日冰浮动测量的基于物理的非线性动态插值
Bridging Gaps in the Climate Observation Network: A Physics-based Nonlinear Dynamical Interpolation of Lagrangian Ice Floe Measurements via Data-Driven Stochastic Models
论文作者
论文摘要
边缘冰区的建模和理解海冰动力学依赖于获得拉格朗日冰浮标的测量。但是，光学卫星图像易受大气噪声的影响，从而导致浮动位置的时间序列差距。本文提出了一个有效且统计上准确的非线性动力学插值框架，用于恢复缺失的浮点观测。它利用了平衡的基于物理和数据驱动的结构，以解决耦合大气 - 冰山系统的高维和非线性性质所带来的挑战，其中有效降低了降级的随机模型，非线性数据同化以及同时参数估计是系统地集成的。新方法成功地恢复了Beaufort Sea中缺失浮标的相关位置，曲线，角位移以及相关的强烈非高斯分布。它还可以准确地估计厚度，并通过适当的不确定性定量恢复未观察到的基础海场，从而促进了我们对北极气候的理解。
Modeling and understanding sea ice dynamics in marginal ice zones relies on acquiring Lagrangian ice floe measurements. However, optical satellite images are susceptible to atmospheric noise, leading to gaps in the retrieved time series of floe positions. This paper presents an efficient and statistically accurate nonlinear dynamical interpolation framework for recovering missing floe observations. It exploits a balanced physics-based and data-driven construction to address the challenges posed by the high-dimensional and nonlinear nature of the coupled atmosphere-ice-ocean system, where effective reduced-order stochastic models, nonlinear data assimilation, and simultaneous parameter estimation are systematically integrated. The new method succeeds in recovering the locations, curvatures, angular displacements, and the associated strong non-Gaussian distributions of the missing floes in the Beaufort Sea. It also accurately estimates floe thickness and recovers the unobserved underlying ocean field with an appropriate uncertainty quantification, advancing our understanding of Arctic climate.
