采用结合数据同化的水平集方法模拟海洋出口冰川的表面高度和终点位置
Simulating surface height and terminus position for marine outlet glaciers using a level set method with data assimilation
AI总结：
本研究构建了耦合浅架近似（SSA）与水平集方法、结合集合变换卡尔曼滤波的数据同化冰流模型，经理想化冰川和Helheim Glacier实验验证，可更准确追踪冰川动态，提升冰盖短期预测精度。
摘要：
我们构建了一个数据同化框架，用于将冰面观测数据与冰川终点位置观测数据整合到数值冰流模型中。该模型采用广为人知的浅架近似（SSA），并与水平集方法耦合，以捕捉冰体运动和冰川几何形态的变化。水平集方法可显式追踪海洋出口冰川不断演变的冰-气边界与冰-洋边界。我们采用集合变换卡尔曼滤波，通过更新描述冰界面的水平集函数，来同化冰面高程和冰川横向范围的观测数据。在理想化的海洋终止冰川上开展的数值实验表明，我们的数据同化方法可有效追踪冰川的季节性及多年进退周期。该模型还被应用于模拟Helheim Glacier（海尔海姆冰川）——这是格陵兰冰盖的一条主要潮汐终止冰川，近期曾经历快速退缩过程。通过同化遥感获取的表面高程剖面观测数据，我们能够更准确地追踪冰川终点的迁移过程以及冰川表面的变化。这些结果佐证了利用数据同化方法可获取更准确的冰盖短期动态预测结果。
英文摘要：
We implement a data assimilation framework for integrating ice surface and terminus position observations into a numerical ice-flow model. The model uses the well-known shallow shelf approximation (SSA) coupled to a level set method to capture ice motion and changes in the glacier geometry. The level set method explicitly tracks the evolving ice-atmosphere and ice-ocean boundaries for a marine outlet glacier. We use an Ensemble Transform Kalman Filter to assimilate observations of ice surface elevation and lateral ice extent by updating the level set function that describes the ice interface. Numerical experiments on an idealized marine-terminating glacier demonstrate the effectiveness of our data assimilation approach for tracking seasonal and multi-year glacier advance and retreat cycles. The model is also applied to simulate Helheim Glacier, a major tidewater-terminating glacier of the Greenland Ice Sheet that has experienced a recent history of rapid retreat. By assimilating observations from remotely-sensed surface elevation profiles we are able to more accurately track the migrating glacier terminus and glacier surface changes. These results support the use of data assimilation methodologies for obtaining more accurate predictions of short-term ice sheet dynamics.
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