利用物理引导的机器学习对海冰上的积雪进行建模
Modeling Snow on Sea Ice using Physics Guided Machine Learning
AI总结：
针对高分辨率积雪过程模型SnowModel计算成本高、普通数据驱动模拟器泛化性与物理一致性不足的问题，本研究提出纳入积雪压实密度变化物理定律的物理引导LSTM模拟器，在北极五区域测试中表现最优，加速超9000倍且保真度高。
摘要：
积雪是海冰系统的关键组成部分，因其低热导率和高反照率影响着海冰的增长与消融。尽管积雪十分重要，但当前的气候模型对积雪的表征较为理想化，通常仅包含单层热力学过程，且忽略了多个决定其属性的过程。虽然存在SnowModel这类先进的积雪过程模型，但由于计算成本过高，它们常被排除在气候建模之外。SnowModel可模拟多层积雪中的积雪深度、密度、吹雪再分布、升华、粒径和热导率，其运行的空间分辨率（1米）和时间分辨率（1小时）都很高。然而，对于北冰洋这类大范围区域，这类高分辨率模拟面临处理速度慢、资源需求大等挑战。数据驱动的模拟器被用于解决这些问题，但它们往往缺乏泛化性，且与物理定律的一致性不足。在本研究中，我们通过开发一款物理引导的模拟器来应对这些挑战，该模拟器纳入了描述压实作用导致积雪密度变化的物理定律。我们在五个北极区域评估了三种机器学习模型：长短期记忆网络（LSTM）、物理引导LSTM以及随机森林。所有模型均取得了较高的准确率，其中物理引导LSTM在准确率和泛化性方面表现最佳。我们的方法提供了一种更快速的SnowModel模拟方式，加速比超过9000倍，同时保持了高保真度。
英文摘要：
Snow is a crucial element of the sea ice system, affecting sea ice growth and decay due to its low thermal conductivity and high albedo. Despite its importance, present-day climate models have an idealized representation of snow, often including only single-layer thermodynamics and omitting several processes that shape its properties. Although advanced snow process models like SnowModel exist, they are often excluded from climate modeling due to their high computational costs. SnowModel simulates snow depth, density, blowing-snow redistribution, sublimation, grain size, and thermal conductivity in a multi-layer snowpack. It operates with high spatial (1 meter) and temporal (1 hour) resolution. However, for large regions like the Arctic Ocean, these high-resolution simulations face challenges such as slow processing and large resource requirements. Data-driven emulators are used to address these issues, but they often lack generalizability and consistency with physical laws. In our study, we address these challenges by developing a physics-guided emulator that incorporates physical laws governing changes in snow density due to compaction. We evaluated three machine learning models: Long Short-Term Memory (LSTM), Physics-Guided LSTM, and Random Forest across five Arctic regions. All models achieved high accuracy, with the Physics-Guided LSTM showing the best performance in accuracy and generalizability. Our approach offers a faster way to emulate SnowModel with a speedup of over 9000 times, maintaining high fidelity.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
