Reduced Order Probabilistic Emulation for Physics‐Based Thermosphere Models
Reduced Order Probabilistic Emulation for Physics‐Based Thermosphere Models
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DOI：
10.1029/2022sw003345
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发表时间：
2022-11
期刊：
影响因子：
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通讯作者：
R. Licata;P. Mehta
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文献类型：
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作者：
R. Licata;P. Mehta
地球空间环境是不稳定和高度驱动的。空间天气对地球磁层产生影响，在热层中引起动态和神秘的反应，特别是对中性质量密度的演变。存在许多使用空间天气驱动器来产生密度响应的模型，但是这些模型对于某些空间天气条件通常计算昂贵或不准确。作为回应，这项工作的目的是采用概率机器学习（ML）方法来创建一个有效的替代热层电离层电动力学环流模型（TIE-GCM），一个基于物理的热层模型。我们的方法利用主成分分析来降低TIE-GCM和递归神经网络的维数，从而比数值模型更快地模拟热层的动态行为。新开发的降阶概率仿真器（ROPE）使用长短期记忆神经网络在简化状态下进行时间序列预测，并提供未来密度的分布。我们表明，在现有的数据中，TIE‐GCM ROPE与以前的线性方法具有类似的误差，同时改善了风暴时间建模。我们还对2003年11月的重大风暴进行了卫星传播研究，结果表明TIE-GCM ROPE可以捕获由TIE-GCM密度产生的位置，偏差<5 km。同时，线性方法提供的点估计值可能导致7-18公里的偏差。
The geospace environment is volatile and highly driven. Space weather has effects on Earth's magnetosphere that cause a dynamic and enigmatic response in the thermosphere, particularly on the evolution of neutral mass density. Many models exist that use space weather drivers to produce a density response, but these models are typically computationally expensive or inaccurate for certain space weather conditions. In response, this work aims to employ a probabilistic machine learning (ML) method to create an efficient surrogate for the Thermosphere Ionosphere Electrodynamics General Circulation Model (TIE‐GCM), a physics‐based thermosphere model. Our method leverages principal component analysis to reduce the dimensionality of TIE‐GCM and recurrent neural networks to model the dynamic behavior of the thermosphere much quicker than the numerical model. The newly developed reduced order probabilistic emulator (ROPE) uses Long‐Short Term Memory neural networks to perform time‐series forecasting in the reduced state and provide distributions for future density. We show that across the available data, TIE‐GCM ROPE has similar error to previous linear approaches while improving storm‐time modeling. We also conduct a satellite propagation study for the significant November 2003 storm which shows that TIE‐GCM ROPE can capture the position resulting from TIE‐GCM density with <5 km bias. Simultaneously, linear approaches provide point estimates that can result in biases of 7–18 km.